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Summary: In this Article, you will learn about python free online course with certification. Programming with Python: Introduction for Beginners Learn Basic Python Programming Python Libraries Read more to know each in detail. Want to become a data scientist but don’t know Python? Don’t worry; we’ve got your back. With our free online Python course for beginners, you can learn Python online free and kickstart your data science journey. You don’t have to spend a dime to enroll in this program. The only investment you’d have to make is 30 minutes a day for a few weeks, and by the end, you’d know how to use Python for data science.  To enroll in our Python course free, head to our upGrad free course page, select the “Python course, and register. This article will discuss the basics of python and its industrial application, our course contents, and what its advantages are. Let’s get started.  Why Learn Python? Python is among the most popular programming languages on the planet. According to a survey from RedMonk, a prominent analyst firm, Python ranked 2nd in their ranking of programming languages by popularity. Python became the first language other than Java or and JavaScript to enter the top two spots. You can see how relevant Python is in the current market. It’s a general-purpose programming language, which means you can use it for many tasks. Apart from data science, Python has applications in web development, machine learning, etc.  Python is one of the most popular programming languages. Python is used for web development, game development, language development, etc. It helps in conducting complex statistical complications and performing data visualisation. It is compatible with various platforms and has an extensive library. Top Python libraries are Numpy, Pandas, Scipy, Keras, Tensorflow, SciKit learn, Matplotlib, Plotly, Seaborn, Scrapy, and Selenium. These libraries serve different purposes such as some of them are for data processing, data modelling, data visualisation, and data mining. You can also consider doing our Python Bootcamp course from upGrad to upskill your career. In data science, Python has many applications. It has multiple libraries that simplify various data operations. For example, Pandas is a Python library for data analysis and manipulation. It offers numerous functions to manipulate vast quantities of structured data. This way, it makes data analysis much more straightforward. Another primary Python library in data science is matplotlib, which helps you with data visualization. Python is one of the core skills of data science professionals. Learning it will undoubtedly help you in entering this field.  Also, check Full Stack Development Bootcamp Job Guaranteed from upGrad Read: Python Applications in Real World Python Installation and Setup Python installation is a simple procedure. Visit the Python website to get hold of the most recent version. Take care to add python to your system’s PATH during installation. You can look for a free python course with certificate online to gain practical experience. Many platforms provide thorough training to assist you in understanding the essentials. After installing python, create and run your code using an integrated development environment (IDE).  Don’t forget to look at python’s numerous libraries and frameworks, which can make development much simpler. As you advance through your python free course with certificate or python certification free put your newfound knowledge into practice by working on projects and practicing consistently. With perseverance, you’ll soon become an expert Python programmer, prepared to take on a variety of programming tasks. Basic Python Syntax and Data Types Any programming enthusiast must be familiar with the fundamental Python syntax and data structures. You will explore these fundamental ideas in your online python course free with certificate. Python is user-friendly for beginners because of its clear and accessible syntax. Line breaks are frequently used to end statements, and indentation is essential for code blocks. The python free certification course you have selected will walk you through variables, which are data storage units, and their naming conventions. Integers, floats, strings, and booleans are just a few of the different data types that python offers. In the python course online free with certificate, you’ll discover how to format and concatenate strings. Lists, another data type, are mutable and used to hold collections of elements. Dictionary entries are stored as key-value pairs, but tuples, like lists, are immutable. Conditional statements like if, else, and elif aid in regulating the program’s flow. Repetitive jobs are made possible via loops like for and while. The python free online course with certificate will place a strong emphasis on applying these ideas through exercises and projects as you progress through your learning process. By the end of the course, you’ll have a firm understanding of python’s syntax and data types and be prepared to go on to more advanced programming approaches. Control Flow and Loops In order to succeed as a programmer, you must master python’s control flow and loops. A thorough python certification course free will go through these topics in great detail. Your program can make decisions depending on conditions with the help of control flow structures like if, else, and elif. Another important idea is the use of loops, which let your code carry out repeated actions. The python full course free with certificate will guide you through the two main forms of loops: for and while. You can iterate over sequences like lists or strings with the “for” loop. At the same time, a condition is true; a ‘while’ loop, on the other hand, keeps repeating. By completing real-world examples and exercises in your chosen python free certification course, you’ll earn practical experience. Your comprehension of control flow and loops will become more robust as a result. By the end of the course, you’ll be able to design complex programs that efficiently make use of these structures. A solid understanding of control flow and loops is crucial when automating processes or creating intricate algorithms, and the correct course will provide you with these important skills. Why Choose Python free course from upGrad? There are many advantages to joining our Python free courses. Here are some of them: Cutting Edge Content upGrad’s professionally created content ensures that you get the best online learning experience. The curriculum of the course is industry relevant and focuses on practical concepts. To be able to learn the concepts a curriculum which is strong is recommended. This is what upGrad recommends. And after finishing a course, there are practice questions that one can solve in order to gauge retention. This free online python course for beginners is focused on the basics of python programming, It is a good opportunity for someone who is new to the field as it would take the learners on the journey step by step. It is also ideal for those learners who have been in the field for a long, so those candidates can brush up on their skills and revisit the concepts. Free Certificate After you complete our Python online course free, you’ll receive a certificate for completion. The certificate would enhance your CV substantially.  Apart from these benefits, the biggest one is that you can join the course for free. It doesn’t require any monetary investment. The free certificate is the validation of your knowledge. You could add the skill of knowing python to your CV and present the certificate in order to show authenticity. Also, the free certificate is shareable on LinkedIn. You could show your skill to potential recruiters. When you are appearing for any interview, or are looking to get promoted at your job these little things come to help where one can confidently show the document for the skillset that they have mentioned in the CV. It sets one apart from the rest of the candidates.  Let’s now discuss what the course is about and what it will teach you: Must read: Data structures and algorithms free course! Watch our Webinar on How to Build Digital & Data Mindset? Top Data Science Skills to Learn Top Data Science Skills to Learn 1 Data Analysis Course Inferential Statistics Courses 2 Hypothesis Testing Programs Logistic Regression Courses 3 Linear Regression Courses Linear Algebra for Analysis What Will You Learn? Learning Python is crucial for becoming a data scientist. It has many applications in this field, and without it, you can’t perform many vital operations related to data science. Because Python is a programming language, many students and professionals hesitate to study it. They read about Python’s various applications in data science, artificial intelligence, and machine learning and think it’s a highly complicated subject. However, Python is an elementary programming language that you can learn quickly.  Our free Python online course for beginners covers this prominent programming language’s basics and helps you understand its fundamental uses in data science. Below are the list of courses available in Python: Programming with Python: Introduction for Beginners Learn Basic Python Programming Python Libraries These sections allow you to learn Python in a stepwise manner. Let’s discuss each one of these sections in detail: Programming with Python: Introduction for Beginners In this course, you’ll get a stepwise tutorial to begin learning Python. It will familiarize you with Python’s fundamentals, what it is, and how you can learn this programming language. Apart from the basics, this section will explain the various jargons present in data science to you. You’ll get to know the meaning behind many technical terms data scientists usually use, including EDA, NLP, Deep Learning, Predictive Analytics, etc. Understanding what Python is will give you the foundation you need to study its more advanced concepts later on.  When you’d know the meaning behind data science jargon, you would understand how straightforward this subject is. It’s an excellent method to get rid of your hesitation in learning data science. By the end of this course, you would be able to use data science jargon casually like another data professional.  In the introduction, you will get to learn about the primary consoles, what are primary actions, what are statuses, and what important pointers. These topics will be covered in the introduction. The primary console is nothing but a media that takes the input front the user and then interprets it. In this opportunity to learn python online for free, you get to understand python programming from the basics. There is no compromise on imparting education. Explore our Popular Data Science Courses Executive Post Graduate Programme in Data Science from IIITB Professional Certificate Program in Data Science for Business Decision Making Master of Science in Data Science from University of Arizona Advanced Certificate Programme in Data Science from IIITB Professional Certificate Program in Data Science and Business Analytics from University of Maryland Data Science Courses Learn Basic Python Programming This section of our course will teach you Python’s basics from a coding perspective, including strings, lists, and data structures. Data structures are one of the essential concepts you can study in data science. The second topic would be concentrating on the basics of python that will be covering the introduction, history of python, how to do installation documentation, and what are arithmetic operations, and string operations. After the module would be over there would also be a focus on practice questions. These practice questions can be solved to understand how much understanding the learner has gotten. The learners upon answering will get the response to the questions on a real-time basis. Python online course free gives an opportunity to gain the skill of knowing python. They help in organizing data so you can access it and perform operations on it quickly. Understanding data structures is vital to becoming a proficient data scientist. Many recruiters ask the candidates about data structures and their applications in technical interviews. This module focuses on programming with Python in data science. So, it covers the basic concepts of many data structures, such as Tuples, sets, dictionaries etc.  The curriculum would also be focusing on dictionaries, and how to map, filter, and reduce functions. It also will focus on the OOPs, class and objects, methods, inheritance, and overriding. They are very important topics, for example, the OOPs is a computer programming model. It includes methods, classes, objects, etc. OOPs is useful for creating and developing real-life applications. Also visit upGrad’s Degree Counselling page for all undergraduate and postgraduate programs. When you’re familiar with the basics, you can easily use them later in more advanced applications. For example, lists are among the most versatile data structures. They allow the storage of heterogeneous items (items of different data types) such as strings, integers, and even other lists. Another prominent property that makes lists a preferred choice is they are mutable. This allows you to change their elements even after you create the list. This course will cover many other topics similar like this. Our learners also read: Excel online course free! Read our popular Data Science Articles Data Science Career Path: A Comprehensive Career Guide Data Science Career Growth: The Future of Work is here Why is Data Science Important? 8 Ways Data Science Brings Value to the Business Relevance of Data Science for Managers The Ultimate Data Science Cheat Sheet Every Data Scientists Should Have Top 6 Reasons Why You Should Become a Data Scientist A Day in the Life of Data Scientist: What do they do? Myth Busted: Data Science doesn’t need Coding Business Intelligence vs Data Science: What are the differences? Learn Python Libraries: NumPy, Matplotlib and Pandas Python is popular among data scientists for many reasons. One of those reasons is its large number of libraries. There are more than 1,37,000 Python libraries. This number should give you an idea of how valuable these libraries are. These libraries simplify specific processes and make it easier for developers to perform related functions. In this course for beginners, you’ll learn about multiple Python libraries data scientists use, such as NumPy, matplotlib, and Pandas.  A Python library contains reusable code that helps you perform specific tasks with less effort. Unlike C or C++, its libraries don’t focus on a context. They are collections of modules. You can import a module from another program to use its functionality. Every Python library simplifies certain functions. For example, with NumPy, you can perform mathematical operations in Python smoothly. It has many high-level mathematical functions and support for multi-dimensional matrices and arrays. Understanding these libraries will help you in performing operations on data.   Pandas are used for better representation of the data, more work can be done with less coding in Pandas. It is a library of python for data analysis purposes. Pandas can be used for neuroscience, analytics, statistics, data science, advertising, etc.   Matplotlib is a library for Python. It is used for data visualisation and graphical plotting. The APIs (Application Programming Interfaces) of the matplotlib can also be used to plot in GUI applications.  Must Read: Python Project Ideas & Topics for Beginners How to Start To join our free online courses on python, follow the below mentioned steps: Head to our upGrad Free Courses Page Select the Python course Click on Register Complete the registration process That’s it. You can learn python for free with upGrad’s Free Courses and get started with your data science journey. You’d only have to invest 30 minutes a day for a few weeks. This program requires no monetary investment.  Sign up today and get started.  If you have any questions or suggestions regarding this topic, please let us know in the comments below. We’d love to hear from you.  If you are curious to learn about Python, data science, check out IIIT-B & upGrad’s Executive PG Programme in Data Science which is created for working professionals and offers 10+ case studies & projects, practical hands-on workshops, mentorship with industry experts, 1-on-1 with industry mentors, 400+ hours of learning and job assistance with top firms.

