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Summary: In this article, you will learn the 17 Exciting Final Year Projects Ideas & Topics 2023. Take a glimpse below. Python Final Year Projects Alarm clock Address book Currency converter Magic 8 ball Dice rolling simulator Data Science Final Year Projects Gender and age detection system Emotion recognition software Customer Segmentation system Android chatbot Movie recommendation system Fraud app detection software Machine Learning Final Year Projects Stock price prediction system Credit scoring system Online examination and evaluation system Fitness activity recognition for smartphone Handwritten digit classification system Personality prediction system Read the full article to know more about the project Ideas & Topics in detail. The final year of a graduation course is one of the most crucial stages of your education and professional grooming. While the initial three years of science stream graduation courses like Computer Science and Engineering (CSE), Computer Engineering (CE)/Computer Science (CS), Information technology (IT), and Electrical and Computer Engineering (ECE) focus on theoretical aspects, in the final year, students get to put their theoretical knowledge to test. This is when students work on practical assignments and projects. The main goal behind including final-year projects in the course curriculum is to encourage students to apply their theoretical knowledge to practical use. Working on final-year projects allows students to couple their intellectual faculties with practical skills to solve real-world engineering and business problems. Check out our free courses offered by upGrad under IT technology. READ: Statistics for data science free courses Learners receive an average salary hike of 58% with the highest being up to 400%. Students can choose their final year projects in specialized study areas to acquire comprehensive knowledge and build niche skills in that domain. Furthermore, while working on their final year projects, students get a more in-depth insight into real-world functional processes. The objectives of final year projects include: To create a platform for students to demonstrate their practical competence. To encourage students to apply their subject knowledge gained in the degree course. To help students sharpen their intellectual qualities like creative thinking, analytical abilities, teamwork, and communication skills. Final year projects are designed to help students to expand their creative abilities by building a new system from scratch. Also, these projects push students to develop their communication skills, both verbal and written. While verbal skills develop throughout the project development process when students engage in one-on-one interactions and discussion sessions with their supervisors, written skills develop through detailed report writing. These reports are pivotal to the final evaluation of each student.  Check Advanced Certification in Digital Marketing from MICA The bottom line – is final year projects prepare students for the professional world. After all, it is easier to catch potential employers’ eyes when your resume highlights your hands-on experiences and projects.   But what exactly can you do with Python programming? And how does it help accomplish so many projects? Let’s find out before diving into learning college project ideas for college students and final-year engineering projects. How to Choose The Project Domain in the Final Year? Selecting the right project domain for the final year is one of the important decision that can significantly impact the academic and professional future. So, keeping this in mind let’s learn about key points to consider while selecting a project domain: 1. Passion and Interest Identify Your Interests: Consider what subjects, topics, or technologies excite you. Passion drives motivation and creativity in your project. Explore Previous Courses: Reflect on the courses you enjoyed the most during your academic journey. Think about the topics that captured your attention. 2. Relevance to Future Goals Career Alignment: Choose a domain that aligns with your future career goals. It should be relevant to the industry or field you plan to work in after graduation. Skill Enhancement: Opt for a domain that allows you to enhance skills valued in your desired profession, whether programming, research, design, or management. 3. Feasibility and Resources Available Resources: Consider the availability of books, journals, online courses, and experts in the chosen domain. Access to Tools and Technologies: Ensure you have access to the tools and technologies required for your project. Consider the availability of software, hardware, and datasets. 4. Complexity and Scope Balancing Complexity: Evaluate the complexity of the project for final year. It should be challenging enough to showcase your skills but not so complex that it becomes unmanageable. Define Scope: Clearly define the scope of your project. Be specific about what you want to achieve and what you can realistically accomplish within the given time frame. 5. Social Impact and Innovation Social Relevance: Explore domains that have a positive impact on society. Projects addressing real-world problems can be highly rewarding and impactful. Innovation: Consider projects that involve innovative solutions or cutting-edge technologies. It can make your project stand out and attract attention. 6. Consultation and Guidance Seek Guidance: Consult with professors, mentors, or industry experts. They can provide valuable insights and suggestions based on your interests and skills. Peer Discussions: Discuss potential domains with your peers. Their perspectives might help you see the project from different angles. 7. Evaluate Previous Projects Review Past Projects: Look into previous projects undertaken by students in your department. It can inspire ideas or help you identify areas not explored extensively. 8. Personal Growth and Learning Learning Opportunities: Choose a domain that offers learning and skill development opportunities. Your project should challenge you to learn new concepts and techniques. Personal Growth: Consider how the project will contribute to your personal and academic growth. Will it push your boundaries and help you develop as a professional? Python Programming to Create Interesting Things Python, a high-level interpreted language, can support various computation processes through shorter codes. Its easy-to-use syntax and versatile layout make it very popular among developers. Python programming’s implementation has helped create many interesting final-year engineering projects for college students and diploma final-year project topics. Let’s look at some of them. Python for Machine Learning and AI Python is a popular language among developers and data scientists used for creating Machine Learning and AI workflows. Instead of using programming languages with lengthy codes, data scientists prefer Python, which adds precision to ML projects. Thanks to its reliable and flexible nature, data science professionals use Python to develop ML and AI algorithms for deep learning college project ideas.  Python for Web Development Being an open-source language, Python is a go-to for developers to use throughout the web development process. Python allows extensive access to its vast framework and modules, equipped with relevant code bundles for different use cases. Python also extends various web development frameworks, including Django, Giotto, and Flask. Top companies like Facebook, Spotify, and Mozilla use Python.  Python for Data Visualization Modern organizations use data visualization to facilitate accurate data representation. Python libraries like Matplotlib, Seaborn, Plotly, etc., are excellent tools for data visualization. The libraries contain different features and capabilities for visualizing descriptive data into a more comprehensible format for both tech and non-tech professionals. Python for Programming Applications Developers can use Python programming to create various software applications, both for mobile and desktop. From video, audio, or picture applications to blockchain apps, Python assists in creating GUIs and APIs for apps and reinforces them with a smooth functioning platform, strengthening practice for diploma final year project topics.  Python for Finance Python can assist data scientists in creating algorithms to find patterns and make predictions by leveraging the collected data. Quantitative and qualitative analysis in the finance sector can help organizations to make insightful decisions. Python libraries like Theano, PyTorch, TensorFlow, Pandas, etc., help data scientists in manifold ways. If you are a final-year student, this article is just what you need! Today, we’ll talk about a few final-year project ideas that will make the choosing process much easier. So, let’s get right into it! Read: Top 10 Highest Paying Jobs in India Final Year Project Ideas Worth Trying We’ve compiled a list of final year project ideas divided under Python projects, Data Science projects, and Machine Learning projects.  