An information retrieval (IR) system is a set of algorithms that facilitate the relevance of displayed documents to searched queries. In simple words, it works to sort and rank documents based on the queries of a user. There is uniformity with respect to the query and text in the document to enable document accessibility. Check out our data science free courses to get an edge over the competition. This also allows a matching function to be used effectively to rank a document formally using their Retrieval Status Value (RSV). The document contents are represented by a collection of descriptors, known as terms, that belong to a vocabulary V. An IR system also extracts feedback on the usability of the displayed results by tracking the user’s behaviour. You can also consider doing our Python Bootcamp course from upGrad to upskill your career. When we speak of search engines, we mean the likes of Google, Yahoo, and Bing among the general search engines. Other search engines include DBLP and Google Scholar.  In this article, we will look at the different types of IR models, the components involved, and the techniques used in Information Retrieval to understand the mechanism behind search engines displaying results.  Our learners also read: Free Python Course with Certification Types of Information Retrieval Model There are several information retrieval techniques and types that can help you with the process. An information retrieval comprises of the following four key elements: D − Document Representation. Q − Query Representation. F − A framework to match and establish a relationship between D and Q. R (q, di) − A ranking function that determines the similarity between the query and the document to display relevant information. Also read: Excel online course free! There are three types of Information Retrieval (IR) models: 1. Classical IR Model — It is designed upon basic mathematical concepts and is the most widely-used of IR models. Classic Information Retrieval models can be implemented with ease. Its examples include Vector-space, Boolean and Probabilistic IR models. In this system, the retrieval of information depends on documents containing the defined set of queries. There is no ranking or grading of any kind. The different classical IR models take Document Representation, Query representation, and Retrieval/Matching function into account in their modelling. This is one of the most used Information retrieval models. 2. Non-Classical IR Model — They differ from classic models in that they are built upon propositional logic. Examples of non-classical IR models include Information Logic, Situation Theory, and Interaction models. It is one of the types of information retrieval systems that is diametrically opposite to the conventional IR model.  Featured Program for you: Fullstack Development Bootcamp Course 3. Alternative IR Model — These take principles of classical IR model and enhance upon to create more functional models like the Cluster model, Alternative Set-Theoretic Models Fuzzy Set model, Latent Semantic Indexing (LSI) model, Alternative Algebraic Models Generalized Vector Space Model, etc. Let’s understand the most-adopted similarity-based classical IR models in further detail:  1. Boolean Model — This model required information to be translated into a Boolean expression and Boolean queries. The latter is used to determine the information needed to be able to provide the right match when the Boolean expression is found to be true. It uses Boolean operations AND, OR, NOT to create a combination of multiple terms based on what the user asks. This is one of the information retrieval models that is widely used.  2. Vector Space Model — This model takes documents and queries denoted as vectors and retrieves documents depending on how similar they are. This can result in two types of vectors which are then used to rank search results either  Binary in Boolean VSM. Weighted in Non-binary VSM. Check out our data science courses to upskill yourself. 3. Probability Distribution Model — In this model, the documents are considered as distributions of terms and queries are matched based on the similarity of these representations. This is made possible using entropy or by computing the probable utility of the document. They are if two types: Similarity-based Probability Distribution Model Expected-utility-based Probability Distribution Model 4. Probabilistic Models — The probabilistic model is rather simple and takes the probability ranking to display results. To put it simply, documents are ranked based on the probability of their relevance to a searched query. This is one of the most basic information retrieval techniques used.  Checkout: Data Science vs Data Analytics upGrad’s Exclusive Data Science Webinar for you – Transformation & Opportunities in Analytics & Insights document.createElement('video'); https://cdn.upgrad.com/blog/jai-kapoor.mp4   Components of Information Retrieval Model Here are the prerequisites for an IR model:  An automated or manually-operated indexing system used to index and search techniques and procedures. A collection of documents in any one of the following formats: text, image or multimedia. A set of queries that serve as the input to a system, via a human or machine. An evaluation metric to measure or evaluate a system’s effectiveness (for instance, precision and recall). For instance, to ensure how useful the information displayed to the user is.  If you draw and explain the IR system block diagram, you will come across different components. The various components of an Information Retrieval Model include:  Step 1 Acquisition The IR system sources documents and multimedia information from a variety of web resources. This data is compiled by web crawlers and is sent to database storage systems. Step 2 Representation The free-text terms are indexed, and the vocabulary is sorted, both using automated or manual procedures. For instance, a document abstract will contain a summary, meta description, bibliography, and details of the authors or co-authors. It is one of the components of the information retrieval system that involves summarizing and abstracting. Step 3 File Organization File organization is carried out in one of two methods, sequential or inverted. Sequential file organization involves data contained in the document. The Inverted file comprises a list of records, in a term by term manner. It is one of the components of information retrieval system that also involves the combination of the sequential and inverted methods.  Also visit upGrad’s Degree Counselling page for all undergraduate and postgraduate programs. Top Data Science Skills to Learn Top Data Science Skills to Learn 1 Data Analysis Course Inferential Statistics Courses 2 Hypothesis Testing Programs Logistic Regression Courses 3 Linear Regression Courses Linear Algebra for Analysis Step 4 Query An IR system is initiated on entering a query. User queries can either be formal or informal statements highlighting what information is required. In IR systems, a query is not indicative of a single object in the database system. It could refer to several objects whichever match the query. However, their degrees of relevance may vary.  Explore our Popular Data Science Courses Executive Post Graduate Programme in Data Science from IIITB Professional Certificate Program in Data Science for Business Decision Making Master of Science in Data Science from University of Arizona Advanced Certificate Programme in Data Science from IIITB Professional Certificate Program in Data Science and Business Analytics from University of Maryland Data Science Courses Importance of Information Retrieval System What is information retrieval? Information is a vital resource for corporate operations, and it has to be managed effectively, just like any other vital resource. However, rapidly advancing technology is altering how even very tiny organizations manage crucial business data via information retrieval in AI. A business is held together by an information or records management system, which is most frequently electronic and created to acquire, analyze, retain, and retrieve information. After we understand what is information retrieval, we need to understand its importance.  Here are some reasons why Information Retrieval in AI is important in today’s world –  Productive and Efficient – It is unproductive and possibly expensive for small businesses and local companies to have an owner or employee spend time looking through piles of loose papers or attempting to find records that are missing or have been improperly filed. In addition to lowering the likelihood of information being misfiled, robust information storage and retrieval system that includes a strong indexing system also accelerates the storing and information extraction. This time-saving advantage results in increased office productivity and efficiency while lowering anxiety and stress. Regulatory Compliance – A privately owned corporation is exempt from the majority of federal and state compliance regulations, unlike a public company. Despite this, many people decide to voluntarily comply in order to increase accountability and the company’s reputation in public. Additionally, small-business owners are required to retain and maintain tax information so that it is easily available in the event of an audit. A well-organized system for information retrieval in Artificial Intelligence that adheres to compliance rules and tax record-keeping requirements greatly boosts a business owner’s confidence that the operation is entirely legal. Manual vs. Electronic – The value of electronic information retrieval in Artificial Intelligence is based on the fact that they demand less storage space and cost less in terms of both equipment and manpower. An ordered file system may be maintained using a manual approach, but it requires financial allotments for storage space, filing equipment, and administrative costs. Additionally, an electronic system may make it much simpler to implement and maintain internal controls intended to prevent fraud, as well as make sure the company is adhering to privacy regulations. Better Working Environment – Anyone passing through an office space may find it depressing to see important records and other material piled on top of file cabinets or in boxes close to desks. Not only does this lead to a tense and unsatisfactory work atmosphere, but if consumers witness this, it could give them a bad impression of the company. To understand how crucial it is for even a small firm to have efficient information storage and retrieval system. Difference Between Information Retrieval and Data Retrieval Data Retrieval systems directly retrieve data from database management systems like ODBMS by identifying keywords in the queries provided by users and matching them with the documents in the database.  Whereas the Information Retrieval system in DBMS is a set of algorithms or programs that involve storing, retrieving, evaluation of document and query representations, esp text-based, to display results based on similarity. S.No Information Retrieval Data Retrieval 1 Retrieves information based on the similarity between the query and the document. Retrieves data based on the keywords in the query entered by the user. 2 Small errors are tolerated and will likely go unnoticed. There is no room for errors since it results in complete system failure. 3 It is ambiguous and doesn’t have a defined structure. It has a defined structure with respect to semantics. 4 Does not provide a solution to the user of the database system. Provides solutions to the user of the database system. 5 Information Retrieval system produces approximate results Data Retrieval system produces exact results. 6 Displayed results are sorted by relevance  Displayed results are not sorted by relevance. 7 The IR model is probabilistic by nature. The Data Retrieval model is deterministic by nature. User Interaction with Information Retrieval System Now that you understand “what is information retrieval system,” let us understand the concept of user interaction with it.  The User Task It begins with the rise of a query from the information converted by the user. In an information retrieval system, conveying the semantics of the requested information is possible through a collection of words. Logical View of the Documents In the past, index terms or keywords were used for characterizing documents. Now, new computers can portray documents with a whole set of words. It can minimize the number of representative words. It is possible by deleting stop words like connectives and articles.  Understanding the Difference Between IRS and DBMS Let us discover the difference between IRS and DBMS here. Category DBMS IRS Data Modelling Facility A DBMS comes with an advanced Data Modeling Facility (DMF) that offers Data Definition Language and Data Manipulation Language.  The Data Modeling Facility is missing in an information retrieval system. In an IRS, data modeling is limited to the classification of objects.  Data Integrity Constraints The Data Definition Language of DBMS can easily define the data integrity constraints.  These validation mechanisms are less developed in an information retrieval system.  Semantics  A DBMS offers precise semantics.  The semantics offered by an information retrieval system is usually imprecise.  Data Format A DBMS comes with a structured data format.  An information retrieval system will have an unstructured data format.  Query Language The query language of a DBMS is artificial. The query language of an information retrieval system is extremely close to natural language.  Query Specification In a DBMS, query specification is always complete.  Query specification is incomplete in an IRS. Exploring the Past, Present, and Future of Information Retrieval After becoming aware of the information retrieval system definition, you should explore its past, present, and future: Early Developments: With the increasing need for gaining information, it also became necessary to build data structures for faster access. The index acts as a data structure for supporting fast information retrieval. For a long time, indexes involved manual categorization of hierarchies.  Information Retrieval in Libraries: The adoption of the IR system for information was popularized by libraries. In the first generation, it includes the automation of previous technologies. Therefore, the search was done according to the author’s name and title. In the second generation, searching is possible using the subject heading, keywords, and more. In the third generation, the search is possible using graphical interfaces, hypertext features, electronic forms, and more.  The Web and Digital Libraries: After learning the definition of an information retrieval system, you will realize that it is less expensive than various other sources of information. Therefore, it offers greater access to networks through digital communication. Moreover, it provides free access to publishing on a larger medium.  Conclusion This brings us to the end of the article. We hope you found the information helpful. If you are looking for more knowledge on Data Science concepts, you should check out India’s 1st NASSCOM certified Executive PG Program in Data Science from IITB on upGrad.  Read our popular Data Science Articles Data Science Career Path: A Comprehensive Career Guide Data Science Career Growth: The Future of Work is here Why is Data Science Important? 8 Ways Data Science Brings Value to the Business Relevance of Data Science for Managers The Ultimate Data Science Cheat Sheet Every Data Scientists Should Have Top 6 Reasons Why You Should Become a Data Scientist A Day in the Life of Data Scientist: What do they do? Myth Busted: Data Science doesn’t need Coding Business Intelligence vs Data Science: What are the differences?

For those of you who use any of the major operating systems regularly, you will be interacting with one of the two most critical components of an operating system- a shell. So, what is Shell? It is both an interactive command language as well as a scripting language. Shell is the interface that connects the kernel and the user. A kernel, on the other hand, is an intermediary between the hardware and the operative system. The moment a user starts the terminal or logs in, you activate the shell. A Shell is a command-line interpreter or a complete environment designed to run commands, shell scripts, and programs. Once you feed commands into the shell, it will execute the program based on your input. When a user enters the command, the shell communicates it to the kernel. Upon execution, the output is displayed to the user. More than one shell can run simultaneously in a system, but only one kernel.  Shell scripting is a programming language used for automating and executing tasks on a Unix-like operating system. Shell scripts, as the name indicates, they are written in plain text formats that help in executing a diverse range of tasks such as: Sending emails Generating various reports Managing the files and repositories stored in the system Scheduling different tasks Running several programs automatically Essentially, it translates the input commands and converts them into a Kernel-compatible language. A Shell Script refers to a list of commands in a program run by the Unix Shell. The script includes comments defining the commands in order of their execution sequence.  Shell Scripting is an open-source computer program. It runs on the Unix/Linux shell and writes commands for the shell to execute. It doesn’t matter whether the sequence of commands is lengthy or repetitive; the program helps simplify it into a single script, making it easy to store and execute. A shell script may be one of the following- Bourne shell, C shell (CSH), Korn shell (KSH), and GNU Bourne-Again shell (BASH).  You may wonder, “Why should I concern myself with Shell Scripting?” The simple answer is- to increase efficiency through automation and remove mundane and repetitive tasks from your work schedule. The plain text file, or shell script, contains one or more command lines and can be executed rather than running manually. It reduces the manual effort that goes into programming. Additionally, it can help with system monitoring and taking routine backups. Shell Scripting assists in adding new functionalities to the shell, as well.  Thinking about opting for a career in Shell Scripting? Are you wondering what are some of the possible Unix Shell Scripting interview questions? If the introduction makes you want to know more about Shell Scripting, keep scrolling till the end – we’ve compiled a list of Shell Scripting interview questions and answers to help kickstart your learning process! If you want to learn more about data science, check out our data science courses.  Shell Scripting Interview Questions & Answers What are the advantages of Shell Scripting? The greatest benefits of Shell Scripting are: It allows you to create a custom operating system to best suit your requirements even if you are not an expert. It lets you design software applications based on the platform you’re using.  It is time-savvy as it helps automate system administration tasks. Compared to other programming languages, the shell script is faster and easier to code.  It can provide linkages between existing platforms. 2. What are Shell variables? Shell variables form the core part of a Shell program or script. The variables allow Shell to store and manipulate information within a Shell program. Shell variables are generally stored as string variables. 3. List the types of variables used in Shell Scripting. Usually, a Shell Script has two types of variables: System-defined variables – They are created by the OS(Linux) and are defined in capital letters. You can view them using the Set command.  User-defined variables – These are created and defined by system users. You can view the variable values using the Echo command. Our learners also read: Free online python course for beginners! How can you make a variable unchangeable? You can make a variable unchangeable using read-only. Let’s say you want the value of the variable ‘a’ to remain as five and keep it constant, so you use readonly like so: $ a=5 $ readonly a Name the different types of Shells. There are four core types of Shells, namely: Bourne Shell (sh) C Shell (csh) Korn Shell (ksh) Bourne Again Shell (bash) The two most important types of Shell in Linux are Bourne Shell and C Shell. Explain “Positional Parameters.” Positional parameters are variables defined by a Shell. They are used to pass information to the program by specifying arguments in the command line. How many Shells and Kernels are available in a UNIX environment? Typically, a UNIX environment has only one Kernel. However, there are multiple Shells available. Do you need a separate compiler to execute a Shell program?                           No, you don’t need a separate compiler to execute a Shell program. Since Shell itself is a command-line in the shell program and executes them. How do you modify file permissions in Shell Scripting? You can modify file permissions via umask. With the unmask (user file-creation mode mask) command, you can change the default permission settings of files that are newly created.   What does a “.” (dot) at the beginning of a file name indicate? A file name that starts with a “.” is a hidden file. Usually, when you try to list the files in a Shell, it lists all files except the hidden files. However, the hidden files are present in the directory. If you wish to view hidden files, you must run the Is command with the “–a” flag. upGrad’s Exclusive Data Science Webinar for you – document.createElement('video'); https://cdn.upgrad.com/blog/jai-kapoor.mp4 Explore our Popular Data Science Courses Executive Post Graduate Programme in Data Science from IIITB Professional Certificate Program in Data Science for Business Decision Making Master of Science in Data Science from University of Arizona Advanced Certificate Programme in Data Science from IIITB Professional Certificate Program in Data Science and Business Analytics from University of Maryland Data Science Courses Bash Scripting Interview Questions Below, there are potential unix interview questions that would help one to be well-informed and prepared in advance. Do you understand Linux? What is Linux? Linux is a type of open-source operating system based on the Linux Kernel, a computer program that is the core of computer operating systems, which enables managing a computer’s hardware and software. What is a Shell? A shell is an application that serves as the interface between the user and the Kernel. What do you mean by Shell Scripting? Shell scripting is written in a plain text format, a programming language that enables the user to automate and execute tasks on an operating system. What are the benefits of shell scripting? It is a lightweight and portable tool that can be used on any Unix-like operating system. It helps in automating and executing a wide variety of tasks It is easier to learn It enables a quick start and an interactive debugging Name different types of shells in shell scripting. C Shell, Bourne Again shell, and Korn shell are some different types of shell that can be used. What is a C shell? C shell or CSH shell is a shell scripting program that uses the C program shell syntax. It was created by Bill Joy in the 1970s in California, America. What are the limitations of shell scripting? Shell scripts are suitable for small tasks. It is difficult to manage and execute complex and big tasks that use multiple large data. It is prone to errors, a simple error may also delete the entire data  Some designs which are not apt or weak may prove to be quite expensive The portability of shell scripting is a huge task; it is not easy. What do you understand about a metacharacter? Meta character is a special character used in a program of a shell. It is used to match a similar pattern of characters in a file. For example, to list all the files in the program that begin with the letter ‘p’, use the ls p* command. Read our popular Data Science Articles Data Science Career Path: A Comprehensive Career Guide Data Science Career Growth: The Future of Work is here Why is Data Science Important? 8 Ways Data Science Brings Value to the Business Relevance of Data Science for Managers The Ultimate Data Science Cheat Sheet Every Data Scientists Should Have Top 6 Reasons Why You Should Become a Data Scientist A Day in the Life of Data Scientist: What do they do? Myth Busted: Data Science doesn’t need Coding Business Intelligence vs Data Science: What are the differences? How to create a shortcut in Linux? You can create shortcuts in Linux via two links: Hard link – These links are linked to the inode of the file. They are always present in the same file system as the file. Even if you delete the original file, the hard link will remain unaffected.  Soft link – These links are linked to the file name. They may or may not reside on the same file system as the file. If you delete the original file, the soft link becomes inactive. 12. Name the different stages of a Linux process. Typically, a Linux process traverses through four phases: Waiting – In this stage, the Linux process has to wait for the requisite resource. Running – In this stage, the process gets executed.  Stopped – After successful execution, the Linux process stops. Zombie – In the final step, even though the process is no longer running, it remains active in the process table. Is there an alternative command for “echo?”  Yes, tput is an alternative for echo command. The tput command allows you to control how the output will be displayed on the screen. How many blocks does a file system contain? A file system has four blocks: Superblock – This block offers information on the state of a file system such as block size, block group size, usage information, empty/filled blocks and their respective counts, size & location of inode tables, etc. Bootblock – This block holds the bootstrap loader program that executes when a user boots the host machine.  Datablock – This block includes the file contents of the file system. Inode table – UNIX treats all elements as files, and all information related to files is stored in the inode table.  Must Read: Python Interview Questions Name the three modes of operation of vi editor. The three modes of operation are: Command mode – This mode treats and interprets any key pressed by a user as editor commands.  Insert mode – You can use this mode to insert a new text, edit an existing text, etc. Ex-command mode – A user can enter all commands at a command line. Define “Control Instructions.” How many types of control instructions are available in a Shell? Control instructions are commands that allow you to specify how the different instructions in a script should be executed. Thus, their primary purpose is to determine the flow of control in a Shell program. A Shell has four types of control instructions:  Sequence control instruction enforces the instructions to be executed in the same order in which they are in the program. Selection/decision control instruction that enables the computer to determine which instruction should be executed next. Repetition/loop control instruction that allows the computer to run a group of statements repetitively. Case-control instruction is used when you need to choose from a range of alternatives. Top Data Science Skills to Learn Top Data Science Skills to Learn 1 Data Analysis Course Inferential Statistics Courses 2 Hypothesis Testing Programs Logistic Regression Courses 3 Linear Regression Courses Linear Algebra for Analysis Define “IFS.” IFS refers to Internal Field Separator. It is a system variable whose default value is space, tab, following by a new line. IFS denotes where a field or word ends in a line and where another begins.  Define “Metacharacters.” A Shell consists of metacharacters, which are special characters in a data field or program that offers information about other characters. For example, the “ls s*” command in a Shell lists all the files beginning with the character ‘s’. Differentiate between $* and $@. While $* treats a complete group of positional parameters as a single string, $@ treats each quoted argument as separate arguments. Also read: Python Developer Salary in India 21. Write the syntax of while loop in Shell Scripting.  In Shell Scripting, the while loop is used when you want to repeat its block of commands several times. The syntax for the “while” loop is: while [test condition] do commands… done How are break and continue commands different? The break command is used to escape out of a loop in execution. You can use the break command to exit from any loop command, including until and while loops. On the other hand, the continue command is used to exit the loop’s current iteration without leaving the complete loop. 23. Why do we use the Shebang line in Shell Scripting? The Shebang line is situated at the top of a Shell script/program. It informs the user about the location of the engine that executes the script. Here’s an example of a Shebang line: #!/bin/sh ct $1 Can you execute multiple scripts in a Shell? Yes, it is possible to execute multiple scripts in a Shell. The execution of multiple scripts allows you to call one script from another. To do so, you must mention the script’s name to be called when you wish to invoke it. Which command should you use to know how long a system has been running? You need to use the uptime command to know how long a system has been running. Here’s an example of the uptime command: u/user1/Shell_Scripts_2018> uptime Which command should you use to check the disk usage? You can use the following three commands to check the disk usage: df – It is used to check the free disk space. du – It is used to check the directory wise disk usage. dfspace – It checks the free disk space in megabytes (MB).  27. What do you mean by the Crontab? Crontab is short for cron table, where Cron is a job scheduler that executes tasks. Crontab is a list of commands you want to run on a schedule, along with the command you want to use to manage that list. 28. When should we not use Shell Scripting? We shouldn’t use Shell Scripting in these instances: If the task is highly complicated, such as writing a complete payroll processing solution, we shouldn’t use Shell Scripting. If the job requires a high level of productivity, we shouldn’t use Shell Scripting. If the job requires multiple software solutions, we shouldn’t use Shell Scripting. 29. How do you compare the strings in a Shell script? We use the test command to compare text strings. It compares text strings by comparing every character present in each string. Read: Data Engineer Interview Questions 30. What do you mean by a file system? A file system is a collection of files along with information related to those files. It controls how the data is retrieved and stored. Without file systems, data present in storage would only be a large body of data with no way of telling where one piece of data ends, and another begins. 31. Can you differentiate between single quotes and double quotes? Yes. We use single quotes where we don’t want to perform the variables’ evaluation to values. On the other hand, we use double quotes where we want to perform the variables’ evaluation to values. 32. What do you mean by GUI scripting? We use GUI to control a computer and its applications. Through GUI scripting, we can handle various applications, depending on the operating system. 33. What do you know about the Super Block in Shell scripting? The Super Block is a program that has a record of particular file systems. It contains characteristics including the block size, filled and empty blocks with their respective counts, the location and the size of the inode tables, usage information, the disk block map, etc. 34. What is the importance of the Shebang line? The Shebang line remains at the script’s top. It gives information about the location where the engine is, which executes the script. 35. Provide some of the most popular UNIX commands. Here are some of the most popular UNIX commands: cd – The cd command changes the directory to the user’s home directory when used as $ cd. You can use it to change the directory to test through $ cd test. ls – The ls command lists the files in the current directory when used as $ ls. You can use it to record files in the long format by using it as $ ls -lrt. rm – The rm command will delete the file named fileA when you use it as $ rm fileA. cat – This command would display the contents present in a file when you use it as $ cat filename. mv – The mv command can rename or move files. For example, the $ mv fileA fileB command would move files named fileA and fileB. date – The date command shows the present time and date. grep – The grep command can search for specific information in a file. For example, the $ grep Hello fileA command would search for the lines where the word ‘Hello’ is present. finger – The finger command shows information about the user. ps – The ps command shows the processes presently running on your machine. man – The man command shows the online help or manual about a specified command. For example, the $ ms rm command would display the online manual for the rm command. pwd – The pwd command shows the current working directory. wc – The wc command counts the number of characters, words, and lines present in a file. history – The history command shows the list of all the commands you used recently. gzip – The gzip command compresses the specified file. For example, the $ gzip fileA command would compress fileA and change it into fileA.gz. logname – The logname command would print the user’s log name. head – The head command shows the first lines present in the file. For example, the $ head -15 fileA command would display the first 15 lines of fileA. Additional Notes: This one is among the most crucial Shell scripting interview questions. We recommend preparing a more thorough list of UNIX commands as many versions of this question are asked in interviews. Must Read: Data Science Interview Questions 36. How is C Shell better than Bourne Shell? C Shell is better than Bourne Shell for the following reasons: C Shell lets you alias the commands. This means the user can give any desired name to a command. It is quite beneficial when the user has to use a lengthy command multiple times. Instead of typing the command’s long name numerous times, the user can type the assigned name. It saves a lot of time and energy, making the process much more efficient. C Shell has a command history feature, where C Shell remembers all the previously used commands. You can use this feature to avoid typing the same command multiple times. It enhances efficiency substantially. Due to the above two reasons, using C Shell is much more advantageous than Bourne Shell. 37. What is it essential to write Shell Scripts? Shell scripting has many benefits that make it crucial. It takes input from users, files it, and displays it on the screen. Moreover, it allows you to make your own commands and automate simple daily tasks. You can use Shell scripting to automate system administration tasks also. Shell scripting makes your processes more efficient by saving you a lot of energy and time. Due to this, it is quite essential and widely used. 38. What are some disadvantages of Shell Scripting? Just as there are several advantages of Shell Scripting, there are also some disadvantages of the program. Shell Script interview questions may ask you to count some of them. They are as follows: Shell scripts are slow in execution. Errors in the shell script may prove to be very costly. Compatibility problems may arise across different problems.  Complex scripts may be difficult to execute. 39. What is Shell Scripting? One of the most basic Shell Script interview questions is what is shell scripting? Simply put, Shell Scripting is an open-source computer program run by Unix/Linus shell to create plain text files that store and execute command lines. It removes the need to code and run repetitive commands manually each time. 40. What is Shell? One of the most unavoidable Unix Shell Scripting interview questions will require you to define Shell. A Shell is an intermediary connecting the kernel and the user. It communicates with the kernel when a user enters a command for execution. Ultimately, the output is displayed to the user.  Conclusion Shell Scripting is a time saver for programs. If you want to remove mundane and repetitive tasks from your workload, Shell Scripting can help you tremendously. You don’t even need to be an expert. We hope these 26 Shell Scripting interview questions and answers help you break the ice on Shell Scripting and prepare for your next interview! If you are curious to learn about data science, check out IIIT-B & upGrad’s Executive PG Programme in Data Science which is created for working professionals and offers 10+ case studies & projects, practical hands-on workshops, mentorship with industry experts, 1-on-1 with industry mentors, 400+ hours of learning and job assistance with top firms.

Summary: In this Article, you will learn about 4 Types of Data Qualitative Data Type Nominal Ordinal Quantitative Data Type Discrete Continuous Read more to know each in detail. Introduction Data science is all about experimenting with raw or structured data. Data is the fuel that can drive a business to the right path or at least provide actionable insights that can help strategize current campaigns, easily organize the launch of new products, or try out different experiments. All these things have one common driving component and this is Data. We are entering into the digital era where we produce a lot of Data. For instance, a company like Flipkart produces more than 2TB of data on daily basis.  In simple terms, data is a systematic record of digital information retrieved from digital interactions as facts and figures. Types of statistical data work as an insight for future predictions and improving pre-existing services. The continuous data flow has helped millions of organizations to attain growth with fact-backed decisions. Data is a vast record of information segmented into various categories to acquire different types, quality, and characteristics of data, and these categories are called data types. When this Data has so much importance in our life then it becomes important to properly store and process this without any error. When dealing with datasets, the category of data plays an important role to determine which preprocessing strategy would work for a particular set to get the right results or which type of statistical analysis should be applied for the best results. Let’s dive into some of the commonly used categories of data. Qualitative Data Type Qualitative or Categorical Data describes the object under consideration using a finite set of discrete classes. It means that this type of data can’t be counted or measured easily using numbers and therefore divided into categories. The gender of a person (male, female, or others) is a good example of this data type. These are usually extracted from audio, images, or text medium. Another example can be of a smartphone brand that provides information about the current rating, the color of the phone, category of the phone, and so on. All this information can be categorized as Qualitative data. There are two subcategories under this: Must read: Data structures and algorithms free course! Nominal These are the set of values that don’t possess a natural ordering. Let’s understand this with some examples. The color of a smartphone can be considered as a nominal data type as we can’t compare one color with others. It is not possible to state that ‘Red’ is greater than ‘Blue’. The gender of a person is another one where we can’t differentiate between male, female, or others. Mobile phone categories whether it is midrange, budget segment, or premium smartphone is also nominal data type. Nominal data types in statistics are not quantifiable and cannot be measured through numerical units. Nominal types of statistical data are valuable while conducting qualitative research as it extends freedom of opinion to subjects. Read: Career in Data Science Ordinal These types of values have a natural ordering while maintaining their class of values. If we consider the size of a clothing brand then we can easily sort them according to their name tag in the order of small < medium < large. The grading system while marking candidates in a test can also be considered as an ordinal data type where A+ is definitely better than B grade.  These categories help us deciding which encoding strategy can be applied to which type of data. Data encoding for Qualitative data is important because machine learning models can’t handle these values directly and needed to be converted to numerical types as the models are mathematical in nature. For nominal data type where there is no comparison among the categories, one-hot encoding can be applied which is similar to binary coding considering there are in less number and for the ordinal data type, label encoding can be applied which is a form of integer encoding. Difference Between Nominal and Ordinal Data Aspect Nominal Data Ordinal Data Definition Categories data into distinct classes or categories without any inherent order or ranking. Categories data into ordered or ranked categories with meaningful differences between them. Examples Colors, gender, types of animals Education levels, customer satisfaction ratings Mathematical Operations No meaningful mathematical operations can be performed (e.g., averaging categories). Limited mathematical operations can be performed, such as determining the mode or median. Order/ Ranking No natural or meaningful order exists. Categories have a specific order or ranking, but the magnitude of differences between ranks may not be uniform. Central Tendency Mode (most frequent category) Mode, median (middle category), but mean is not typically used due to lack of uniform interval between ranks. Example Use Case Classifying objects, grouping data Rating scales, survey responses, educational levels Quantitative Data Type This data type tries to quantify things and it does by considering numerical values that make it countable in nature. The price of a smartphone, discount offered, number of ratings on a product, the frequency of processor of a smartphone, or ram of that particular phone, all these things fall under the category of Quantitative data types. Also read: Learn python online free! The key thing is that there can be an infinite number of values a feature can take. For instance, the price of a smartphone can vary from x amount to any value and it can be further broken down based on fractional values. The two subcategories which describe them clearly are: Discrete The numerical values which fall under are integers or whole numbers are placed under this category. The number of speakers in the phone, cameras, cores in the processor, the number of sims supported all these are some of the examples of the discrete data type. Discrete data types in statistics cannot be measured – it can only be counted as the objects included in discrete data have a fixed value. The value can be represented in decimal, but it has to be whole. Discrete data is often identified through charts, including bar charts, pie charts, and tally charts. Our learners also read: Excel online course free! upGrad’s Exclusive Data Science Webinar for you – Transformation & Opportunities in Analytics & Insights document.createElement('video'); https://cdn.upgrad.com/blog/jai-kapoor.mp4 Continuous  The fractional numbers are considered as continuous values. These can take the form of the operating frequency of the processors, the android version of the phone, wifi frequency, temperature of the cores, and so on.  Unlike discrete data types of data in research, with a whole and fixed value, continuous data can break down into smaller pieces and can take any value. For example, volatile values such as temperature and the weight of a human can be included in the continuous value. Continuous types of statistical data are represented using a graph that easily reflects value fluctuation by the highs and lows of the line through a certain period of time.  Difference between Discrete Data and Continous Data Aspect Discrete Data Continuous Data Definition Consists of distinct, separate values. It can take any value within a given range. Examples Number of students in a class, coin toss outcomes (1, 2, 3), customer count. Height, weight, temperature, time. Nature Usually involves whole numbers or counts. Involves any value along a continuous spectrum. Gaps in values Gaps between values are common and meaningful. Values can be infinitely divided without gaps. Measurement Often measured using integers. Measured with decimal numbers or fractions. Graphical representation Typically represented with bar charts or histograms. Represented with line graphs or smooth curves. Mathematical Operations Typically involves counting or summation. Involves arithmetic operations, including fractions and decimals. Probability Distribution Typically represented using probability mass functions Typically represented using probability density functions. Example Use Case Counting occurrences, tracking integers. Measuring quantities and analyzing measurements. Explore our Popular Data Science Courses Executive Post Graduate Programme in Data Science from IIITB Professional Certificate Program in Data Science for Business Decision Making Master of Science in Data Science from University of Arizona Advanced Certificate Programme in Data Science from IIITB Professional Certificate Program in Data Science and Business Analytics from University of Maryland Data Science Courses Importance of Qualitative and Quantitative Data Qualitative types of data in research work around the characteristics of the retrieved information and helps understand customer behavior. This type of data in statistics helps run market analysis through genuine figures and create value out of service by implementing useful information. Qualitative types of data in statistics can drastically affect customer satisfaction if applied smartly. On the other hand, the Quantitative data types of statistical data work with numerical values that can be measured, answering questions such as ‘how much’, ‘how many’, or ‘how many times’. Quantitative data types in statistics contain a precise numerical value. Therefore, they can help organizations use these figures to gauge improved and faulty figures and predict future trends. Must Read: Data Scientist Salary in India Can Ordinal and Discrete type overlap? If you pay attention to this, you can give numbering to the ordinal classes, and then it should be called discrete type or ordinal? The truth is that it is still ordinal. The reason for this is that even if the numbering is done, it doesn’t convey the actual distances between the classes. For instance, consider the grading system of a test. The respective grades can be A, B, C, D, E, and if we number them from starting then it would be 1,2,3,4,5. Now according to the numerical differences, the distance between E grade and D grade is the same as the distance between the D and C grade which is not very accurate as we all know that C grade is still acceptable as compared to E grade but the mid difference declares them as equal. You can also apply the same technique to a survey form where user experience is recorded on a scale of very poor to very good. The differences between various classes are not clear therefore can’t be quantified directly.  Top Data Science Skills to Learn Top Data Science Skills to Learn 1 Data Analysis Course Inferential Statistics Courses 2 Hypothesis Testing Programs Logistic Regression Courses 3 Linear Regression Courses Linear Algebra for Analysis Different Tests We have discussed all the major classifications of Data. This is important because now we can prioritize the tests to be performed on different categories. Now it makes sense to plot a histogram or frequency plot for quantitative data and a pie chart and bar plot for qualitative data. Regression analysis, where the relationship between one dependent and two or more independent variables is analyzed is possible only for quantitative data. ANOVA test (Analysis of variance) test is applicable only on qualitative variables though you can apply two-way ANOVA test which uses one measurement variable and two nominal variables. In this way, you can apply the Chi-square test on qualitative data to discover relationships between categorical variables. Why Are Data Types Important in Statistics?  Data types play a crucial role in statistics for several reasons: 1. Data Understanding Data types provide information about the nature of the variables and the kind of values they can take, aiding in understanding the dataset. 2. Analysis Selection Different data types require different analysis techniques. Choosing the appropriate analysis method depends on the data types involved. 3. Statistical Tests The choice of statistical tests depends on the data types of variables. Parametric tests are used for continuous data, while non-parametric tests are suitable for categorical or ordinal data. 4. Data Treatment Understanding data types helps decide how to effectively handle missing values, outliers, and other data anomalies. 5. Visualization Data types determine the visualizations most appropriate for conveying insights, such as bar charts for categorical data and histograms for continuous data. 6. Data Transformation Data types influence the need for data transformation, such as normalizing or standardizing continuous variables for certain analyses. 7. Model Building In machine learning and regression analysis, the type of dependent and independent variables affects the choice of algorithms and the model’s assumptions. 8. Interpretation Data types impact how results are interpreted. The meaning of statistical measures like mean, median, and mode varies based on whether the data is continuous, discrete, or categorical. 9. Accuracy and Validity Misidentifying data types can lead to incorrect analyses, invalid conclusions, and inaccurate predictions. 10. Data Integration Understanding data types ensures consistency and compatibility between datasets when combining data from different sources. 11. Data Privacy and Security Sensitivity to data types helps preserve data privacy by ensuring that the appropriate anonymization techniques are applied based on the data’s nature. 12. Reporting and Communication Accurate identification of data types ensures that findings are communicated clearly and accurately to stakeholders and decision-makers. 13. Efficient Storage Understanding data types helps in efficient data storage and retrieval, optimizing database performance. 14. Resource Allocation Data types affect memory and processing requirements. The efficient allocation of resources depends on accurate knowledge of data types. Learn Data Science Courses online at upGrad Read our popular Data Science Articles Data Science Career Path: A Comprehensive Career Guide Data Science Career Growth: The Future of Work is here Why is Data Science Important? 8 Ways Data Science Brings Value to the Business Relevance of Data Science for Managers The Ultimate Data Science Cheat Sheet Every Data Scientists Should Have Top 6 Reasons Why You Should Become a Data Scientist A Day in the Life of Data Scientist: What do they do? Myth Busted: Data Science doesn’t need Coding Business Intelligence vs Data Science: What are the differences? Conclusion In this article, we discussed how the data we produce can turn the tables upside down, how the various categories of data are arranged according to their need. We also looked at how ordinal data types can overlap with the discrete data types. What type of plot is suitable for which category of data was also discussed along with various types of test that can be applied on specific data type and other tests that uses all types of data.  If you are curious about learning data science to be in the front of fast-paced technological advancements, check out upGrad & IIIT-B’s Advanced Certification in Data Science The program comes with an in-demand course structure created exclusively under industry leaders to deliver sought-after skills.  With the Big Data industry experiencing a surge in the digital market, job roles like data scientist and analyst are two of the most coveted roles. The course prepares learners with the right set of skills to strengthen their skillset and bag exceptional opportunities. Explore upGrad courses to learn more!