Python Final Year Projects 1. Alarm clock This beginner-level Python project is quite practical since almost everyone uses an alarm clock on a daily basis. The project is a CLI(Command Line Interface) application with a unique twist. Apart from the standard alarm clocks features like a clock, alarm, stopwatch, and timer, this alarm clock has YouTube integration. You can include YouTube links in a text file and code the application to read the file. So, when you set a time for an alarm, the app will choose a random YouTube link stored in the text file and start playing the video. Read: Career in data science and its future growth 2. Address book The address book project is a pretty simple GUI application wherein users can add multiple contact details, displaying them in a list format. Users can add and store contact details like name, contact number, and address. To add new contact information, a user needs to type the desired information in the text fields and click on the add button to add the record. They can also delete any contact record that they no longer need. The three core components for this Python final year project are AddressBook.py, db.py, and gui.py. Read: Career options in science after graduation 3. Currency converter Another GUI application in the list, this project involves building a currency converter that can convert one currency’s value into another currency unit. For instance, you can convert the Indian rupee into a dollar or pound and vice versa. The challenge that lies here is that the value of currencies fluctuates daily. However, you can solve this issue by importing an excel spreadsheet containing the updated currency values. To build this project, you must have a basic knowledge of python programming and the Pygame library. Explore Our Software Development Free Courses Fundamentals of Cloud Computing JavaScript Basics from the scratch Data Structures and Algorithms Blockchain Technology React for Beginners Core Java Basics Java Node.js for Beginners Advanced JavaScript 4. Magic 8 ball This is a super fun project for beginners. A Magic 8 ball is a spherical toy designed for fortune-telling and advice-seeking. Just like a toy Magic 8 ball, this application will also provide answers to users’ questions. However, here, you have to allow the users to enter their question, display an “in-progress” message, and finally reveal the answer. For example, if a user asks, “what is my favorite color?” the answer could be the name of any random color or a simple “yes” or “no.” So, you will have to program at least 10 to 20 responses. Also, the app should have the option to let the users continue playing or quit the game.  Our learners also read – python course for free! 5. Dice rolling simulator The dice rolling simulator is a Python application that can imitate the functions of a physical rolling dice. It works something like this – when a user rolls the dice in the game, it will generate a random number between 1 to 6 and display the final answer. The user can roll the dice any number of times they want since the program has the option of rolling the dice repeatedly. Essentially, the dice-rolling simulator should be able to pick and display a random number each time a user rolls the dice.  Checkout: Python Project Ideas Data Science Final Year Projects 1. Gender and age detection system The gender and age detection application is a popular Data Science final-year project that helps strengthen your programming skills. For developing the gender and age detection project, you will need Python, Support Vector Machine, and Convolutional Neural Network. Fortunately, you’ll get plenty of datasets for training the model. As the name suggests, the application can predict an individual’s gender and age through image recognition. Thus, once you feed a person’s image into the model, it will display their gender and age.  2. Emotion recognition software In this project, you will develop an emotion recognition system with integrated audio input. It is a simple yet practical final-year project for students to build their real-world skills. The components required for this project include Python, Support Vector Machine, RNN algorithm, and Convolutional Neural Network. You can use the Vox celebrity dataset having different voice samples for training the model, while the Librosa package can be used to extract and classify audio samples. It is an excellent application for people with a hearing impairment. Also, Check out online degree programs at upGrad. 3. Customer Segmentation system Customer segmentation is a popular method used by brands to get a deeper insight into their target audience via unsupervised learning. Customer segmentation helps segment a brand’s target audience into different buyer personas according to factors like buying behavior, gender, age, location, income, interests, and preferences. The project uses the partition method to split the customers according to these attributes. Other requirements for the customer segmentation project are R, K-mean clustering, Density-based clustering, and Model-based clustering.  4. Android chatbot This is a general chatbot for the Android platform. It is designed to understand users’ queries and the intent behind them and provide relevant answers. So, when a user enters their question in the system, the bot will analyze the keywords and generate an appropriate response for the specific query. The chatbot can communicate with humans on a wide range of topics, including sports, health, education, entertainment, etc. Since chatbots are hugely popular now, this project is an excellent choice for final-year students.  5. Movie recommendation system With online content platforms becoming more and more popular every day, thanks to personalized content suggestions, recommendation engines have become the latest trend in the digital domain. You can create a movie recommendation system using R and Collaborative Filtering. This project’s main goal is to study a user’s browsing and viewing history and recommend movies that match their interests. This final-year project is an ideal choice for aspirants who wish to understand the mechanisms of recommendation engines.  Read: How to make a chatbot in Python? 6. Fraud app detection software Both Apple Store and PlayStore are replete with fraudulent apps. Malicious apps can not only damage the phone’s normal functioning but also access and misuse sensitive data stored on the phone. Here, you’ll develop software that can process the information, comments, and user review of apps in the Apple Store/PlayStore to determine whether or not it is a genuine apps. The software can process multiple applications simultaneously.  Read: Data Science Project Ideas Machine Learning Final Year Projects 1. Stock price prediction system In this ML project, you will build a stock price predictor that can predict the future prices of stock. The best thing about working with stock market data is that it generally has short feedback cycles, making it easy for data analysts to use new market data to validate stock price predictions. However, stock market data tends to be very granular, varied, and volatile. You can model this stock price predictor to perform simple calculations like predicting an organization’s six-month price movement based on fundamental indicators from its quarterly report. You can also model it to find and group similar stocks based on their price movements and identify periods when there are significant fluctuations in their prices. 2. Credit scoring system The credit scoring system determines a user’s credit scoring using Big Data. This ML project combines social network analytics with mobile phone data to evaluate the credibility of users. Since it feeds on colossal amounts of financial data from across different countries and studies a comprehensive range of financial metrics (factors), the ML model features an enhanced decision-making process for determining the credit score.  3. Online examination and evaluation system In this ML project, you will build an application that will allow students to give their admission test online. According to the marks obtained in the test, the system will generate a list of colleges fit for a student. This application’s main aim is to offer a quick and hassle-free process of appearing for online exams and accessing the results almost immediately. The admission test conducted via this platform will have multiple-choice options, and built-in AI verifies the answers.  4. Fitness activity recognition for smartphone This ML project uses smartphone data, particularly fitness activity data captured through the phone’s inertial sensors. This fitness activity recognition project’s primary goal is to design a classification model that can identify human fitness activities like running, cycling, speedwalking, etc. If you choose this as one of your final year projects, it will help you understand how to build ML models for solving multi-classification problems. Also read, Career options in medical 5. Handwritten digit classification system This project is an excellent way to understand Deep Learning and how neural networks function. It is essentially based on image recognition. One of the best datasets for this project is the MNIST dataset because it is both varied and beginner-friendly. In this project, you will learn how to teach a machine (ML model) to understand and classify handwritten digits’ images as ten digits (0–9). The goal is to train the model to recognize numbers from disparate sources like bank cheques, images, emails, and anything else containing a numeric entry. 6. Personality prediction system This ML project focuses on building an automated personality classification system using advanced ML algorithms and data mining techniques to extract user behavior and characteristics data and find meaningful patterns. It can classify and predict users’ personalities based on past classifications as well. The system studies the observed patterns stored in its vast database and predicts a new user’s personality based on similar patterns. This is a handy tool for brands that offer personalized products to customers based on their personalities. Read: Machine Learning Project Ideas Wrapping up All these projects will be excellent additions to your portfolio as they will showcase your real-world skills and hands-on experience to prospective employers. So, which of these will you choose as your final year project? If you are curious about learning data science to be in the front of fast-paced technological advancements, check out upGrad & IIIT-B’s PG Diploma in Data Science.