Summary: In this article, you will learn in detail about Course Eligibility Demand Who is Eligible? Curriculum Subjects & Skills The Science Behind Data Data Science Syllabus Read more to know each in detail. Data Science is a field in the interdisciplinary domain. It is about the scientific approach in processing huge data requirements. The science of data pertains to various scientific techniques and theories derived from several fields in the context of mathematics, statistics, computer science, information science, and domain knowledge.   The following article will mainly talk about the various requirements for data scientist eligibility in India. It will also shed light on the different data scientist qualifications in India and the many topics that are included in data science courses offered by multiple universities.  What is Data Science Course? A Data Science program is an educational curriculum or set of courses designed to teach individuals the skills and knowledge required to analyze, interpret, and extract valuable insights from data. Data Science combines various fields such as statistics, computer science, mathematics, and domain expertise to handle large and complex datasets. Data Science Course Eligibility Who can do data science course? Lately, Data Science has been in great demand in the industry. To cope with the demand, students have started looking forward to studying the DS subjects. Industries started upgrading the skills of their staff to remain competitive. Several institutes and course providers picked the industry needs and designed suitable courses in Data Science. On that note, let’s take a look at some of the data science requirements in India. Learn Statistics for data science course free Data Science Demand According to the report by the US Bureau of Labor Statistics, the rise of Data Science needs will create approximately 11.5 million job openings by 2026. The World Economic Forum predicts that by 2022, the profession of data scientist would be the most emerging in the world. As the growth suggests, after the United States, India is looked at as the second most prominent hub for Data Science developments. As per the current industry job trends, Data Science is a highly employable and appealing profession. The demand, therefore, introduced a swift surge in the Data Science course providers. Who is Eligible? Anyone, whether a newcomer or a professional, willing to learn Data Science can opt for it. Engineers, Marketing Professionals, Software, and IT professionals can take up part-time or external programs in Data Science. For regular courses in Data Science, basic high school level subjects are the minimum requirement. Data Science, loosely, is an amalgamation of concepts from Mathematics, Computer Science, and Statistics. Students should have a degree in one of the fields in science, technology, engineering, and mathematics (STEM background). So a data scientist eligibility in India is anyone who is from a STEM background, as it is one of the minimum requirements for data scientist that any newcomer should possess.  Having studied computer programming in high school is an additional benefit. Students study the fundamentals, as well as advanced concepts in Data Science. Based on the subject knowledge of statistics, machine learning, and programming, students become experts in implementing Data Science methodologies in the practical world.  Also read: Free data structures and algorithm course! Students from other streams, like business studies, are also eligible for relevant courses in Data Science. Similarly, business professionals having a basic degree in Business Administration, such as BBA or MBA, are also eligible for higher studies in the Data Science domain. These professionals work in the capacity of Executives in the IT industry. They are mostly responsible for making CRM reports, MIS (Management Information System), and business-related DQA (Data Quality Assessment). Read: Career in data science and its future growth The basic data scientist eligibility in India for a course in data science after 12th standard includes basic knowledge of maths, computer science, and statistics. Aspiring students also need to have at least a total aggregate of 50% on their high school score sheet.  The data science course eligibility for students looking for opportunities for Diploma in Data Science includes a BE/BTech/MCA/MSc degree, with their core subjects being statistics, computer, or programming. Furthermore, the said students also need to have a total aggregate of 50% and above to qualify for a Diploma in Data Science.  The Data Science course eligibility criteria for someone interested in MSSc/MTech/MCA Data Science is a BCA/BE/BTech degree or any other equivalent degree from a recognized university. Furthermore, the aspiring candidates should also have a minimum aggregate of 50% marks on their university score sheet, alongside a detailed knowledge of mathematics and statistics.  Data science requirements for someone opting for a PhD in Data Science include a minimum of 55% marks in the postgraduation programme. Students having a GPA higher than the average are likely to be given more preference than others.  Data Science Curriculum The majority of courses designed are PG and certificate level courses for graduates. Recently, several technical institutes and engineering colleges in India launched degree level programs in Data Science and Analytics.  upGrad’s Exclusive Data Science Webinar for you – The Future of Consumer Data in an Open Data Economy document.createElement('video'); https://cdn.upgrad.com/blog/sashi-edupuganti.mp4   DS Subjects and Skills In general, for admission to a DS course, the following are the data scientist qualifications in India– Degree – A graduation from the STEM stream. No coding experience is required. Mathematics – This subject is the heart of ML/DS and Data Analysis, where the model is created by processing mathematical algorithms’ data. In general, mathematics broadly covers topics in arithmetic, algebra, calculus, differentiation probability, statistics, geometry, and allied. Statistics – Statistical concepts will help you understand data, analyse, and derive a conclusion from the data. Data Visualisation – Access, retrieve data, and perform visualisation and presentation with R and Tableau. Exploratory Data Analysis – Explore Excel and databases to derive useful insights from the pool of data and learn from the data attributes and properties. Hypothesis Testing – Formulate and test hypotheses that are applied in case studies to solve real business problems. Programming Languages – Though coding is not the criteria for admission to DS courses, knowledge about programming languages, such as Java, Python, Scala, or equivalent, is highly recommended. Database – A good understanding of the databases is highly desirable. If one is able to master all of the skills mentioned above then they have nothing else to worry about regarding data scientist eligibility in India. Must read: Learn excel online free! Explore our Popular Data Science Courses Executive Post Graduate Programme in Data Science from IIITB Professional Certificate Program in Data Science for Business Decision Making Master of Science in Data Science from University of Arizona Advanced Certificate Programme in Data Science from IIITB Professional Certificate Program in Data Science and Business Analytics from University of Maryland Data Science Courses The Science Behind Data The Data Science field highlights the processes involving methods, algorithms, and systems to derive knowledge and intelligence from a pool of structured and unstructured data. Data Science caters to several data-driven initiatives, such as Data Mining, Big Data, Machine Learning, and Artificial Intelligence. In greater detail, Data Science could be looked at as a concept that unifies statistics, analysis of data, and methodologies to analyse and make sense of the real phenomena with the help of data. Data Science Syllabus Educationalists designed a Data Science course syllabus to help make the students industry-ready to implement DS knowledge in the industry. The curriculum is also tuned to match the needs of the industry. The syllabus focuses on specific areas, such as open-source tools, libraries, databases, SQL, Python, R, data visualisation, data analysis, and machine learning. The centric concept in the course follows the methodologies in data handling. It uses models based on systematically designed algorithms.  Major Tools and Programming Languages that are Data Science Requirements Python or R: Python is an object-oriented, interpreted language. It has built-in data structures, dynamic typing (runtime type checks), and binding. Python syntax is easy to read and learn. Python and its libraries are free. Python improves programmers’ coding efficiency. It includes libraries like NumPy, Pandas, Scikit, Keras, Tensorflow, Matplotlib, etc. Jupyter Notebook, a live-code-sharing web app, makes data science explanations smooth. R is a statistical/computing/graphics programming language. R includes linear and nonlinear modelling, statistical testing, clustering, etc. R’s strength is how easily it can plot mathematical notations and formulae. Knowledge in Python or R is definitely a data scientist requirements that companies look for.  Mathematics and Statistics: Machine Learning algorithms rely heavily on mathematics and statistics. Knowing the methods that underlie different Machine Learning algorithms can help you choose the right one for the job. Algorithms: Machine learning and Deep Learning algorithms are the base of an Artificial Intelligence model. It is one of the key data scientist requirements. SVM, Random Forest, and K-means clustering are to name a few. Data Visualizations: Users of data science and data analytics software may benefit from data visualization by gaining insight into the large volumes of data they have access to. Data visualization is one of the many data science requirements. The ability to discover new patterns and flaws in the data is beneficial to them. By understanding these patterns, users are better able to focus their attention on areas that signal potential problems or advancements. Tableau is one of the many data visualization software out there. Tableau is quite popular since it enables users to do data analysis in a relatively short amount of time. Additionally, dashboards and spreadsheets may be created from the visualizations. Tableau enables users to construct dashboards that provide insights that can be put into action and move a company’s operations forward. When properly configured with the appropriate underlying operating system and hardware, Tableau products will always run in virtualized environments. Data scientists investigate the data using Tableau, which provides them with endless visual analytics. Spark, SQL, NoSQL: Spark is a platform for processing massive datasets using analytics, machine learning, graph processing, and more. Spark’s compatibility with popular database technologies like NoSQL and SQL makes it possible for data scientists to derive useful insights from massive datasets. Hadoop: Apache Hadoop is a system that is developed using open source software that is used to effectively store and analyze huge datasets with sizes ranging from gigabytes to petabytes. Hadoop enables the clustering of several computers so that big datasets may be analyzed in parallel and much more rapidly than was previously possible. This is done in place of having a single gigantic system to store and analyze the data. Most Jobs Expect a DS Professional to Have the Following Skills, these are the basic requirements for data scientist – A good grade and understanding of Statistics, Mathematics, Computer fundamentals, and Machine Learning. Expertise in one or more of the programming languages, preferably R or Python. Thorough understanding of databases. Exposure to Big Data tools, like Hadoop, Spark, and MapReduce. Experience in data wrangling, data cleaning, mining, visualisation, and reporting tools. Top Data Science Skills to Learn Top Data Science Skills to Learn 1 Data Analysis Course Inferential Statistics Courses 2 Hypothesis Testing Programs Logistic Regression Courses 3 Linear Regression Courses Linear Algebra for Analysis Till now we have learned some of the basic data scientist qualifications in India. But what are the topics that are covered in Data Science and Data Analytics courses? In the following list lies the answer to this question. Almost every university has four major components in their data science courses offered to the students. These include  Big Data Machine Learning Artificial Intelligence Modelling Big Data This particular topic mainly focuses on engaging students in Big Data methods and strategies. Big Data in its first stage is made up of several unstructured data that are gathered in the form of videos, pictures, messages, posts, etc. This unstructured data is then transformed into organised data.  Machine Learning Students get to learn various mathematical models and algorithms, which are included under this segment of the Data science curriculum. The main objective is to help students adapt better to everyday developments, and face the challenges of an organization.  Artificial Intelligence Following the accumulation and gathering of data, enterprises need to be able to interpret and display the said data accordingly. That is where machine learning comes into play. It not only helps you to have a better understanding of the market side of the process, but you are also able to see the patterns and then make business progress accordingly.  What Are the Important Areas of Data Science? Data science encompasses a wide range of areas and skills, all aimed at extracting insights and knowledge from data. Some important areas within data science course include: 1. Statistics and Probability A solid foundation in statistics and probability theory is crucial for understanding data distributions, making inferences, and building models. 2. Data Cleaning and Preprocessing Before analysis, data often requires cleaning and preprocessing to handle missing values, outliers, and inconsistencies, ensuring accurate and reliable results. 3. Data Exploration and Visualization Exploring data through visualizations helps in identifying patterns, trends, and relationships, aiding in the formulation of hypotheses and understanding the data’s underlying structure. 4. Machine Learning This is a core area where algorithms are developed to enable computers to learn patterns from data and make predictions or decisions. Subfields include supervised learning, unsupervised learning, and reinforcement learning. 5. Feature Engineering Selecting, transforming, and creating meaningful features from raw data to improve the performance of machine learning models is a critical step. 6. Model Selection and Evaluation Choosing the right model for a given problem and evaluating its performance using metrics like accuracy, precision, recall, F1-score, etc., are essential tasks. 7. Deep Learning A subset of machine learning that deals with neural networks and complex hierarchical representations, often used for tasks like image recognition, natural language processing, and more. 8. Natural Language Processing (NLP) It helps machines to understand, explain, and create human language, with applications ranging from sentiment analysis to language translation. 9. Computer Vision Involves the development of algorithms that allow computers to interpret and understand visual information from the world, often used in tasks like image and video analysis. 10. Time Series Analysis This area deals with data collected over time, often used in forecasting and trend analysis. It involves techniques like autoregressive integrated moving average (ARIMA) and more. 11. Big Data Technologies With the explosion of data, tools like Hadoop, Spark, and distributed computing frameworks are important for handling and processing large datasets efficiently. 12. Data Ethics and Privacy As data science involves handling sensitive information, understanding ethical considerations and ensuring data privacy are essential aspects. 13. Domain Expertise Having domain-specific knowledge is crucial for framing problems, interpreting results, and making informed decisions based on data insights. 14. A/B Testing It is used to collate different versions of a website page or app against one another to find out which will deliver best results in terms of user engagement or other metrics. 15. Data Storytelling and Communication The ability to convey complex insights in a clear and understandable manner to non-technical stakeholders is important for driving decision-making based on data. 16. Data Governance and Management Involves establishing processes, policies, and standards for managing data throughout its lifecycle to ensure its quality, security, and compliance. 17. Feature Selection and Dimensionality Reduction Techniques to identify the most relevant features and reduce the number of features in high-dimensional data, leading to more efficient models. These areas often overlap and complement each other. Depending on the specific problem you’re working on and your role in the data science process, you may need to delve more deeply into certain areas than others. Read our popular Data Science Articles Data Science Career Path: A Comprehensive Career Guide Data Science Career Growth: The Future of Work is here Why is Data Science Important? 8 Ways Data Science Brings Value to the Business Relevance of Data Science for Managers The Ultimate Data Science Cheat Sheet Every Data Scientists Should Have Top 6 Reasons Why You Should Become a Data Scientist A Day in the Life of Data Scientist: What do they do? Myth Busted: Data Science doesn’t need Coding Business Intelligence vs Data Science: What are the differences? Conclusion This is the time for aspirant students to decide on taking the right course in the Data Science stream. Assess your capabilities and decide on taking the courses that suit you the most to achieve data scientist qualifications in India. upGrad offers various courses in Data Science that make eligible aspirants industry-ready professionals in Data Science.The courses range from Executive PG Programme in Data Science, PG Certification, to Masters.  