Pandas is a BSD-licensed and open-source Python library offering high-performance, easy-to-use data structures, and data analysis tools. Python with Pandas is used in a wide array of disciplines, including economics, finance, statistics, analytics, and more. In this article, we have listed some essential pandas interview questions and NumPy interview questions that a python learner must know. If you want to learn more about python, check out our data science programs. What are the Different Job Titles That Encounter Pandas and Numpy Interview Questions? Here are some common job titles that often encounter pandas in python interview questions. 1. Data Analyst Data analysts often use Pandas to clean, preprocess, and analyze data for insights. They may be asked about their proficiency in using Pandas for data wrangling, summarization, and visualization. 2. Data Scientist Data scientists use Pandas extensively for preprocessing and exploratory data analysis (EDA). During interviews, they may face questions related to Pandas for data manipulation and feature engineering. 3. Machine Learning Engineer When building machine learning models, machine learning engineers leverage Pandas for data preparation and feature extraction. They may be asked Pandas-related questions in the context of model development. 4. Quantitative Analyst (Quant) Quants use Pandas for financial data analysis, modeling, and strategy development. They may be questioned on their Pandas skills as part of the interview process. 5. Business Analyst Business analysts use Pandas to extract meaningful insights from data to support decision-making. They may encounter Pandas interview questions related to data cleaning and visualization. 6. Data Engineer Data engineers often work on data pipelines and ETL processes where Pandas can be used for data transformation tasks. They may be quizzed on their knowledge of Pandas in data engineering scenarios. 7. Research Analyst Research analysts across various domains, such as market research or social sciences, might use Pandas for data analysis. They may be assessed on their ability to manipulate data using Pandas. 8. Financial Analyst Financial analysts use Pandas for financial data analysis and modeling. Interview questions might focus on using Pandas to calculate financial metrics and perform time series analysis. 9. Operations Analyst Operations analysts may use Pandas to analyze operational data and optimize processes. Questions might revolve around using Pandas for efficiency improvements. 10. Data Consultant Data consultants work with diverse clients and datasets. They may be asked Pandas questions to gauge their adaptability and problem-solving skills in various data contexts. What is the Importance of Pandas in Data Science? Pandas is a crucial library in data science, offering a powerful and flexible toolkit for data manipulation and analysis. So, let’s explore Panda in detail: – 1. Data Handling Pandas provides essential data structures, primarily the Data Frame and Series, which are highly efficient for handling and managing structured data. These structures make it easy to import, clean, and transform data, often the initial step in any data science project. 2. Data Cleaning Data in the real world is messy and inconsistent. Pandas simplifies the process of cleaning and preprocessing data by offering functions for handling missing values, outliers, duplicates, and other data quality issues. This ensures that the data used for analysis is accurate and reliable. 3. Data Exploration Pandas facilitate exploratory data analysis (EDA) by offering a wide range of tools for summarizing and visualizing data. Data scientists can quickly generate descriptive statistics, histograms, scatter plots, and more to gain insights into the dataset’s characteristics. 4. Data Transformation Data often needs to be transformed to make it suitable for modeling or analysis. Pandas support various operations, such as merging, reshaping, and pivoting data, essential for feature engineering and preparing data for machine learning algorithms. 5. Time Series Analysis Pandas are particularly useful for working with time series data, a common data type in various domains, including finance, economics, and IoT. It offers specialized functions for resampling, shifting time series, and handling date/time information. 6. Data Integration It’s common to work with data from multiple sources in data science projects. Pandas enable data integration by allowing easy merging and joining of datasets, even with different structures or formats. Pandas Interview Questions & Answers Question 1 – Define Python Pandas. Pandas refer to a software library explicitly written for Python, which is used to analyze and manipulate data. Pandas is an open-source, cross-platform library created by Wes McKinney. It was released in 2008 and provided data structures and operations to manipulate numerical and time-series data. Pandas can be installed using pip or Anaconda distribution. Pandas make it very easy to perform machine learning operations on tabular data. Question 2 – What Are The Different Types Of Data Structures In Pandas? Panda library supports two major types of data structures, DataFrames and Series. Both these data structures are built on the top of NumPy. Series is a one dimensional and simplest data structure, while DataFrame is two dimensional. Another axis label known as the “Panel” is a 3-dimensional data structure and includes items such as major_axis and minor_axis. Source Question 3 – Explain Series In Pandas. Series is a one-dimensional array that can hold data values of any type (string, float, integer, python objects, etc.). It is the simplest type of data structure in Pandas; here, the data’s axis labels are called the index. Question 4 – Define Dataframe In Pandas. A DataFrame is a 2-dimensional array in which data is aligned in a tabular form with rows and columns. With this structure, you can perform an arithmetic operation on rows and columns. Our learners also read: Free online python course for beginners! Question 5 – How Can You Create An Empty Dataframe In Pandas? To create an empty DataFrame in Pandas, type import pandas as pd ab = pd.DataFrame() Also read: Free data structures and algorithm course! Question 6 – What Are The Most Important Features Of The Pandas Library? Important features of the panda’s library are: Data Alignment Merge and join Memory Efficient Time series Reshaping Read: Dataframe in Apache PySpark: Comprehensive Tutorial Question 7 – How Will You Explain Reindexing In Pandas? To reindex means to modify the data to match a particular set of labels along a particular axis. Various operations can be achieved using indexing, such as- Insert missing value (NA) markers in label locations where no data for the label existed. Reorder the existing set of data to match a new set of labels. upGrad’s Exclusive Data Science Webinar for you – How to Build Digital & Data Mindset document.createElement('video'); https://cdn.upgrad.com/blog/webinar-on-building-digital-and-data-mindset.mp4 Question 8 – What are the different ways of creating DataFrame in pandas? Explain with examples. DataFrame can be created using Lists or Dict of nd arrays. Example 1 – Creating a DataFrame using List import pandas as pd     # a list of strings     Strlist = [‘Pandas’, ‘NumPy’]     # Calling DataFrame constructor on the list     list = pd.DataFrame(Strlist)     print(list)    Must read: Learn excel online free! Example 2 – Creating a DataFrame using dict of arrays import pandas as pd     list = {‘ID’: [1001, 1002, 1003],’Department’:[‘Science’, ‘Commerce’, ‘Arts’,]}     list = pd.DataFrame(list)     print (list)    Check out: Data Science Interview Questions Question 9 – Explain Categorical Data In Pandas? Categorical data refers to real-time data that can be repetitive; for instance, data values under categories such as country, gender, codes will always be repetitive. Categorical values in pandas can also take only a limited and fixed number of possible values.  Numerical operations cannot be performed on such data. All values of categorical data in pandas are either in categories or np.nan. This data type can be useful in the following cases: If a string variable contains only a few different values, converting it into a categorical variable can save some memory. It is useful as a signal to other Python libraries because this column must be treated as a categorical variable. A lexical order can be converted to a categorical order to be sorted correctly, like a logical order. 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 Question 10 – Create A Series Using Dict In Pandas. import pandas as pd     import numpy as np     ser = {‘a’ : 1, ‘b’ : 2, ‘c’ : 3}     ans = pd.Series(ser)     print (ans)    Question 11 – How To Create A Copy Of The Series In Pandas? To create a copy of the series in pandas, the following syntax is used: pandas.Series.copy Series.copy(deep=True) * if the value of deep is set to false, it will neither copy data nor the indices. Question 12 – How Will You Add An Index, Row, Or Column To A Dataframe In Pandas? To add rows to a DataFrame, we can use .loc (), .iloc () and .ix(). The .loc () is label based, .iloc() is integer based and .ix() is booth label and integer based. To add columns to the DataFrame, we can again use .loc () or .iloc (). Question 13 – What Method Will You Use To Rename The Index Or Columns Of Pandas Dataframe? .rename method can be used to rename columns or index values of DataFrame Question 14 – How Can You Iterate Over Dataframe In Pandas? To iterate over DataFrame in pandas for loop can be used in combination with an iterrows () call. 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 15 – What Is Pandas Numpy Array? Numerical Python (NumPy) is defined as an inbuilt package in python to perform numerical computations and processing of multidimensional and single-dimensional array elements.  NumPy array calculates faster as compared to other Python arrays. Question 16 – How Can A Dataframe Be Converted To An Excel File? To convert a single object to an excel file, we can simply specify the target file’s name. However, to convert multiple sheets, we need to create an ExcelWriter object along with the target filename and specify the sheet we wish to export. Question 17 – What Is Groupby Function In Pandas? In Pandas, groupby () function allows the programmers to rearrange data by using them on real-world sets. The primary task of the function is to split the data into various groups. Also Read: Top 15 Python AI & Machine Learning Open Source Projects Frequently Asked Python Pandas Interview Questions For Experienced Candidates Till now, we have looked at some of the basic pandas questions that you can expect in an interview. If you are looking for some more advanced pandas interview questions for the experienced, then refer to the list below. Seek reference from these questions and curate your own pandas interview questions and answers pdf. 1. What do we mean by data aggregation? One of the most popular numpy and pandas interview questions that are frequently asked in interviews is this one. The main goal of data aggregation is to add some aggregation in one or more columns. It does so by using the following Sum- It is specifically used when you want to return the sum of values for the requested axis. Min-This is used to return the minimum values for the requested axis. Max- Contrary to min, Max is used to return a maximum value for the requested axis.  2. What do we mean by Pandas index?  Yet another frequently asked pandas interview bit python question is what do we mean by pandas index. Well, you can answer the same in the following manner. Pandas index basically refers to the technique of selecting particular rows and columns of data from a data frame. Also known as subset selection, you can either select all the rows and some of the columns, or some rows and all of the columns. It also allows you to select only some of the rows and columns. There are mainly four types of multi-axes indexing, supported by Pandas. They are  Dataframe.[ ] Dataframe.loc[ ] Dataframe.iloc[ ] Dataframe.ix[ ] 3. What do we mean by Multiple Indexing? Multiple indexing is often referred to as essential indexing since it allows you to deal with data analysis and analysis, especially when you are working with high-dimensional data. Furthermore, with the help of this, you can also store and manipulate data with an arbitrary number of dimensions.  These are some of the most common python pandas interview questions that you can expect in an interview. Therefore, it is important that you clear all your doubts regarding the same for a successful interview experience. Incorporate these questions in your pandas interview questions and answers pdf to get started on your interview preparation! 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 Conclusion We hope the above-mentioned Pandas interview questions and 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 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.

In the world of computer science, data structure refers to the format that contains a collection of data values, their relationships, and the functions that can be applied to the data. Data structures arrange data so that it can be accessed and worked on with specific algorithms more effectively. In this article, we will list some useful dsa project ideas to help you learn, create, and innovate! You can also check out our free courses offered by upGrad under machine learning and IT technology. Data Structure Basics Data structures can be classified into the following basic types: Arrays Linked Lists Stacks Queues Trees Hash tables Graphs Selecting the appropriate setting for your data is an integral part of the programming and problem-solving process. And you can observe that data structures organize abstract data types in concrete implementations. To attain that result, they make use of various algorithms, such as sorting, searching, etc. Learning data structures is one of the important parts in data science courses. With the rise of big data and analytics, learning about these fundamentals has become almost essential for data scientists. The training typically incorporates various topics in data structure to enable the synthesis of knowledge from real-life experiences. Here is a list of dsa topics to get you started! Check out our Python Bootcamp created for working professionals. Benefits of Data structures: Data structures are fundamental building blocks in computer science and programming. They are important tools that helps inorganizing, storing, and manipulating data efficiently. On top of that it provide a way to represent and manage information in a structured manner, which is essential for designing efficient algorithms and solving complex problems. So, let’s explore the numerous benefits of Data Structures and dsa topics list in the below post: – 1. Efficient Data Access Data structures enable efficient access to data elements. Arrays, for example, provide constant-time access to elements using an index. Linked lists allow for efficient traversal and modification of data elements. Efficient data access is crucial for improving the overall performance of algorithms and applications. 2. Memory Management Data structures help manage memory efficiently. They helps in allocating and deallocating memory resources as per requirement, reducing memory wastage and fragmentation. Remember, proper memory management is important for preventing memory leaks and optimizing resource utilization. 3. Organization of Data Data structures offers a structured way to organize and store data. For example, a stack organizes data in a last-in, first-out (LIFO) fashion, while a queue uses a first-in, first-out (FIFO) approach. These organizations make it easier to model and solve specific problems efficiently. 4. Search and Retrieval Efficient data search and retrieval are an important aspect in varied applications, like, databases and information retrieval systems. Data structures like binary search trees and hash tables enable fast lookup and retrieval of data, reducing the time complexity of search operations. 5. Sorting Sorting is a fundamental operation in computer science. Data structures like arrays and trees can implement various sorting algorithms. Efficient sorting is crucial for maintaining ordered data lists and searching for specific elements. 6. Dynamic Memory Allocation Many programming languages and applications require dynamic memory allocation. Data structures like dynamic arrays and linked lists can grow or shrink dynamically, allowing for efficient memory management in response to changing data requirements. 7. Data Aggregation Data structures can aggregate data elements into larger, more complex structures. For example, arrays and lists can create matrices and graphs, enabling the representation and manipulation of intricate data relationships. 8. Modularity and Reusability Data structures promote modularity and reusability in software development. Well-designed data structures can be used as building blocks for various applications, reducing code duplication and improving maintainability. 9. Complex Problem Solving Data structures play a crucial role in solving complex computational problems. Algorithms often rely on specific data structures tailored to the problem’s requirements. For instance, graph algorithms use data structures like adjacency matrices or linked lists to represent and traverse graphs efficiently. 10. Resource Efficiency Selecting the right data structure for a particular task can impact the efficiency of an application. Regards to this, Data structures helps in minimizing resource usage, such as time and memory, leading to faster and more responsive software. 11. Scalability Scalability is a critical consideration in modern software development. Data structures that efficiently handle large datasets and adapt to changing workloads are essential for building scalable applications and systems. 12. Algorithm Optimization Algorithms that use appropriate data structures can be optimized for speed and efficiency. For example, by choosing a hash table data structure, you can achieve constant-time average-case lookup operations, improving the performance of algorithms relying on data retrieval. 13. Code Readability and Maintainability Well-defined data structures contribute to code readability and maintainability. They provide clear abstractions for data manipulation, making it easier for developers to understand, maintain, and extend code over time. 14. Cross-Disciplinary Applications Data structures are not limited to computer science; they find applications in various fields, such as biology, engineering, and finance. Efficient data organization and manipulation are essential in scientific research and data analysis. Other benefits: It can store variables of various data types. It allows the creation of objects that feature various types of attributes. It allows reusing the data layout across programs. It can implement other data structures like stacks, linked lists, trees, graphs, queues, etc. Why study data structures & algorithms? They help to solve complex real-time problems. They improve analytical and problem-solving skills. They help you to crack technical interviews. Topics in data structure can efficiently manipulate the data. Studying relevant DSA topics increases job opportunities and earning potential. Therefore, they guarantee career advancement. Data Structures Project Ideas 1. Obscure binary search trees Items, such as names, numbers, etc. can be stored in memory in a sorted order called binary search trees or BSTs. And some of these data structures can automatically balance their height when arbitrary items are inserted or deleted. Therefore, they are known as self-balancing BSTs. Further, there can be different implementations of this type, like the BTrees, AVL trees, and red-black trees. But there are many other lesser-known executions that you can learn about. Some examples include AA trees, 2-3 trees, splay trees, scapegoat trees, and treaps.  