Summary: In this article, you will learn about Data Scientist salaries in India based on Location, Skills, Experience, country and more. Read the complete article to know in detail. Wondering what is the range of Data Scientist salary in India? Career opportunities in data have exponentially grown in the recent few years. Companies are eager to capture data and derive insights from it because of the technological advancements we are seeing. Accessibility of the data today can help to reap multiple benefits organizations from it. Because of this reason, companies are not shying away from offering increased data scientist salaries in India. Companies are throwing huge salaries at those having the skills to take on the positions of Data Analysts, Scientists, Engineers, etc.  You can check out upGrad’s Data Science Free Courses page, and choose the course you want to learn to bag a high data scientist salary in India. India is the second-highest country to recruit employees in the field of data science course or data analytics, etc. with 50,000 positions available – second only to the United States. Following the growth of data science as a field, the offered data science salary and The demand for data experts is equally competitive, whether you look at the big companies, the e-commerce industry or even start-ups. Thus, if you have the required skill set and are ready to keep yourself updated, your career as a Data Scientist is expected to keep growing onwards and upwards. This line stands true especially when we consider that a data scientist’s salary in India is directly or indirectly dependent on how upskilled and updated they are. You can also consider doing our Python Bootcamp course from upGrad to upskill your career. We are sure, this must-have sparked a will to become a Data Scientist within you! This can be your first step towards earning a good data science salary, so Let’s take a look at who exactly is a Data Scientist and what is a typical Data Scientist’s salary in India. Also, read our article on statistics for data science free courses Who is a Data Scientist & What Do They Do? Data Scientists are inherently analytical data experts equipped with the requisite skills to solve complex problems complemented with the unquenching thirst for exploring a wide array of issues that need to be addressed. They are highly skilled individuals combining the best of both worlds – IT and business. Hence, data scientists are part computer scientists, part mathematicians, and part trend-analysers. Because of the demand, the data scientist’s salary in India is one of the highest. Data Science has varied applications, ranging in different fields, such as- Manufacturing E-Commerce BFSI Healthcare Transportation This industry has real-world applications where data science makes the operations of the company much more data-driven, accurate and speedy. The data scientist through their knowledge allows the company to respond to the market trends quickly. This response to the trends allows the company to acquire new customers by understanding their needs and not only that, but it also allows the company to retain the existing customers. Data understands the customer’s requirements and allows the company to make decisions that favor their customers. That eventually leads to better customer satisfaction and bigger revenue. A data scientist creates a bigger impact on the company’s value which leads to them being highly compensated for their efforts and skills, and that is one of the reasons why a data scientist salary is high. A data scientist might not be a conventional role, but it sure comes with ample potential to ensure it stays relevant in the near future and a considerably higher average salary of data scientist in India as its proof. Perhaps, even beyond that! After all, data in real-time is the most realistic measure of anything you want to analyze! Read: Career in data science and its Scope. Requisite skills for a data scientist Knowledge of algorithms, statistics, mathematics and machine learning. Programming languages such as R, Python, SQL, SAS, and Hive. Business understanding and the aptitude to frame the right questions to ask, and find answers in the available data. Communication skills in order communicate the results effectively to the rest of the team. It is recommended that the candidates aspiring for the data science role acquire the recommended skills, for them to be able to solve the problems and to ace in their career. The freshers who are new to the industry are advised to begin by acquiring the basic knowledge and understanding the usage of basic programming languages and tools. That would help them to get high data scientist salary for freshers. Appealing Trend of Data Science in India  Data science is swiftly progressing in India as many enterprises acknowledge the significance of making data-based decisions. We aim to delve into the job scenario of data science in India, encompassing various levels of experience, role categories, data scientist salary, and trends across different cities. Experience Levels Data science positions in India span a spectrum of experience tiers, encompassing roles ranging from novices to seasoned leaders. At the entry-level, prerequisites typically involve a bachelor’s degree in pertinent domains such as computer science or statistics, coupled with rudimentary familiarity with programming languages like Python or R. Some instances of entry-level designations in the realm of data science comprise data analyst, data engineer, and data scientist with a decent data scientist salary. Companies often stipulate a minimum of 3-5 years of industry exposure for intermediate-level positions, complemented by a master’s or doctoral qualification in pertinent disciplines. Roles at this tier may entail data science manager, data architect, or machine learning engineer. Ascending to senior leadership roles within data science necessitates considerable hands-on experience, augmented by advanced degrees and an established history of accomplishments in the field. Illustrative positions in this category encompass chief data officer, director of data science, and head of data analytics. A heightened demand exists for mid and senior-level data science professionals compared to their entry-level counterparts in the Indian context. Nevertheless, the call for entry-level professionals is also steadily gaining momentum. The request for data science experts possessing 0-3 years of experience has surged by 45% over the past year offering a good fresher data scientist salary in India. This surge can be ascribed to the mounting inclination of companies towards data-driven decision-making, propelling the search for professionals capable of harnessing data to foster business expansion. City-wise Trends In urban areas, Bangalore, Delhi, Mumbai, and Hyderabad emerge as the leading Indian cities offering the most excellent prospects for data science employment. These urban hubs boast a significant clustering of IT firms, startups, and other sectors heavily reliant on data scrutiny. Bangalore takes the lead regarding data science job vacancies with a decent data scientist salary, followed by the Delhi National Capital Region (NCR) and Mumbai. Bangalore: Regarded as the nucleus of India’s technological sector, Bangalore harbours a flourishing community dedicated to data science. Numerous eminent tech enterprises, including Google, Amazon, and Microsoft, are situated within its bounds, boasting substantial cohorts of data scientists under their employ with great data scientist salary in Bangalore. Delhi: Another prominent Indian city rapidly growing in the data science industry is Delhi. The city’s progress in this area is fueled by several significant banking and financial organisations and e-commerce behemoths like Flipkart and Snapdeal, all of which have headquarters in Delhi. Mumbai: Banks, insurance companies, and investment companies are just a few major participants in the financial services industry that have their headquarters in Mumbai. These organisations primarily rely on data analysis to guide their business decisions, offering good data scientist salary in Mumbai, making it an attractive location for anyone seeking jobs in data science. Strategies For Securing a Career in Data Science in India Acquire Data Science Skills Data science enthusiasts should concentrate on honing their technical skills and expanding their knowledge of the subject. This may be accomplished by registering for classes, reading pertinent material, and passing online tutorials. Attend Conferences & Workshops Networking opportunities and insights into cutting-edge technology may be found by participating in data science conferences and workshops. Additionally, it allows participants to network with potential employers or recruiters and gain a deeper comprehension of the data science industry. Networking Contacting people in the data science industry can bring you new ideas on prospective job opportunities and create connections that could one day be useful. Pursue Certifications Another way to demonstrate expertise and strengthen one’s CV when looking for jobs in data science is to obtain professional certifications relevant to the industry. The designations Cisco Certified Data Scientist (CCDS), Microsoft Certified Data Scientist (MCDS), and others are examples of well-respected credentials. Data Science Job Roles For A High Data Science Salary In India 1. Data Scientists Data science is basically statistics implemented through programming. Alongside R, Python has also shown its mettle in sorting out data as per generic as well as specific requirements. As far as India is concerned, Python programmers for data science earn more than both software developers as well as DevOps programmers. The reason for this is that data collection, data cleaning and processing is becoming very common nowadays as companies need data to gather market and customer information. This requires a niche of Python programmers who are specially trained in the collection and processing of data through libraries like NumPy and Pandas. Data scientists are in high demand with a higher Data Science Salary In India, including major metros like Delhi-NCR and Mumbai and emerging cities such as Pune and Bangalore. Data Scientists help the company in working with large data and make effective decisions in a short span of time. The data scientists use statistics, code, analyse the data and draw actionable insights from the data. They also effectively communicate the findings and report those to the concerned stakeholders who are responsible for effective business decision-making. Our learners also read: Excel online course free! Responsibilities of Data Scientists Gathering vast amounts of structured and unstructured data and converting them into actionable insights. Identifying the data-analytics solutions that hold the most significant potential to drive the growth of organisations. Using analytical techniques like text analytics, machine learning, and deep learning to analyse data, thereby unravelling hidden patterns and trends. Encouraging data-driven approach to solving complex business problems. Cleansing and validating data to optimise data accuracy and efficacy. Communicating all the productive observations and findings to the company stakeholders via data visualisation. Featured Program for you: Fullstack Development Bootcamp Course Explore our Popular Data Science Courses Executive Post Graduate Programme in Data Science from IIITB Professional Certificate Program in Data Science for Business Decision Making Master of Science in Data Science from University of Arizona Advanced Certificate Programme in Data Science from IIITB Professional Certificate Program in Data Science and Business Analytics from University of Maryland Data Science Courses Data Scientists Salary Range in India The salary of data scientist in India varies widely based on several factors. Though, The average data scientists salary is ₹698,412. An entry-level data scientist can earn around ₹500,000 per annum with less than one year of experience. Early level data scientists with 1 to 4 years experience get around ₹610,811 per annum. A mid-level data scientist with 5 to 9 years experience earns ₹1,004,082 per annum in India. As your experience and skills grow, your earnings rise dramatically as senior-level data scientists around more than ₹1,700,000 a year in India!   Source The data scientists are highly paid due to the ever evolving nature of this field. The field of data science is evolving, and entrusted with major responsibilities. The demand for these skills are global as the organsiations have become data driven and to bring analytical quotient in the decision making, data scientists plays a major role.  And the demand is equally high in India as well, which is seen in the compensation the data scientists receive, as the data scientist salary in india is high. Some of the top companies that recruit for data science are Amazon, Deloiite, EY, IBM, Microsoft. 2. Data Engineers The primary job of a Data Engineer is to design and engineer a reliable infrastructure for transforming data into such formats as can be used by Data Scientists. Apart from building scalable pipelines to covert semi-structured and unstructured data into usable formats, Data Engineers must also identify meaningful trends in large datasets. Essentially, Data Engineers work to prepare and make raw data more useful for analytical or operational uses. There are many myths about data engineers and most of them are far from reality. In an organization, the position of a Data Engineer is as vital as that of a Data Scientist. The only reason why Data Engineers remain away from the limelight is that they have no direct link to the end product of the analysis. However, with the growing market demand and a growth in the average salary of data scientist in India, people now see a potential career in it, expanding the market even further.  The market scope for data engineering is growing rapidly and the Data Engineering market in India is USD 18.2 billion in 2022. Data Engineers, collect the data, manage it and prepare it for it to be used by the Data scientists. They facilitate in procuring the data to be used either by the business analysts or data scientists. Data engineers mainly work with Java and Python. They gather and process the raw data to create data pipelines. Majorly they work with tools such as NoSQL, Hadoop, etc. Responsibilities of Data Engineers Integrate, consolidate, and cleanse data collected from multiple sources. Prepare raw data for manipulation and predictive/prescriptive modelling by Data Scientists. Develop the necessary infrastructure for optimal extraction, transformation, and loading of data from disparate sources using SQL, AWS, and other Big Data technologies. Deploy sophisticated analytics programs, machine learning algorithms, and statistical techniques to build data pipelines. Assemble vast and complex data sets to cater to the functional and non-functional business requirements. Identify and develop innovative ways to improve data reliability, efficiency, and quality. Develop, construct, test, and maintain data architectures. upGrad’s Exclusive Data Science Webinar for you – document.createElement('video'); https://cdn.upgrad.com/blog/jai-kapoor.mp4 Skills required to be a Data Engineer Active project management and organizational skills. Strong analytic skills to handle and work with large, unstructured datasets. Strong programming flair in trending languages, including Python, Java, C++, Scala, Ruby, etc. Advanced working knowledge of SQL, along with experience in working with relational databases. Proficiency in working with a wide variety of databases. Our learners also read: Learn Python Online Course Free  Top Data Science Skills to Learn Top Data Science Skills to Learn 1 Data Analysis Course Inferential Statistics Courses 2 Hypothesis Testing Programs Logistic Regression Courses 3 Linear Regression Courses Linear Algebra for Analysis Data Engineer Salary Range in India According to Glassdoor, the average Data Engineer salary in India is Rs.8,56,643 LPA. But of course, the Data Engineer salary depends on several factors, including company size and reputation, geographical location, education qualifications, job position, and work experience. Reputed companies and big players in the Big Data industry like Amazon, Airbnb, Spotify, Netflix, IBM, Accenture, Deloitte, and Capgemini, to name a few, usually pay high compensation to Data Engineers. Also, the more your past work experience in Big Data, the higher will be your market value. Must read: Data structures and algorithms free course! “While IT firms have shown a negative trend, the demand for data engineering professionals has increased across the companies, resulting in a significant jump in their salary structure. Whereas for salaries across analytics skills, advanced analytics roles and predictive modelling professionals grabbed the limelight compared to other roles.” As for Data Engineers in their early career (1-4 years of experience), they make anywhere around Rs.7,37,257 LPA. As they proceed to mid-level (with 5-9 years of experience), the salary of a Data Engineer becomes Rs.1,218,983 LPA. Data Engineers having over 15 years of work experience can make more than Rs.1,579,282 LPA. Source Source Also visit upGrad’s Degree Counselling page for all undergraduate and postgraduate programs. 3. Data Analyst Data Analysts are professionals who translate numbers, statistics, figures, into plain English for everyone to understand. They earn a good pay based on data analyst and data scientist salary India. Given the circumstances, there’s always an increasing scope for Data Analysts at the workplace, and it may be an excellent choice for those who have a strong foothold in mathematics, statistics, computer science or business backgrounds. This position includes data mining, fluency in languages like SQL, Python, etc. to extract the relevant insights from the data sets as well as channel those ideas through visualizations and reports. The data Analyst market is expected to grow to USD 655.53 billion by 2029. Data Analysts mine the data, make sure the quality is intact, and prepare the data for better scrutiny. They majorly define the purpose of the data, analyse, interoet and predict the data. Once they are through with the data process, the data analysts present the insights darwin from the data to the stakeholders. The insight could be anything depending upon the purpose of the data.  The top recruiters for Data Analyst are, Deloitte, LinkedIn, Flipkart, IBM, MuSigma, etc. Data Analyst Responsibilities These are some of the responsibilities a data analyst must obtain to obtain lucrative opportunities in the market, just as the high salary of data scientist in India.  To analyze and mine business data to identify correlations and discover valuable patterns from disparate data points. To work with customer-centric algorithm models and personalize them to fit individual customer requirements. To create and deploy custom models to uncover answers to business matters such as marketing strategies and their performance, customer taste, and preference patterns, etc. To map and trace data from multiple systems to solve specific business problems. To write SQL queries to extract data from the data warehouse and to identify the answers to complex business issues. To apply statistical analysis methods to conduct consumer data research and analytics. Read our popular Data Science Articles Data Science Career Path: A Comprehensive Career Guide Data Science Career Growth: The Future of Work is here Why is Data Science Important? 8 Ways Data Science Brings Value to the Business Relevance of Data Science for Managers The Ultimate Data Science Cheat Sheet Every Data Scientists Should Have Top 6 Reasons Why You Should Become a Data Scientist A Day in the Life of Data Scientist: What do they do? Myth Busted: Data Science doesn’t need Coding Business Intelligence vs Data Science: What are the differences? Data Analyst Salary in India For a data analyst in India, having 1 – 4 years of experience has a gross earning (including tips, bonus and overtime pay) of Rs 3,96,128, while a mid-career Data Analyst with 5 – 9 years of experience can make up to Rs 6,03,120 based on the organization and the location of the working place. And if you are a matured and experienced Data Analyst who has been in the industry or 10 – 19 years can earn an average total compensation of Rs 9,00,000. The salary might seem lower than the usually offered data scientist salary per month, but with experience, and skillset, the numbers are bound to grow. Source – 78% of the analytics professionals in India are under the salary bracket of 0 – 6 Lakhs at their entry level, but since there has been a rise in the number of freshers in Data Analysis in India, this is an excellent indication for maturing the industry. – The salaries for 4 – 6 years of experienced remain stable at 8.7 Lakhs. – For the Senior Data Analyst having substantial experience of 12 or more years has witnessed a sharp 20% rise in their salaries last year. Key Reasons to Become a Data Scientist 1. Highly in-demand field Data Science is one of the most in-demand jobs for 2021. It is predicted that by 2026, data science and analytics would be having more than 11 million jobs. After the United States, India is the second prominent hub of jobs for data scientists. The demand is one of the important reason why data scientist salary in India is significantly high. Data is the new oil and the companies have become data driven. With the rising competition, the companies want to respond to the market trends within the shorter span of time. This understanding of the customer’s desires, comes throught the analytical capabilities which are neither vague nor guided by the emotions. That is the reason why data science is adopted and the professionals working in this field are highly paid. ALso, data does not serve only one purpose rather it serves many purposes such as, application and web development, tracking of data, smart devices, sports, news, banking, etc. Today almost each and every industry works on data and it solves many problems. This is also the reason why it is in demand because the scope of employment is not limited to the tech industry. 