You can base your project on these alternatives and explore how they can outperform other widely-used BSTs in different scenarios. For instance, splay trees can prove faster than red-black trees under the conditions of serious temporal locality.  Also, check out our business analytics course to widen your horizon. 2. BSTs following the memoization algorithm Memoization related to dynamic programming. In reduction-memoizing BSTs, each node can memoize a function of its subtrees. Consider the example of a BST of persons ordered by their ages. Now, let the child nodes store the maximum income of each individual. With this structure, you can answer queries like, “What is the maximum income of people aged between 18.3 and 25.3?” It can also handle updates in logarithmic time.  Moreover, such data structures are easy to accomplish in C language. You can also attempt to bind it with Ruby and a convenient API. Go for an interface that allows you to specify ‘lambda’ as your ordering function and your subtree memoizing function. All in all, you can expect reduction-memoizing BSTs to be self-balancing BSTs with a dash of additional book-keeping.  Dynamic coding will need cognitive memorisation for its implementation. Each vertex in a reducing BST can memorise its sub–trees’ functionality. For example, a BST of persons is categorised by their age. This DSA topics based project idea allows the kid node to store every individual’s maximum salary. This framework can be used to answer the questions like “what’s the income limit of persons aged 25 to 30?” Checkout: Types of Binary Tree 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 3. Heap insertion time When looking for data structure projects, you want to encounter distinct problems being solved with creative approaches. One such unique research question concerns the average case insertion time for binary heap data structures. According to some online sources, it is constant time, while others imply that it is log(n) time.  But Bollobas and Simon give a numerically-backed answer in their paper entitled, “Repeated random insertion into a priority queue.” First, they assume a scenario where you want to insert n elements into an empty heap. There can be ‘n!’ possible orders for the same. Then, they adopt the average cost approach to prove that the insertion time is bound by a constant of 1.7645. When looking for Data Structures tasks in this project idea, you will face challenges that are addressed using novel methods. One of the interesting research subjects is the mean response insertion time for the sequential heap DS. Inserting ‘n’ components into an empty heap will yield ‘n!’ arrangements which you can use in suitable DSA projects in C++. Subsequently, you can implement the estimated cost approach to specify that the inserting period is limited by a fixed constant. Our learners also read: Excel online course free! 4. Optimal treaps with priority-changing parameters Treaps are a combination of BSTs and heaps. These randomized data structures involve assigning specific priorities to the nodes. You can go for a project that optimizes a set of parameters under different settings. For instance, you can set higher preferences for nodes that are accessed more frequently than others. Here, each access will set off a two-fold process: Choosing a random number Replacing the node’s priority with that number if it is found to be higher than the previous priority As a result of this modification, the tree will lose its random shape. It is likely that the frequently-accessed nodes would now be near the tree’s root, hence delivering faster searches. So, experiment with this data structure and try to base your argument on evidence.  Also read: Python online course free! At the end of the project, you can either make an original discovery or even conclude that changing the priority of the node does not deliver much speed. It will be a relevant and useful exercise, nevertheless. Constructing a heap involves building an ordered binary tree and letting it fulfill the “heap” property. But if it is done using a single element, it would appear like a line. This is because in the BST, the right child should be greater or equal to its parent, and the left child should be less than its parent. However, for a heap, every parent must either be all larger or all smaller than its children. The numbers show the data structure’s heap arrangement (organized in max-heap order). The alphabets show the tree portion. Now comes the time to use the unique property of treap data structure in DSA projects in C++. This treap has only one arrangement irrespective of the order by which the elements were chosen to build the tree. You can use a random heap weight to make the second key more useful. Hence, now the tree’s structure will completely depend on the randomized weight offered to the heap values. In the file structure mini project topics, we obtain randomized heap priorities by ascertaining that you assign these randomly. 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 upGrad’s Exclusive Data Science Webinar for you – Transformation & Opportunities in Analytics & Insights document.createElement('video'); https://cdn.upgrad.com/blog/jai-kapoor.mp4 5. Research project on k-d trees K-dimensional trees or k-d trees organize and represent spatial data. These data structures have several applications, particularly in multi-dimensional key searches like nearest neighbor and range searches. Here is how k-d trees operate: Every leaf node of the binary tree is a k-dimensional point Every non-leaf node splits the hyperplane (which is perpendicular to that dimension) into two half-spaces The left subtree of a particular node represents the points to the left of the hyperplane. Similarly, the right subtree of that node denotes the points in the right half. You can probe one step further and construct a self-balanced k-d tree where each leaf node would have the same distance from the root. Also, you can test it to find whether such balanced trees would prove optimal for a particular kind of application.  Also, visit upGrad’s Degree Counselling page for all undergraduate and postgraduate programs. 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? With this, we have covered five interesting ideas that you can study, investigate, and try out. Now, let us look at some more projects on data structures and algorithms.  Read : Data Scientist Salary in India 6. Knight’s travails In this project, we will understand two algorithms in action – BFS and DFS. BFS stands for Breadth-First Search and utilizes the Queue data structure to find the shortest path. Whereas, DFS refers to Depth-First Search and traverses Stack data structures.  For starters, you will need a data structure similar to binary trees. Now, suppose that you have a standard 8 X 8 chessboard, and you want to show the knight’s movements in a game. As you may know, a knight’s basic move in chess is two forward steps and one sidestep. Facing in any direction and given enough turns, it can move from any square on the board to any other square.  If you want to know the simplest way your knight can move from one square (or node) to another in a two-dimensional setup, you will first have to build a function like the one below. knight_plays([0,0], [1,2]) == [[0,0], [1,2]] knight_plays([0,0], [3,3]) == [[0,0], [1,2], [3,3]] knight_plays([3,3], [0,0]) == [[3,3], [1,2], [0,0]]  Furthermore, this project would require the following tasks:  Creating a script for a board game and a night Treating all possible moves of the knight as children in the tree structure Ensuring that any move does not go off the board Choosing a search algorithm for finding the shortest path in this case Applying the appropriate search algorithm to find the best possible move from the starting square to the ending square. 7. Fast data structures in non-C systems languages Programmers usually build programs quickly using high-level languages like Ruby or Python but implement data structures in C/C++. And they create a binding code to connect the elements. However, the C language is believed to be error-prone, which can also cause security issues. Herein lies an exciting project idea.  You can implement a data structure in a modern low-level language such as Rust or Go, and then bind your code to the high-level language. With this project, you can try something new and also figure out how bindings work. If your effort is successful, you can even inspire others to do a similar exercise in the future and drive better performance-orientation of data structures.   Also read: Data Science Project Ideas for Beginners 8. Search engine for data structures The software aims to automate and speed up the choice of data structures for a given API. This project not only demonstrates novel ways of representing different data structures but also optimizes a set of functions to equip inference on them. We have compiled its summary below. The data structure search engine project requires knowledge about data structures and the relationships between different methods. It computes the time taken by each possible composite data structure for all the methods. Finally, it selects the best data structures for a particular case.  Read: Data Mining Project Ideas 9. Phone directory application using doubly-linked lists This project can demonstrate the working of contact book applications and also teach you about data structures like arrays, linked lists, stacks, and queues. Typically, phone book management encompasses searching, sorting, and deleting operations. A distinctive feature of the search queries here is that the user sees suggestions from the contact list after entering each character. You can read the source-code of freely available projects and replicate the same to develop your skills.  This project demonstrates how to address the book programs’ function. It also teaches you about queuing, stacking, linking lists, and arrays. Usually, this project’s directory includes certain actions like categorising, scanning, and removing. Subsequently, the client shows recommendations from the address book after typing each character. This is the web searches’ unique facet. You can inspect the code of extensively used DSA projects in C++ and applications and ultimately duplicate them. This helps you to advance your data science career. 10. Spatial indexing with quadtrees The quadtree data structure is a special type of tree structure, which can recursively divide a flat 2-D space into four quadrants. Each hierarchical node in this tree structure has either zero or four children. It can be used for various purposes like sparse data storage, image processing, and spatial indexing.  Spatial indexing is all about the efficient execution of select geometric queries, forming an essential part of geo-spatial application design. For example, ride-sharing applications like Ola and Uber process geo-queries to track the location of cabs and provide updates to users. Facebook’s Nearby Friends feature also has similar functionality. Here, the associated meta-data is stored in the form of tables, and a spatial index is created separately with the object coordinates. The problem objective is to find the nearest point to a given one.  You can pursue quadtree data structure projects in a wide range of fields, from mapping, urban planning, and transportation planning to disaster management and mitigation. We have provided a brief outline to fuel your problem-solving and analytical skills.  QuadTrees are techniques for indexing spatial data. The root node signifies the whole area and every internal node signifies an area called a quadrant which is obtained by dividing the area enclosed into half across both axes. These basics are important to understand QuadTrees-related data structures topics. Objective: Creating a data structure that enables the following operations Insert a location or geometric space Search for the coordinates of a specific location Count the number of locations in the data structure in a particular contiguous area One of the leading applications of QuadTrees in the data structure is finding the nearest neighbor. For example, you are dealing with several points in a space in one of the data structures topics. Suppose somebody asks you what’s the nearest point to an arbitrary point. You can search in a quadtree to answer this question. If there is no nearest neighbor, you can specify that there is no point in this quadrant to be the nearest neighbor to an arbitrary point. Consequently, you can save time otherwise spent on comparisons. Spatial indexing with Quadtrees is also used in image compression wherein every node holds the average color of each child. You get a more detailed image if you dive deeper into the tree. This project idea is also used in searching for the nods in a 2D area. For example, you can use quadtrees to find the nearest point to the given coordinates. Follow these steps to build a quadtree from a two-dimensional area: Divide the existing two-dimensional space into four boxes. Create a child object if a box holds one or more points within.  This object stores the box’s 2D space. Don’t create a child for a box that doesn’t include any points. Repeat these steps for each of the children. You can follow these steps while working on one of the file structure mini project topics. 11. Graph-based projects on data structures You can take up a project on topological sorting of a graph. For this, you will need prior knowledge of the DFS algorithm. Here is the primary difference between the two approaches: We print a vertex & then recursively call the algorithm for adjacent vertices in DFS. In topological sorting, we recursively first call the algorithm for adjacent vertices. And then, we push the content into a stack for printing.  Therefore, the topological sort algorithm takes a directed acyclic graph or DAG to return an array of nodes.  Let us consider the simple example of ordering a pancake recipe. To make pancakes, you need a specific set of ingredients, such as eggs, milk, flour or pancake mix, oil, syrup, etc. This information, along with the quantity and portions, can be easily represented in a graph. But it is equally important to know the precise order of using these ingredients. This is where you can implement topological ordering. Other examples include making precedence charts for optimizing database queries and schedules for software projects. Here is an overview of the process for your reference: Call the DFS algorithm for the graph data structure to compute the finish times for the vertices Store the vertices in a list with a descending finish time order  Execute the topological sort to return the ordered list  12. Numerical representations with random access lists In the representations we have seen in the past, numerical elements are generally held in Binomial Heaps. But these patterns can also be implemented in other data structures. Okasaki has come up with a numerical representation technique using binary random access lists. These lists have many advantages: They enable insertion at and removal from the beginning They allow access and update at a particular index Know more: The Six Most Commonly Used Data Structures in R 13. Stack-based text editor Your regular text editor has the functionality of editing and storing text while it is being written or edited. So, there are multiple changes in the cursor position. To achieve high efficiency, we require a fast data structure for insertion and modification. And the ordinary character arrays take time for storing strings.  You can experiment with other data structures like gap buffers and ropes to solve these issues. Your end objective will be to attain faster concatenation than the usual strings by occupying smaller contiguous memory space.  This project idea handles text manipulation and offers suitable features to improve the experience. The key functionalities of text editors include deleting, inserting, and viewing text. Other features needed to compare with other text editors are copy/cut and paste, find and replace, sentence highlighting, text formatting, etc. This project idea’s functioning depends on the data structures you determined to use for your operations. You will face tradeoffs when choosing among the data structures. This is because you must consider the implementation difficulty for the memory and performance tradeoffs. You can use this project idea in different file structure mini project topics to accelerate the text’s insertion and modification. Conclusion Data structure skills form the bedrock of software development, particularly when it comes to managing large sets of data in today’s digital ecosystem. Leading companies like Adobe, Amazon, and Google hire for various lucrative job positions in the data structure and algorithm domain. And in interviews, recruiters test not only your theoretical knowledge but also your practical skills. So, practice the above data structure projects to get your foot in the door! 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 more about Business Analytics Free Online Course with Certification. Why Learn Business Analytics? Getting a competitive edge with business analytics certification Benefits of free business analyst courses for beginners Skills you will Learn from free business analyst courses Why choose the business analytics starter pack from upGrad? What Will You Learn? Read more to know each in detail. Wondering how to become a business analyst in India? What if we told you that you could learn business analytics free and enter this sector? With our new upStart program, it is now possible to learn business analytics online for free. All you have to do is go to our upStart page, select the course you want to study, and register.  In this article, we’ll talk about the benefits of learning business analytics course online. This course can help you understand this skill, and what its contents are. Let’s get started. 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The free business analyst certification for beginners includes Agile Business Analysis which helps business analysts efficiently perform their roles. Why Choose Business Analytics Starter Pack from upGrad? You’re sure about becoming a business analyst but wonder whether you should learn a business analytics course online free through upGrad. So here are some reasons why you should learn business analytics for free through upGrad. Earn an MBA from one of the Top 5 Institutes in India, without leaving your Job. 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 1 to 1 Industry Mentorship You get to learn exclusively from a leading industry expert. You get dedicated attention, which helps in learning quickly and adequately. Cutting Edge Content upGrad creates some of the best online courses in partnership with premier institutes. The detailed and professionally curated content will enhance your learning experience substantially.   Weekly Live Lectures You’ll get live lectures per week during the duration of the course. The instructor will explain every topic in detail, and you’d be free to ask questions or share doubts.  Free Certificate After the completion of the business analytics free course, you’ll earn a certificate. It will enhance your resume considerably.  What Will You Learn? upGrad’s business analytics free course will help you learn all the necessary skills you need to get started in this field. Business analytics is a complicated sector that deters many people from entering it. Our course will give you all the tools you need to simplify this sector for yourself.  