2. Highly Paid & Diverse Roles Not only is the demand for data scientists booming, but the kinds of job positions are also abundant. Such roles are much needed in any organization today. This adds innovation to the business as well as helps any company crunch through data to actually make sense of the same. The job roles in the data science industry are not limited to the data scientist, data engineer or data analyst. Rather the roles are spreaded, such as- Data Administrator Data Architect Data and Analytics Manager Data Journalist Decision Scientist Statistician 3. Evolving workplace environments Data science is shaping the workplace of the future. With the advent of artificial intelligence and robotics, more and more routine and manual tasks are getting automated, and data science sure is an innovative step in that same direction. Data science technologies have made it possible to train machines in performing repetitive tasks as humans take on more critical thinking and problem-solving roles. For example, the consumer identification for the companies, the companies are able to identify their consumers basis the amount of time they spend looking at a page, their kyeowrds for search, etc. Basis the inputs the companies are able to understand what the customer needs and eventually they target the customers accordingly. 4. Improving product standards Usage of machine learning has enabled companies to customize their offerings and enhance customer experiences. For example, the e-commerce industry. We give reviews post procuring a product online, the given reviews are the data which we have givine as an input. A product has various reviews and ratings given to them. These reviews are useful for the company’s growth as it helps the company to understand the shortcomings. To analyse these reviews rationally is the task of the data ptofessinals whose insights are not driven by the emotions. This is also how the data science has helped the company in improving the product standards.  5. Helping the world Predictive analytics and machine learning have revolutionized the healthcare industry. Data science is saving lives by enabling early detection of tumors, organ anomalies, and more. Also, data science has helped the banking sector by facilitating the understanding of the customer lifetime value, fraud detection, algorithmic trading and customer segmentation. Factors Affecting Data Scientist Salary in India Data Scientist salaries in India can be affected by multiple factors. Let’s see some primary salary affecting factors: Location Experience Skills Company Data Scientist Salary by Location The location plays a vital role since this governs the average payout involved as well as the kind of customers the company caters to. The number of job opportunities and the annual data scientist salary in India for data innovators is the highest in Mumbai, followed by Bangalore and New Delhi. However, since Bangalore is the startup capital of India, it has the most opportunities for jobs in startups. A data scientist’s salary in Bangalore would more likely to be higher than the other cities as it is considered to be the hub of the tech industry of India. There are certain locations where the salaries given to the professional are high, the reason could be high tech companies being situated there, metropolitan cities, cost of living etc. Also, there are certain cities which attract the highly skilled labour, and companies want to acquire or retain the highly skilled labours and for that they give out competitive salaries and benefits. Remember that the further a start-up ecosystem takes shape and form, the jobs in data are proportionately set to go up. With more innovations, newer work strategies, and more interesting product types, customer behavior needs to be understood graphically. Moreover, numbers speak the right stories. Marketing, branding, and even sales are strategized as per what numbers reveal about the results. According to Payscale, Data scientist salary in India based on location: Mumbai – ₹788,789 Chennai – ₹794,403 Bangalore – ₹984,488 Hyderabad – ₹795,023 Pune – ₹725,146 Kolkata – ₹402,978 Bangalore, Chennai and Hyderabad are among the highest-paying cities for the Data Scientists in India How Can I Double My Salary? Data Science is Your Answer Data Scientist Salary by Experience Experience plays a big role behind procuring a high salary, career growth and advancement. As in all jobs, the more you have experience in the domain, the better your worth is to a company. Experience is the testimonial for polished skills, technical advancements and better abilities to solve a problem. But the experience is not only looked from the perspective of the hard skills, the emphasis on the soft skills is equally important. The fresher should learn these skills and the experienced people should polish their skills. And eventually the professionals should also undertake certain projects in order to showcase their skillsets and wide applicability of their knowledge. As you gain more experience, you also have higher exposure. This is what actually adds to your skills and makes you a seasoned player. Data scientist salary per month or annually will dramatically shoot up with every notable jump in experience. Remember that you must keep yourself abreast of the latest trends. Sitting back on your skills will not work in your favor. Re-skilling and learning newer concepts in data is the right road to take. Let’s see how a Data Scientist salary in India varies based on experience. Source A career in data especially appeals to young IT professionals because of the positive correlation between years of work experience and higher-paying salaries. In this section, we will see how data scientist salary increase based on experience. Salaries in the field of data might look something like the following, in the future: For a fresh graduate, the average entry-level data scientist salary in India is ₹511,468. An early career data scientist with 1-4 years of experience earns an average of ₹773,442 annually. An employee with 5-9 years of experience would have the potential to secure between INR 12-14 lakhs. According to payscale, the average mid-level data scientist salary is ₹1,367,306.   A highly experienced employee with decades of experience or who has held managerial roles can expect anywhere from INR 24 lakhs up to a healthy crore of rupees!  A Data analyst’s salary increases by 50% with a transition/promotion from the role designated to them to a higher level. Data Scientist Salary by Skills Skillset is something that you cannot afford to be stagnant on. You need to always be a go-getter and upskill for better career prospects! Companies look for certain skills which help the company in driving their operations better. The importance of having skillsets is given greater emphasis for a reason. Skills are important for data science because the time taken to do a job lessens, and along with that the ability to solve a problem comes with the knowledge of a wide range of skills. Along with the hard skills, the soft skills allow the professionals to think innovatively and work better for better customer satisfaction. In order to secure such a high-paying job, you are expected to go beyond the qualifications of a Master’s degree and be familiar with the respective languages and software utilized for managing data. Some more insights from AIM: Source The most important and coveted skill is being familiar with R followed by Python. Python salary in India alone promises of 10.2 lakhs INR. The combination of knowledge of Big Data and Data Science increases a Data Analyst’s salary by 26% compared to being skilled in only one of the areas. SAS users are paid between INR 9.1-10.8 lakhs versus SPSS experts earning INR 7.3 lakhs. Machine Learning salary in India starts from around 3.5 lakhs INR to if you grow in this field it can take a leap up to 16 lakhs INR. Python is one of the most recommended languages when it comes to ML, and to add to that, Python developers’ salary in India is among one of the highest. The data scientist salary India follows closely as well. Extended knowledge of Artificial Intelligence can help you make a career overall. The Artificial Intelligence salary in India offers not less than 5-6 lakhs INR if you are a novice in this industry. So, now is the time to master your skills in data in order to further optimize your salary! Data Scientist Salary by Companies The average company does pay well for data science jobs but the bigger names tend to pay much more than the average. Without a doubt, prestigious firms dominate the charts of the highest paying salaries for data jobs. They also hold a reputation for increasing salaries by 15%, annually. Some of data scientists salary provided by top firms: Source IBM Corp: INR 1,468,040 Accenture: INR 1,986,586 JP Morgan Chase and Co:  INR 997,500 American Express: INR  1,350,000 McKinsey and Company: INR 1,080,000 Impetus: INR 1,900,000 Wipro Technology: INR  1,750,000 Difference between Data Analyst, Data Scientist, Data Engineer and Data Visualiser Data Scientist Salary in India You can notice an exuberating hike in the Data Scientists salary in India. To sum up, even if you find a tad bit of curiosity in this enterprise, there are much higher chances for you to earn higher than your peers working in any other field. Data Science is a much bigger umbrella that includes a vast variety of subjects that might interest you. From Data Analyst to Machine Learning Engineer, to even Python Developer. All of it comes under the umbrella of “Data Science”, and each of these positions is awarded a hefty salary, obviously, depending on their skillset. Data Science is a high paying career in India, that is reflected in the high compensation. Also, ther is expected to be a hge growth in data science in India and that is also seen in the traction of data science professionals towards the industry. The highly skilled professionals are turning towards data science because of its high applicability in almost every field. Data Scientist Salary in Other Countries Let’s look at the average data scientist salary in other countries. Data Scientist Salary in The US: $96,072 Source Data Scientist Salary in The UK: £40,159 Source Salaries of Other Related Roles  Let’s look at the average salaries of other related roles compared to the Data Scientist salary in India. Software engineer average salary in India: ₹510,982 Senior Business Analyst average salary in India: ₹975,409 Technical Consultant average salary in India: ₹895,842 IT Consultant average salary in India Source Talking about a usual comparison between AI and ML’s potential, there are ups and downs in both fields. It all comes down to the interest of the individual. One thing is for sure, both fields have a huge scope in the future. In fact, you cannot align the two with or against each other. Going forward, the two will entwine together to be used across a large number of business practices and even applications.  Conclusion The opportunities for Analysts and Data Scientists are currently at their prime in India. With the large volumes of data being generated by businesses and the availability of data and tools to extract it – and the urge to gain insights from it. It includes the rise in Data Analyst’s salary and Data Scientists salary India. We hope you liked our article on Data Scientist salary in India. These numbers above are not set in stone. The real influencer of your salary is the skills you have,  the mastery you have attained over them, and how quickly you grow and make the company grow as well. You are likely to receive an annual bump up of around 15% in your salary. This will further increase with an increase in the years of work experience and the number of skills you’ve mastered. Therefore, whether you’re starting from scratch or you’re already experienced in the field of data. You’ll always have this motivating factor driving you in your career! If you are curious about learning data science to be in the front of fast-paced technological advancements, check out upGrad & IIIT-B’s Executive PG Program in Data Science.

Introduction A career in Data Science necessitates hands-on experience, and what better way to obtain it than by working on real-world data mining projects? This post provides a wide range of data mining project ideas for beginners. Whether you’re looking at data mining in database management systems, data mining projects in Java, or creative data mining project ideas, this list has you covered. Today, data mining has become strategically important to organizations across industries. It not only helps in predicting outcomes and trends but also in removing bottlenecks and improving existing processes. Data mining research topics 2020 was already in the search bar of millions of users 2 years ago. It looks like this trend is about to continue in 2023 and beyond. So, if you are a beginner, the best thing you can do is work on some real-time data mining projects.  If you are just getting started in data science, making sense of advanced data mining techniques can seem daunting. Along with the plethora of data mining research topics available online, we have compiled some useful data mining project topics to support you in your learning journey. We, here at upGrad, believe in a practical approach as theoretical knowledge alone won’t be of help in a real-time work environment if you do not work on data mining projects yourself. In this article, we will be exploring some fun and exciting data mining projects and data mining research topics which beginners can work on to put their data mining knowledge to test. In this post, you will learn about top 16 data mining projects for beginners. In this article, you will find 42 top python project ideas for beginners to get hands-on experience on Python But first, let’s address the more important and frequently question that must be lurking in your mind: why to build data mining projects? But before we begin, let us look at an example to decode what data mining is all about. Suppose you have a data set containing login logs of a web application. It can include things like the username, login timestamp, activities performed, time spent on the site before logging out, etc. Our learners also read: Python online course free! Such unstructured data in itself would not serve any purpose unless it is organized systematically and analyzed to extract relevant information for the business. By applying the different techniques of data mining, you can discover user habits, preferences, peak usage timings, etc. These insights can further increase the software system’s efficiency and boost its user-friendliness. Learn more about data mining with our data science programs. In today’s digital era, the computing processes of collecting, cleaning, analyzing, and interpreting data make up an integral part of business strategies. So, data scientists are required to have adequate knowledge of methods like pattern tracking, classification, cluster analysis, prediction, neural networks, etc. The more you experiment with different data mining projects, the more knowledge you gain. Data Mining Project Ideas & Topics for Beginners This list of data mining projects for students is suited for beginners, and those just starting out with Data Science in general. These data mining projects will get you going with all the practicalities you need to succeed in your career. Further, if you’re looking for data mining project for final year, this list should get you going as this list also contains data mining projects for students. So, without further ado, let’s jump straight into some data mining projects that will strengthen your base and allow you to climb up the ladder. Also read: Excel online course free! 1. iBCM: interesting Behavioral Constraint Miner One of the best ideas to start experimenting you hands-on data mining projects for students is working on iBCM. A sequence classification problem deals with the prediction of sequential patterns in data sets. It discovers the underlying order in the database based on specific labels. In doing so, it applies the simple mathematical tool of partial orders. However, you would require a better representation to achieve more accurate, concise, and scalable classification. And a sequence classification technique with a behavioral constraint template can address this need. With the iBCM project, you can delve into the field of sequence categorization. Using behavioral constraint templates, this venture predicts sequential patterns inside datasets. This method employs mathematical tools such as partial orders to reveal underlying data patterns in an accurate and simple manner. Beyond traditional sequence mining, iBCM finds a wide range of patterns, making it a good starting point for inexperienced data miners. The interesting Behavioral Constraint Miner (iBCM) project can express a variety of patterns over a sequence, such as simple occurrence, looping, and position-based behavior. It can also mine negative information, i.e., the absence of a particular behavior. So, the iBCM approach goes much beyond the typical sequence mining representations and is a perfect starting point for those looking for data mining projects for students. 2. GERF: Group Event Recommendation Framework This is one of the simple data mining projects yet an exciting one. It is an intelligent solution for recommending social events, such as exhibitions, book launches, concerts, etc. A majority of the research focuses on suggesting upcoming attractions to individuals. So, a Group Event Recommendation Framework (GERF) was developed to propose events to a group of users. GERF addresses group social event recommendations by utilizing learning-to-rank algorithms for reliable choices. This project provides efficient event recommendations for a varied user population by extracting group preferences and environmental impacts, with applications ranging from exhibitions to travel services. This model uses a learning-to-rank algorithm to extract group preferences and can incorporate additional contextual influences with ease, accuracy, and time-efficiency. Learning to rank, also known as machine-learned ranking (MLR), is the process of building ranking models for systems needing information retrieval using machine learning techniques such as supervised learning, semi-supervised learning, and reinforcement learning. The objects used for training are organized into lists, with the relative order between the lists being partially described. In most cases, a number or ordinal score is assigned to each item, or a binary judgment (such as “relevant” for true values(binary 1) or “not relevant” for false values(binary 0)) is made. The objective of the ranking model is to apply the same logic used to rank the training data to the rating of fresh, unknown lists. Also, it can be conveniently applied to other group recommendation scenarios like location-based travel services.  Explore our Popular Data Science Courses Executive Post Graduate Programme in Data Science from IIITB Professional Certificate Program in Data Science for Business Decision Making Master of Science in Data Science from University of Arizona Advanced Certificate Programme in Data Science from IIITB Professional Certificate Program in Data Science and Business Analytics from University of Maryland Data Science Courses upGrad’s Exclusive Data Science Webinar for you – The Future of Consumer Data in an Open Data Economy document.createElement('video'); https://cdn.upgrad.com/blog/sashi-edupuganti.mp4 3. Efficient similarity search for dynamic data streams Online applications use similarity search systems for tasks like pattern recognition, recommendations, plagiarism detection, etc. Typically, the algorithm answers nearest-neighbor queries with the Location-Sensitive Hashing or LSH approach, a min-hashing related method. It can be implemented in several computational models with large data sets, including MapReduce architecture and streaming. Mentioning data mining projects can help your resume look much more interesting than others. For a variety of functions, online apps rely on similarity search engines. This research focuses on effective similarity search strategies for dynamic data streams, with a special emphasis on scalability in huge datasets. Its novel features, such as the use of the Jaccard index as a similarity measure and estimating techniques based on sketching, improve accuracy in pattern recognition and recommendation tasks. Dynamic data streams, however, require scalable LSH-based filtering and design. To this end, the efficient similarity search project outperforms previous algorithms. Here are some of its main features: Relies on the Jaccard index as a similarity measure Suggests a nearest-neighbor data structure feasible for dynamic data streams Proposes a sketching algorithm for similarity estimation  4. Frequent pattern mining on uncertain graphs Application domains like bioinformatics, social networks, and privacy enforcement often encounter uncertainty due to the presence of interrelated, real-life data archives. This uncertainty permeates the graph data as well. Frequent pattern mining on uncertain graphs is critical in settings requiring uncertain data, such as bioinformatics and social networks. This project addresses the issue of transitive interactions with uncertain graph data. It efficiently manages real-world data archives with increased performance by utilizing enumeration-evaluation methods and approximation techniques. This problem calls for innovative data mining projects that can catch the transitive interactions between graph nodes. This beginner-level data mining projects will help build a strong foundation for fundamental programming concepts. One such technique is the frequent subgraph and pattern mining on a single uncertain graph. The solution is presented in the following format: An enumeration-evaluation algorithm to support computation under probabilistic semantics An approximation algorithm to enable efficient problem-solving Computation sharing techniques to drive mining performance Integration of check-point based and pruning approaches to extend the algorithm to expected semantics 5. Cleaning data with forbidden itemsets or FBIs Data cleaning methods typically involve taking away data errors and systematically fixing the issue by specifying constraints (illegal values, domain restrictions, logical rules, etc.)   Data cleansing frequently entails defining limitations to correct inaccuracies. The FBI’s effort introduces a fixing method based on banned itemset, finding constraints in dirty data automatically and improving error detection precision. Empirical evaluations establish the mechanism’s trustworthiness and dependability, which is critical in the big data scenario. In the real-life big data universe, we are inundated with dirty data that comes without any known constraints. In such a scenario, the algorithm automatically discovers constraints on the dirty data and further uses them to identify and repair errors. But when this discovery algorithm runs on the repaired data again, it introduces new constraint violations, rendering the data erroneous. This is one of the excellent data mining projects for beginners. Hence, a repairing method based on forbidden itemsets (FBIs) was devised to record unlikely co-occurrences of values and detect errors with more precision. And empirical evaluations establish the credibility and reliability of this mechanism.  Top Data Science Skills to Learn Top Data Science Skills to Learn 1 Data Analysis Course Inferential Statistics Courses 2 Hypothesis Testing Programs Logistic Regression Courses 3 Linear Regression Courses Linear Algebra for Analysis 6. Protecting user data in profile-matching social networks This is one of the convenient data mining projects that has a lot of use in the future. Consider the user profile database maintained by the providers of social networking services, such as online dating sites. The querying users specify certain criteria based on which their profiles are matched with that of other users. This process has to be secure enough to protect against any kind of data breaches. There are some solutions in the market today that use homomorphic encryption and multiple servers for matching user profiles to preserve user privacy.  Read our popular Data Science Articles Data Science Career Path: A Comprehensive Career Guide Data Science Career Growth: The Future of Work is here Why is Data Science Important? 8 Ways Data Science Brings Value to the Business Relevance of Data Science for Managers The Ultimate Data Science Cheat Sheet Every Data Scientists Should Have Top 6 Reasons Why You Should Become a Data Scientist A Day in the Life of Data Scientist: What do they do? Myth Busted: Data Science doesn’t need Coding Business Intelligence vs Data Science: What are the differences? 7. PrivRank for social media Social media sites mine their users’ preferences from their online activities to offer personalized recommendations. However, user activity data contains information which can be used to infer private details about an individual (for example, gender, age, etc.) And any leak or release of such user-specified data can increase the risk of interference attacks.  Learn Data Science Courses online at upGrad 8. Practical PEKs scheme over encrypted email in cloud server In the light of current high-profile public events related to email leaks, the security of such sensitive messages has emerged as a primary concern for users worldwide. To that end, the Public Encryption with Keyword Search (PEKS) technology offers a viable solution. This is one of the useful data mining projects in which this combines security protection with efficient search operability functions.  When searching over a sizable encrypted email database in a cloud server, we would want the email receivers to perform quick multi-keyword and boolean searches without revealing additional information to the server. Read: Data Mining Real World Applications 9. Sentimental analysis and opinion mining for mobile networks This project concerns post-publishing applications where a registered user can share text posts or images and also leave comments on posts. Under the prevailing system, users have to go through all the comments manually to filter out verified comments, positive comments, negative remarks, and so on. With the sentiment analysis and opinion mining system, users can check the status of their post without dedicating much time and effort. It provides an opinion on the comments made on a post and also gives the option to view a graph.  10. Mining the k most frequent negative patterns via learning In behavior informatics, the negative sequential patterns (NSPs) can be more revealing than the positive sequential patterns (PSPs). For instance, in a disease or illness-related study, data on missing a medical treatment can be more useful than data on attending a medical procedure. But to the present day, NSP mining is still at a nascent stage. And the ‘Topk-NSP+’ algorithm presents a reliable solution for overcoming the obstacles in the current mining landscape. This is one of the trending data mining and this is how the project proposes the algorithm: Mining the top-k PSPs with the existing method Mining the to-k NSPs from these PSPs by using an idea similar to the top-k PSPs mining  Employing three optimization strategies to select useful NSPs and reduce computational costs Also try: Machine Learning Project Ideas for Beginners 11. Automated personality classification project The automatic system analyzes the characteristics and behaviors of participants. And after observing the past patterns of data classification, it predicts a personality type and stores its own patterns in a dataset. This project idea can be summarized as follows: Store personality-related data in a database Collect associated characteristics for each user Extract relevant features from the text entered by the participant Examine and display the personality traits  Interlink personality and user behavior (There can be varying degrees of behavior for a particular personality type) Such models are commonplace in career guidance services where a student’s personality is matched with suitable career paths. This can be an interesting and useful data mining projects. 12. Social-Aware social influence modeling This is one of the most popular data mining mini projects. This project deals with big social data and leverages deep learning for sequential modeling of user interests. The stepwise process is described below: A preliminary analysis of two real datasets (Yelp and Epinions) Discovery of statistically sequential actions of users and their social circles, including temporal autocorrelation and social influence on decision-making Presentation of a novel deep learning model called Social-Aware Long Short-Term Memory (SA-LSTM), which can predict the type of items or Points of Interest that a particular user will buy or visit next. Long short-term memory, often known as LSTM, is a kind of neural network that is used in the domains of deep learning and artificial intelligence. LSTM neural networks have feedback connections, in contrast to more traditional feedforward neural networks so that they can change the training parameters or hyperparameters to be more precise, with each epoch. LSTM is a kind of recurrent neural network, commonly known as an RNN, which is capable of processing, not just individual data points but also complete data sequences. Experimental results reveal that the structure of this proposed solution enables higher prediction accuracy as compared to other baseline methods. This is one of the data mining mini projects that will definitely help you get some real-world exposure. 13. Predicting consumption patterns with a mixture approach Individuals consume a large selection of items in the digital world today. For example, while making purchases online, listening to music, using online navigation, or exploring virtual environments. Applications in these contexts employ predictive modeling techniques to recommend new items to users. However, in many situations, we want to know the additional details of previously-consumed items and past user behavior. And this is where the baseline approach of matrix factorization-based prediction falls short. This is one of the creative data mining projects.  A mixture model with repeated and novel events offers a suitable alternative for such problems. It aims to deliver accurate consumption predictions by balancing individual preferences in terms of exploration and exploitation. Also, it is one of those data mining project topics that include an experimental analysis using real-world datasets. The study’s results show that the new approach works efficiently across different settings, from social media and music listening to location-based data.  14. GMC: Graph-based Multi-view Clustering  The existing clustering methods for multi-view data require an extra step to produce the final cluster as they do not pay much attention to the weights of different views. Moreover, they function on fixed graph similarity matrices of all views. And this is the perfect idea for your next data mining project as this can also be considered as a graph mining projects. A novel Graph-based Multi-view Clustering (GMC) can tackle this issue and deliver better results than the previous alternatives. It is a fusion technique that weights data graph matrices for all views and derives a unified matrix, directly generating the final clusters. Other features of the graph mining projects include: Partition of data points into the desired number of clusters without using a tuning parameter. For this, a rank constraint is imposed on the Laplacian matrix of the unified matrix. Optimization of the objective function with an iterative optimization algorithm  15. ITS: Intelligent Transportation System A multi-purpose traffic solution generally aims to ensure the following aspects: Transport service’s efficiency Transport safety Reduction in traffic congestion Forecast of potential passengers Adequate allocation of resources Consider a project that uses the above system to optimize the process of bus scheduling in a city. ITS is one of the interesting data mining projects for beginners. You can take the past three years’ data from a renowned bus service company, and apply uni-variate multi-linear regression to conduct passengers’ forecasts. Further, you can calculate the minimum number of buses required for optimization in a Generic Algorithm. Finally, you validate your results using statistical techniques like mean absolute percentage error (MAPE) and mean absolute deviation (MAD). Mean Absolute Percentage Error(MAPE): The accuracy of a forecasting system may be quantified by calculating the mean absolute percentage error (MAPE). Measured as a percentage, it is derived by taking the sum of the absolute values of the errors across all time periods and dividing by the real values to provide a reading on how close the estimate is to the true value. The most popular way to quantify forecast errors is via the use of the mean absolute percentage error (MAPE), perhaps because the variable’s units are already in percentage form. A lack of extremes in the data is necessary for optimal performance (and no zeros). In regression analysis and model assessment, it is frequently used as a loss function. Mean Absolute Deviation(MAD): It measures how far each data point is from the dataset’s mean value. It helps us get a sense of the data’s overall dispersion. To find out the MAD for a data set, we must first calculate the mean and then the distance of each data point from the mean using MPD(Mean positive distances) which would yield the absolute deviation. This absolute deviation is the measure of this gap between the mean and each data point. Now, we take the total of all these deviations, add it and then divide it by the total number of data points in the data set. Also read: Data Science Project Ideas 16. TourSense for city tourism City-scale transport data about buses, subways, etc. could also be used for tourist identification and preference analytics. But relying on traditional data sources, such as surveys and social media, can result in inadequate coverage and information delay. The TourSense project demonstrates how to override such shortcomings and provide more valuable insights. This tool would be useful for a wide range of stakeholders, from transport operators and tour agencies to tourists themselves. This is one of the excellent data mining projects for beginners. Here are the main steps involved in its design:  A graph-based iterative propagation learning algorithm to identify tourists from other public commuters A tourist preference analytics model (utilizing the tourists’ trace data) to learn and predict their next tour An interactive UI to serve easy information access from the analytics Data Mining Projects: Conclusion In this article, we have covered 16 data mining projects. If you wish to improve your data mining skills, you need to get your hands on these data mining projects. Dive into Data Science involves more than just academic understanding; it also necessitates practical experience. These data mining project ideas are designed for novices, with options to investigate sequence classification, group suggestions, similarity search, graph mining, and data cleaning. As you work on these projects, you’ll lay a solid foundation in Data Science and prepare for future challenges in this ever-changing area. Data mining and correlated fields have experienced a surge in hiring demand in the last few years as data mining research topics 2020 was already in the search bar of millions of users 2 years ago and is still there. With the above data mining project topics, you can keep up with the market trends and developments. So, stay curious and keep updating your knowledge! If you are curious to learn about data science, check out IIIT-B & upGrad’s Executive PG Program in Data Science which is created for working professionals and offers 10+ case studies & projects, practical hands-on workshops, mentorship with industry experts, 1-on-1 with industry mentors, 400+ hours of learning and job assistance with top firms.

If you are looking to have a glorious career in the technological sphere, you already know that a qualification in NumPy is one of the most sought-after skills out there. After all, NumPy is built on the de facto standards of computing arrays.  NumPy is one of the commonly used libraries of Python for working with arrays. It is broadly used for performing the vast majority of advanced mathematical calculations on a large scale of data. The NumPy arrays are much faster and more compact than Python lists. There are various advantages of using NumPy as well such as the utilization of lesser storage space. This lesser storage space allows the users to specify the data types. The feature of specifying the data type allows the further optimization of code. A common apprehension is that “Why should we use NumPy rather than Matlab, octave or yorick?” To answer, NumPy supports the operations on arrays of homogenous data. This makes Python act as a really advanced programming language that manipulates numerical data. It increases the functionality and operability of NumPy. Although many relevant questions have been discussed in the article a few basic things should also be known in case the interviewer asks during the NumPy coding questions. Arrays- Arrays in NumPy are a grid of values. All of these values are of the same type.  Function in NumPy-  Some of the functions are mentioned below- numpy.linspace numpy.digitize numpy.random Numpy.nan numpy.repeat Sometimes the interviewer can also ask about the founding year of NumPy, one should be prepared with a brief answer. This can be asked even during numpy interview questions for data science.  NumPy was created in the year 2005 by Travis Oliphant. So, here’s a listing of some commonly asked NumPy interview questions and answers you might want to look up before you appear for your next interview.  Top 15 NumPy Interview Questions and Answers Question 1: What is NumPy? NumPy is an open-source, versatile general-purpose package used for array-processing. It is short on Numerical Python. It is known for its high-end performance with powerful N-dimensional array objects and the tools it is loaded with to work with arrays. The package is an extension of Python and is used to perform scientific computations and other broadcasting functions. NumPy is easy to use, well-optimized, and highly flexible. It is compared with MATLAB on the basis of their functionalities as both of them facilitate writing fast programs as long as most of the functions work on the arrays. NumPy is closely integrated with Python and makes it a much more sophisticated programming language. No Coding Experience Required. 360° Career support. PG Diploma in Machine Learning & AI from IIIT-B and upGrad. Question 2: What are the uses of NumPy? The open-source numerical library on Python supports multi-dimensional arrays and contains matrix data structures. Different types of mathematical operations can be performed on arrays using NumPy. This includes trigonometric operations as well as statistical and algebraic computations. Numeric and Numarray are extensions of NumPy.  Another answer for NumPy data science interview questions could be – “NumPy is used for scientific computing, deep learning, and financial analysis. Various functions can be performed with the aid of NumPy such as the arithmetic operations, stacking, matrix operations, broadcasting, linear algebra, etc.” Question 3: Why is NumPy preferred to other programming tools such as IDL, Matlab, Octave, Or Yorick? NumPy is a high-performance library in the Python programming language that allows scientific calculations. It is preferred to Idl, Matlab, Octave, Or Yorick because it is open-source and free. Also, since it uses Python which is a general-purpose programming language, it scores over a generic programming language when it comes to connecting Python’s interpreter to C/C++ and Fortran code.  NumPy supports multi-dimensional arrays and matrices and helps to perform complex mathematical operations on them.  Question 4: What are the various features of NumPy? As a powerful open-source package used for array-processing, NumPy has various useful features. They are: Contains an N-dimensional array object It is  interoperable; compatible with many hardware and computing platforms Works extremely well with array libraries; sparse, distributed or GPU Ability to perform complicated (broadcasting) functions Tools that enable integration with C or C++ and Fortran code  Ability to perform high-level mathematical functions like statistics, Fourier transform, sorting, searching, linear algebra, etc  It can also behave as a multi-dimensional container for generic data Supports scientific and financial calculations. Can work with various types of databases Provides multi-dimensional arrays Indexing, Slicing, or Masking with other arrays facilitate sin accessing the specific pixels of an image. Must read: Excel online course free! Question 5: How can you Install NumPy on Windows? To install NumPy on Windows, you must first download and install Python on your computer. Follow the steps given below to install Python:  Step 1: Visit the official page of Python and download Python and Python executable binaries on your Windows 10/8/7 Step 2: Open Python executable installer and press Run Step 3: Install pip on your Windows system Using pip, you can install NumPy in Python. Below is the Installation Process of NumPy:  Step 1: Start the terminal Step 2: Type pip  Step 3: install NumPy Explore our Popular Data Science Courses Executive Post Graduate Programme in Data Science from IIITB Professional Certificate Program in Data Science for Business Decision Making Master of Science in Data Science from University of Arizona Advanced Certificate Programme in Data Science from IIITB Professional Certificate Program in Data Science and Business Analytics from University of Maryland Data Science Courses Check out our data science courses to upskill yourself. Question 6. List the advantages NumPy Arrays have over (nested) Python lists? Python’s lists, even though hugely efficient containers capable of a number of functions, have several limitations when compared to NumPy arrays. It is not possible to perform vectorised operations which includes element-wise addition and multiplication.  They also require that Python store the type information of every element since they support objects of different types. This means a type dispatching code must be executed each time an operation on an element is done. Also, each iteration would have to undergo type checks and require Python API bookkeeping resulting in very few operations being carried by C loops.  This makes for one of the commonly asked numpy questions, where the advantages are required to enlist. Another advantage could be the less memory space that is utilized to store the data which helps in further optimization of the code. Scientific computing and array-oriented computing are more aligned advantages of NumPy. Question 7: List the steps to create a 1D array and 2D array A one-dimensional array is created as follows:  num=[1,2,3] num = np.array(num) print(“1d array : “,num)  A two-dimensional array is created as follows:  num2=[[1,2,3],[4,5,6]] num2 = np.array(num2) print(“\n2d array : “,num2) A 1-D array stands for a one-dimensional array that creates the array in one dimension. Whereas the 2D arrays have a collection of rows and columns. Check out: Data Science Interview Questions Question 8: How do you create a 3D array? A three-dimensional array is created as follows:  num3=[[[1,2,3],[4,5,6],[7,8,9]]] num3 = np.array(num3) print(“\n3d array : “,num3) Read our popular Data Science Articles Data Science Career Path: A Comprehensive Career Guide Data Science Career Growth: The Future of Work is here Why is Data Science Important? 