The course material is suitable for students and professionals who want to learn about business analytics. It covers the fundamentals of this field so you can make progress comfortably. The course lasts for six weeks, and you would only have to invest 30 minutes every day during this period.  It has the following sections: Jargon Busting Understanding the Business Problem and Formulating Hypothesis Anatomy of Decisions Data Analysis in Excel Analyzing Patterns and Storytelling EDA (Excel) This structure helps you learn all the necessary concepts step-by-step while removing your doubts. Let’s discuss each of these sections in detail: Must 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? Jargon Busting The first section of the business analyst course online will cover the technical terms business analysts use regularly. A common reason why people deter from pursuing a career in this field is the number of technical terms present in it. The jargon seems scary to many because it restricts their ability to understand what is actually being said in the study material. The terminology makes it difficult for new students to understand the concepts. That’s why we begin our business analytics free online courses by demystifying the jargon. This section will make you familiar with the vocabulary of the data science field. It would help you in understanding the meaning behind the various technical terms.  Read our Other Articles Related to Business Analytics What is Business Analytics? Career, Salary & Job Roles Top 7 Career Options in Business Analytics Business Analytics Free Online Course with Certification Business Analytics Vs Data Analytics: Difference Between Business Analytics and Data Analytics Top 7 Best Business Analytics Tools Recommended for every Business Analyst Top 11 Industry Applications of Business Analytics Future Scope of Business Analytics Business Analytics Eligibility or Requirement 8 Business Analytics Tips: Which Helps to Run Business Successfully Our learners also read: Learn Python Online Course Free  Understanding the Business Problem & Hypothesis A business problem is a short or long challenge a business faces. They might be preventing the company from achieving a particular goal or implementing a plan. They can even threaten the long term existence of the enterprise.  Business analysts’ primary task is to identify the issues their organization is facing and propose solutions accordingly. So, to become a business analyst, you should know how to spot business problems and create hypotheses for them. A hypothesis is your proposed explanation for a specific occurrence. A hypothesis doesn’t have a predetermined outcome.  Our course teaches you about what a business problem is, which is one of the building blocks of business analytics. By understanding the concept of business problems, you’d find it easier to identify them in real life and formulate solutions for them. The course teaches you the stepwise process of recognizing and understanding a business problem and the process of building a hypothesis around that issue.   Explore our Business Analytics Programs from World's Top Universities Business Analytics Certification Programme Busines Analytics EPGP - LIBA Professional Certificate Program in Data Science and Business Analytics Master of Science in Business Analytics Global Master Certificate in Business Analytics Busines Analytics Certification - upGrad Anatomy of Decisions Decision-making is a highly complicated subject. It is among the major sections of business analytics too. There are multiple steps in a business decision: collecting information, analyzing the data, identifying or developing alternatives, choosing an alternative, implementation, and following up on the same.  This section will explore all of these steps of business decision-making. You’ll learn how you can utilize analytics and its tools and concepts to make better-informed decisions. The section will give you use cases of business problems where data science has been successful (and where it hasn’t) to help you understand the use of business analytics in decision making.  Data Analysis in Excel Microsoft Excel is a spreadsheet tool from Microsoft for macOS, Windows, iOS, and Android. It has many tools and features that allow data handling and performing operations such as pivot tables, calculation, graphing methods, etc. For data science professionals, MS Excel is a must-have.  You can use Excel to perform many business analysis operations, and this section will help you learn about the same. This section will explore different aspects of Excel you’d be using as a business analyst, from simple keyboard shortcuts to dedicated tools. Moreover, MS Excel boasts of having dedicated functions for performing analysis of available data. This section of our course will cover the basics of using Excel in business analysis. We’ll discuss the more advanced MS Excel implementations and its uses in business analysis in a later section of our business analytics free course.  Analyzing Patterns and Storytelling In this section, you’ll get to learn how business analysts recognize patterns in their data to analyze the same. Identifying patterns and generating insights from them is one of the vital sections of business analytics. However, pattern recognition and insight generation aren’t enough. You might have found something spectacular, but if you can’t convey it correctly, your effort would go in vain. That’s why we’ll also explore storytelling in our business analytics free online courses. Mastering storytelling takes effort, and this section of our course will help you learn this vital skill. You’d know how to convey your thoughts and information in an exciting and useful manner.  EDA (Excel) It’s the final section of our business analyst course online, and it will cover another prominent area of data analysis, EDA. EDA stands for exploratory data analysis, and it focuses on analysing data to summarise its primary highlights through visual representations. You’d learn how to use MS Excel to analyze, summarize, and display your findings through the right representations. Graphical representations are vital for business analytics professionals because they help them share their insights with non-technical audiences.  Also Read: Career Options in Business Analytics Roles and Responsibilities of Business Analysts A business analyst course can teach you how to fulfill the following roles and responsibilities with precision: Analyzing Data A business analyst needs to analyze to understand the latest market trends and explore the best opportunities. An online free business analytics course teaches you how to leverage different tools and software like SQL and Excel for analyzing data. The analyzed data is applied in different places to perform a wide range of functions. Business analysts also collect and analyze data to look for areas of improvement and inform senior management about them.  Conducting Research A business analyst course free with a certificate also sheds light on how to perform research efficiently. Research skills are essential for studying the market and gathering crucial insights about it. Performing efficient research is also efficient for evaluating current market conditions and finding the best business opportunities.  Collaborating with Others A business analyst course free will teach you how to collaborate and communicate with different stakeholders. It will help ensure that everyone involved in the project understands the timeline and requirements. Business analysts are also responsible for conveying crucial information to stakeholders.  Understanding Requirements A business analyst needs to understand the goals and requirements of a project before starting it. A business analytics free course will teach you how to create a list of clear requirements. Additionally, business analysts need to ensure that the requirements of a project align with the goals and standards of a company. Performing Tests Business analysts are responsible for ensuring that a project is working seamlessly. A business analytics course free will teach how to ensure every step in a project aligns with industry norms. With this knowledge, business analysts should be able to test every step involved in the project and determine its current status. Moreover, these professionals need to prepare reports to showcase whether the project is delivering the desired outcomes.   Creating Plans One of the major duties of a business analyst is to create effective plans. A free business analyst certification teaches how to develop realistic plans to achieve the desired outcome. With professional training, business analysts learn how to consider different factors like project requirements and timelines while creating a plan.  Top Career Paths After a Business Analyst Course A business analyst free course will impart a wide range of skills that can help pursue the following job roles: Quantitative Analyst These professionals are responsible for evaluating financial markets and investment opportunities for companies. The tasks of a quantitative analyst revolve around data analysis, market research, financial modeling, risk management, and more. Quantitative analysts require a strong knowledge of statistical and mathematical methods along with programming skills.  IT Analyst A business analyst free certification course can also prepare you for the career of an IT analyst. These professionals help bridge the gap between the IT and business departments of an organization. The major duties of an IT analyst include solution designing, project management, and collecting data for project requirements.  Data Analyst You can also pursue the role of a data analyst after gaining a business analyst certification free. Data analysts collect data from different sources and inspect them for valuable insights. The primary duty of a data analyst is to transform large data sets into easily digestible information.  