8 Ways Data Science Brings Value to the Business Relevance of Data Science for Managers The Ultimate Data Science Cheat Sheet Every Data Scientists Should Have Top 6 Reasons Why You Should Become a Data Scientist A Day in the Life of Data Scientist: What do they do? Myth Busted: Data Science doesn’t need Coding Business Intelligence vs Data Science: What are the differences? Question 9: What are the steps to use shape for a 1D array, 2D array and 3D/ND array respectively? 1D Array: num=[1,2,3] if not added print(‘\nshpae of 1d ‘,num.shape) 2D Array: num2=[[1,2,3],[4,5,6]] if not added print(‘\nshpae of 2d ‘,num2.shape) 3D or ND Array:  num3=[[[1,2,3],[4,5,6],[7,8,9]]] if not added print(‘\nshpae of 3d ‘,num3.shape) Question 10: How can you identify the datatype of a given NumPy array? Use the following sequence of codes to identify the datatype of a NumPy array.  print(‘\n data type num 1 ‘,num.dtype) print(‘\n data type num 2 ‘,num2.dtype) print(‘\n data type num 3 ‘,num3.dtype) Our learners also read: Free Online Python Course for Beginners Question 11. What is the procedure to count the number of times a given value appears in an array of integers? You can count the number of times a given value appears using the bincount() function. It should be noted that the bincount() function accepts positive integers or boolean expressions as its argument. Negative integers cannot be used.  Use NumPy.bincount(). The resulting array is >>> arr = NumPy.array([0, 5, 4, 0, 4, 4, 3, 0, 0, 5, 2, 1, 1, 9]) >> NumPy.bincount(arr)   Must read: Data structures and algorithm free! Question 12. How do you check for an empty (zero Element) array? If the variable is an array, you can check for an empty array by using the size attribute. However, it is possible that the variable is a list or a sequence type, in that case, you can use len(). The preferable way to check for a zero element is the size attribute. This is because:  >>> a = NumPy.zeros((1,0)) >>> a.size 0 whereas >>> len(a) 1 Question 13: What is the procedure to find the indices of an array on NumPy where some condition is true? You may use the function numpy.nonzero() to find the indices or an array. You can also use the nonzero() method to do so.  In the following program, we will take an array a, where the condition is a > 3. It returns a boolean array. We know False on Python and NumPy is denoted as 0. Therefore, np.nonzero(a > 3) will return the indices of the array a where the condition is True.  >>> import numpy as np >>> a = np.array([[1,2,3],[4,5,6],[7,8,9]]) >>> a > 3 array([[False, False, False],        [ True,  True,  True],        [ True,  True,  True]], dtype=bool) >>> np.nonzero(a > 3) (array([1, 1, 1, 2, 2, 2]), array([0, 1, 2, 0, 1, 2])) You can also call the nonzero() method of the boolean array. >>> (a > 3).nonzero() (array([1, 1, 1, 2, 2, 2]), array([0, 1, 2, 0, 1, 2])) Read: Dataframe in Apache PySpark: Comprehensive Tutorial Question 14: Shown below is the input NumPy array. Delete column two and replace it with the new column given below. import NumPy sampleArray = NumPy.array([[34,43,73],[82,22,12],[53,94,66]])  newColumn = NumPy.array([[10,10,10]]) upGrad’s Exclusive Data Science Webinar for you – The Future of Consumer Data in an Open Data Economy document.createElement('video'); https://cdn.upgrad.com/blog/sashi-edupuganti.mp4   Expected Output: Printing Original array [[34 43 73]  [82 22 12]  [53 94 66]] Array after deleting column 2 on axis 1 [[34 73]  [82 12]  [53 66]] Array after inserting column 2 on axis 1 [[34 10 73]  [82 10 12]  [53 10 66]] Solution: import NumPy print(“Printing Original array”) sampleArray = NumPy.array([[34,43,73],[82,22,12],[53,94,66]])  print (sampleArray) print(“Array after deleting column 2 on axis 1”) sampleArray = NumPy.delete(sampleArray , 1, axis = 1)  print (sampleArray) arr = NumPy.array([[10,10,10]]) print(“Array after inserting column 2 on axis 1”) sampleArray = NumPy.insert(sampleArray , 1, arr, axis = 1)  print (sampleArray) Data Science Advanced Certification, 250+ Hiring Partners, 300+ Hours of Learning, 0% EMI Top Data Science Skills to Learn Top Data Science Skills to Learn 1 Data Analysis Course Inferential Statistics Courses 2 Hypothesis Testing Programs Logistic Regression Courses 3 Linear Regression Courses Linear Algebra for Analysis Question 15: Create a two 2-D array. Plot it using matplotlib Solution: import NumPy print(“Printing Original array”) sampleArray = NumPy.array([[34,43,73],[82,22,12],[53,94,66]])  print (sampleArray) print(“Array after deleting column 2 on axis 1”) sampleArray = NumPy.delete(sampleArray , 1, axis = 1)  print (sampleArray) arr = NumPy.array([[10,10,10]]) print(“Array after inserting column 2 on axis 1”) sampleArray = NumPy.insert(sampleArray , 1, arr, axis = 1)  print (sampleArray) How NumPy and Pandas Revolutionized Data Analysis In the world of data analysis and manipulation, NumPy and Pandas have emerged as two powerful tools that have transformed the way professionals handle and process data. These libraries provide adaptable and efficient solutions to a variety of data-related problems. Let’s look more closely at how NumPy and Pandas have transformed data analysis. Streamlined Data management: Before NumPy and Pandas, data management and manipulation were generally time-consuming and tedious processes. Analysts and data scientists had to resort to intricate loops and complex code to perform even basic operations. NumPy introduced the concept of arrays, enabling vectorized operations that significantly expedited tasks like element-wise calculations, array transformations, and aggregations. Pandas further elevated this by introducing DataFrames, simplifying the representation and manipulation of tabular data. This simplified method improved performance while also making the code more readable and maintained. Bridging the Domain Gap: NumPy and Pandas have played critical roles in bridging the domain gap within the data environment. Data analysis, scientific computing, and machine learning often require a seamless integration of mathematical operations and data processing. NumPy’s array-based operations allowed professionals from diverse backgrounds to leverage their domain-specific knowledge while efficiently performing mathematical computations. Similarly, Pandas’ tabular data structure facilitated collaboration between analysts, data engineers, and domain experts, as it provided a standardized and intuitive way to work with data across disciplines. Accelerating Innovation: The introduction of NumPy and Pandas sparked innovation by enabling faster experimentation and development. Researchers, analysts, and data scientists could focus more on formulating hypotheses, designing experiments, and extracting insights, rather than getting entangled in intricate data manipulation code. This acceleration in the data analysis process led to quicker iterations and facilitated the discovery of patterns, trends, and correlations within datasets. As a result, these libraries played a significant role in driving advancements in fields such as scientific research, finance, healthcare, and more. Embracing the Power of NumPy and Pandas in Your Career In today’s data-driven world, knowing NumPy and Pandas can boost your professional chances and open doors to new opportunities. These libraries have become indispensable resources for professionals involved in data analysis, machine learning, research, and a variety of other fields. Let’s look at how using NumPy and Pandas may help you advance in your profession. Enhanced Employability: Proficiency in NumPy and Pandas is highly valued by employers seeking candidates with strong data analysis and manipulation skills. Whether you’re applying for a data analyst, data scientist, or research position, showcasing your ability to efficiently handle and process data using these libraries can give you a competitive edge in the job market. Many job descriptions explicitly mention these skills as prerequisites, underscoring their importance. Lifelong Learning and Growth: NumPy and Pandas remain at the forefront of data analysis and manipulation as the data environment evolves. You are going on a path of lifetime learning and progress by devoting time and effort to mastering these resources. Their vast documentation, active forums, and ongoing development guarantee that there is always something new to learn and apply to your skill set. As you gain a deeper grasp of NumPy and Pandas, you will be better prepared to adapt to future data technologies and approaches. Conclusion We hope the above-mentioned NumPy interview questions will help you prepare for your upcoming interview sessions. If you are looking for courses that can help you get a hold of Python language, upGrad can be the best platform. If you are curious to learn about data science, check out IIIT-B & upGrad’s Online Data Science Programs which are created for working professionals and offer 10+ case studies & projects, practical hands-on workshops, mentorship with industry experts, 1-on-1 with industry mentors, 400+ hours of learning and job assistance with top firms.  We hope this helps. Good luck for with your Interview!

Engineering projects provide excellent avenues for professional growth, whether you want to enter the tech arena as an employee or an entrepreneur. We have compiled a list of the most popular project ideas for engineering students to guide your search. Tips for Selecting Science Project Work Before you dive into a particular project ideas for engineering students spend some time charting out the fundamentals. Here is a summary of the steps you must follow during the ideation, implementation, and post-completion phases.  Explore your interest areas Start the journey by looking at your interests. That means searching topics within the realm of science that intrigue you the most. Is it entirely biology, chemistry, physics, astronomy, environmental science, or something else? Choosing a project that aligns with your passions will make the process more enjoyable and engaging. Define a Clear Objective Before settling on a project, establish a clear objective. What do you want to achieve with your project? Whether exploring a specific scientific phenomenon, answering a research question, or solving a practical problem, having a well-defined goal will guide your project’s direction. Consider Feasibility Assess the resources available – time, materials, equipment, and expertise. Make sure that your chosen project is realistic within your constraints. A project that’s too ambitious might lead to frustration, while one that’s too simple might not offer enough depth. Research Existing Work Conduct thorough research to understand what’s already been done in your chosen area of interest. This will help you avoid duplicating existing work and give you insights into gaps or opportunities for further exploration. Brainstorm Ideas Generate a list of potential project ideas for engineering students. Consider your questions about the natural World or problems you’d like to solve. Brainstorming allows you to explore various options before narrowing down to the most compelling idea. Focus on Originality While it’s important to research existing work, strive to bring originality to your project. Look for ways to approach a topic from a new angle, add a unique twist, or combine different areas of science to create something novel. Consider Relevance Select a engineering project ideas that has relevance and real-world implications. Projects that address current issues, like environmental concerns or medical advancements, tend to have greater impact and significance. Testable Hypothesis Formulate a clear and testable hypothesis for your project. A hypothesis is a statement that predicts the outcome of your experiment or investigation. It serves as the foundation of your project’s methodology and analysis. Plan Your Experiment Design a detailed plan for conducting your experiment or investigation. Outline the materials you’ll need, the procedure you’ll follow, and the data you’ll collect. A well-structured experiment ensures accurate results and a smoother project experience. Seek Guidance Consult with teachers, mentors, or experts in the field. Their insights can help refine your project idea, guide experimental design, and offer valuable feedback. Embrace Challenges Science projects for engineering students often come with unexpected challenges and setbacks. Embrace these as learning opportunities. Problem-solving and adapting to unforeseen circumstances are valuable skills in the World of science. Ethical Considerations If your project involves human subjects, animals, or potentially hazardous materials, ensure that you adhere to ethical guidelines and obtain any necessary permissions or approvals. Keep a Detailed Record Maintain a thorough lab notebook or project journal to document every step of your project – from initial ideas and experimental setups to results and conclusions. This documentation is crucial for presenting your work and validating your findings. Analyze Results Once your experiment is complete, analyze the data you’ve collected. Interpret the results in the context of your hypothesis and draw meaningful conclusions. Communicate Your Findings Present your innovative project ideas for engineering students findings through a report, presentation, or poster. Effective communication of your work is essential for sharing your discoveries and insights with others. Top 10 Engineering Projects in 2023 1. Electronics Electronics projects deal with circuits, resistors, microcontrollers, etc. You can find many examples of electronic appliances that are integrated with emerging technology features. For instance, you may come across a speed detecting device that flashes a laser beam when a vehicle exceeds the predetermined limit. Or a device that can track electricity usage and send updates to your smartphone via SMS. If you are into data authentication, you can build a biometric system that confirms user IDs based on their fingerprint.  You can also choose a topic depending upon the industry or sector, such as: Agriculture: Powered tiller and weeder for farms; Android-based monitoring device for greenhouse environments; tracking system for solar panels. Biomedical: Heart rate and temperature monitoring device for patients; Bluetooth or WiFi transmission device for ECG signals. Spatial/Locational: Arduino-based GPS tracker; remotely operated vehicles. Home assistance: Door locking system through password mechanism; home appliances Control through a smartphone; water level indicators for tanks. 2. Mechanical These projects span across automation, mechatronics, and robotics, sometimes requiring cross-disciplinary knowledge. You can discover standard applications in the following areas: Energy and Environment: Wind and solar power charging station, Food shredder compost machine.  Home appliances: Air purifier and humidifier; solar water heater; mattress deep cleaning device. Manufacturing: Wireless material handling system; automatic hydraulic jack; automobile prototyping. Ecommerce: Automatic sorting system using images; theft-proof delivery robot. Additionally, you can delve into the world of three-dimensional objects and computer-aided design with 3D printers. Building a 3D printer from scratch will bring a practical dimension to your knowledge of additive manufacturing, CAD models, RAMPS Boards, SMPS and Motor Drivers, Arduino Programming, etc.  3. Robotics Robotics is a multidisciplinary field specializing in electronics, mechanical engineering, and artificial intelligence technologies. It is expected to transform lives and how we work in the recent future. To stay one step ahead of the change, you can try out any of the following project ideas and master different robotics skills.  Surveillance robot that captures live video footage and transmits to remote locations over the internet.  Mobile-controlled robotic arm with multiple degrees of freedom. A voice-controlled robot that uses speech recognition, android app development, Bluetooth communication, and Arduino programming to perform specific tasks. An intelligent robot that can solve a problem, such as come out of a maze puzzle. A fire-fighting robot, equipped with digital IR sensors and a DC fan, detects the fire and moves to put it off.  4. Machine Learning Machine Learning (ML) projects can help you gain conceptual clarity and hands-on experience in applying Mean Squared Error Function, Update Function, Linear Regression, Gaussian Naive Bayes Algorithm, Confusion Matrix, Tensor Flow & Keras Libraries, Clustering, among other things.  Sentiment analysis (using text mining and computational linguistics) to uncover customer options and market trends. Credit Card fraud detection project using ML algorithms and Python language.  A recommendation engine that suggests movie titles based on a user’s viewing history. Handwritten digit organizer that applies deep learning like convolutional neural networks. 5. Data Science and Analytics Budding data scientists can choose from various projects and tutorials to learn web scraping, data cleaning, exploratory data analysis or EDA, data visualization, etc.  Below are some examples:  Web scraping project uses Scrapy or Beautiful Soup to crawl public data sets on the internet for relevant insights. Data scrubbing project that introduces you to the fundamentals of removing incorrect and duplicate data, managing gaps, and ensuring consistent formatting. EDA project where you ask questions about the data, discover its underlying structure, look for patterns and anomalies, test hypotheses, validate assumptions, and so on.  A visualization and manipulation project using R and its various packages.   6. Computer Vision Computer Vision is a subfield of Artificial Intelligence, encompassing object recognition, image processing, facial recognition, among other things. You can develop a text scanner with optical character recognition capabilities and display the text on a screen. Or build an intelligent selfie device that takes snaps when you smile and stores them on your smartphone.  Free tools like Lobe can help you select the right architecture for Computer Vision projects involving image classification. Once you have trained the model, deploying it on a website only takes a few simple steps. 7. Internet of Things (IoT) Budding software professionals can implement several projects to gain familiarity with IoT concepts and applications, Arduino architecture and programming, interfacing and calibrating sensors, and integration of cloud platforms. Consider these two examples: Smart Building Project: You can develop a system that senses the number of occupants to switch the lights on or off automatically. You can further analyze the usage of rooms, occupancy at different times of the day, and the total power consumed. Automated Street Lighting: Here, you build a public street lighting system capable of adjusting according to the amount of sunlight present. It is an energy-efficient solution that sends data to a cloud for storage and analysis. 8. Python Projects Python has extensive use cases spanning web development, data science, and machine learning. Beginner programmers can hone their python language skills with the following project ideas: QR code generation encodes data like contact details, YouTube links, app download links, digital transaction details, etc.  GUI application using Tkinter that you can use to generate the calendar for any year.  An application that converts images into pencil sketches with the aid of the OpenCV library. 9. Android App Projects As advanced mobile technologies gain prominence across global markets, Android app development is necessary from a tech career perspective. Engineering projects can bring you up to speed with Java, Firebase, networking basics, and the launch process on Playstore. You can start your quest with any of these platforms. Flutter Project: Learn to build apps for authentication activities using the Dart programming language. Android Studio Project: Try your hand at creating online stores, fitness apps, social media apps, etc. 10. Cloud Computing  The possibilities for cloud computing projects are endless: Bring software development and IT operations together with a DevOps project, or host static websites on the Amazon Web Service (AWS) or Azure platforms. With regular practice, you can move on to building dynamic websites and go serverless with your applications and services. Since cloud computing is among India’s leading technology skills, pursuing project work on this topic will give you an edge in job applications as well. Other Engineering Projects Civil engineers and architects can also utilize projects to combine domain knowledge with smart technologies and project management tools. Such projects typically train you in 3D modeling, rendering techniques, critical path method, project budgeting techniques, etc.  Learn Software Development Courses online from the World’s top Universities. Earn Executive PG Programs, Advanced Certificate Programs or Masters Programs to fast-track your career. Wrapping up With this, we have compiled some exciting engineering projects for you to try. You can consider upskilling with upGrad’s online courses and implement industry projects and build a stellar portfolio to differentiate yourself from the competition. For instance, Fullstack Development Program from Purdue University is an excellent choice for candidates who wish to master the nitty-gritty of full-stack development and build complete applications.  Don’t wait. Pick the right program and start your learning journey today!