How to Start To learn a business analyst course online free, you only have to go to our upStart page, select our business analytics course, and register. The process is quick and easy to follow. Once you register, you can start your learning journey through upGrad. By investing mere 30 minutes every day for a few weeks, you’d have developed a new professional skill.  Sign up here today, and start learning.  If you have any questions or doubts, feel free to contact us by dropping a comment below or through the Contact Us page. We’d love to help you out.  If you are interested to learn more about business analytics, upGrad’s Global Master Certificate in Business Analytics from Michigan State University with separate courses on “Data Mining and Management Strategies” and “Applying Business Analytics” can help you enhance your business performance and drive growth. Since the course is conducted 100% online, it doesn’t require you to compromise focus on business operations. What are business analyst courses used for? A business analyst analyses a problem or a new step that a company or domain will take. They evaluate and document all possible outcomes, enabling companies to make valuable decisions. This, as a result, raises the effectiveness of management decisions.  How will the business analyst course assist you? Candidates who have successfully completed a business analyst course online will undoubtedly have an advantage over their competitors. It teaches excellent problem-solving abilities that can be applied to diverse business-related obstacles. If you are already a business analyst, several variations of the course will help you advance in your career. Is it preferable to take the business analyst course online? A business analyst course can be taken online or in person. If there are no nearby offline courses, online courses can be pursued with a flexible schedule. An online course is also preferable to other courses because it can be completed from the convenience of one’s workplace or home. What qualifications are required for the position of a business analyst? An undergraduate degree in computer science, business information systems, computing and systems development, or business management is required to apply for free business analyst courses for beginners. Is it simple to learn business analytics? Technically, a business analytics course online is not particularly difficult. You could teach it to almost anyone with a basic understanding of mathematics. Surfing through the detailed datasets created by data scientists, business analytics requires candidates to simply apply their learned skills to reaping valuable insights while keeping several factors in mind, which makes it a fairly manageable job role.  Are Online Business Courses Beneficial? A business analyst course online is the next best thing if you don’t have the time or money to pursue a graduate degree in business. After all, taking an online course from a well-known business school does not necessitate weeks or months of preparation for a standardized test. You can do it without quitting your job or making significant time sacrifices from your family. And it

Data Engineering Projects & Topics Data engineering is among the core branches of big data. If you’re studying to become a data engineer and want some projects to showcase your skills (or gain knowledge), you’ve come to the right place. In this article, we’ll discuss data engineering project ideas you can work on and several data engineering projects, and you should be aware of it. No Coding Experience Required. 360° Career support. PG Diploma in Machine Learning & AI from IIIT-B and upGrad.   You should note that you should be familiar with some topics and technologies before you work on these projects. Companies are always on the lookout for skilled data engineers who can develop innovative data engineering projects. So, if you are a beginner, the best thing you can do is work on some real-time data engineering projects. Working on a data engineering project will not only give you more insight into how data engineering works but will also strengthen your problem-solving skills when you encounter bugs inside the project and debug them yourself. We, here at upGrad, believe in a practical approach as theoretical knowledge alone won’t be of help in a real-time work environment. In this article, we will be exploring some interesting data engineering projects which beginners can work on to put their data engineering knowledge to test. In this article, you will find top data engineering projects for beginners to get hands-on experience. If you are a beginner and interested to learn more about data science, check out our data analytics courses from top universities. Amid the cut-throat competition, aspiring Developers must have hands-on experience with real-world data engineering projects. In fact, this is one of the primary recruitment criteria for most employers today. As you start working on data engineering projects, you will not only be able to test your strengths and weaknesses, but you will also gain exposure that can be immensely helpful to boost your career. That’s because you’ll need to complete the projects correctly. Here are the most important ones: Python and its use in big data Extract Transform Load (ETL) solutions Hadoop and related big data technologies Concept of data pipelines Apache Airflow Also Read: Big Data Project Ideas What is a Data Engineer? Data engineers make raw data usable and accessible to other data professionals. Organizations have multiple sorts of data, and it’s the responsibility of data engineers to make them consistent, so data analysts and scientists can use the same. If data scientists and analysts are pilots, then data engineers are the plane-builders. Without the latter, the former can’t perform its tasks. Data engineering topics have been word of mouth everywhere in the domain of data science, from an analyst to a Big Data Engineer. Data engineers play a pivotal role in the data ecosystem, acting as the architects and builders of infrastructure that enables data analysis and interpretation. Their expertise extends beyond data collection and storage, encompassing the intricate task of transforming raw, disparate data into a harmonized and usable format. By designing robust data pipelines, data engineers ensure that data scientists and analysts have a reliable and structured foundation to conduct their analyses. These professionals possess a deep understanding of data manipulation tools, database systems, and programming languages, allowing them to orchestrate the seamless flow of information across various platforms. They implement strategies to optimize data retrieval, processing, and storage, accounting for scalability and performance considerations. Moreover, data engineers work collaboratively with data scientists, analysts, and other stakeholders to comprehend data requirements and tailor solutions accordingly. Essentially, data engineers are the architects of the data landscape, laying the groundwork for actionable insights and informed decision-making. As the data realm continues to evolve, the role of data engineers remains indispensable, ensuring that data flows seamlessly, transforms meaningfully, and empowers organizations to unlock the true potential of their data-driven endeavors. Explore Our Software Development Free Courses Fundamentals of Cloud Computing JavaScript Basics from the scratch Data Structures and Algorithms Blockchain Technology React for Beginners Core Java Basics Java Node.js for Beginners Advanced JavaScript Skills you need to become a Data Engineer As a Data Engineer you have to work on raw data and perform certain tasks on the data. Some tasks of a data engineer are: Acquiring and sourcing data from multiple places Cleaning the data and get rid of useless data & errors Remove any duplicates present in the sourced data Transform the data into the required format To become a proficient data engineer, you need to acquire certain skills. Here’s a list and a bit about each skill that will help you become a better data engineer: Coding skills: Most data engineering jobs nowadays need candidates with strong coding skills. Numerous job postings stipulate a minimum requirement of applicants’ familiarity with a programming language, often one of the popular coding languages such as Scala, Perl, Python, Java, etc. DBMS: Engineers working with data should be well-versed in all things related to database administration. An in-depth understanding of Structured Query Language (SQL) is crucial in this profession since it is the most popular choice. SQL stands for Structured Query Language and retrieves and manipulates information in a database table. If you want to succeed as a data engineer, learning about Bigtable and other database systems is essential. Data Warehousing: Data engineers are responsible for managing and interpreting massive amounts of information. Consequently, it is essential for a data engineer to be conversant with and have expertise with data warehousing platforms like Redshift by AWS. Machine Learning: Machine learning is the study of how machines or computers may “learn” or use information gathered from previous attempts to improve their performance on a given task or collection of activities.Though data engineers do not directly work on creating or designing machine learning models. It is their job to create the architecture on which Data Scientists and Machine Learning Engineers apply their models. Hence, a knowledge of Machine Learning is essential for a Data Engineer. Operating Systems, Virtual Machines, Networking, etc. As the demand for big data is increasing, the need for data engineers is rising accordingly. Now that you know what a data engineer does, we can start discussing our data engineering projects.  Let’s start looking for data engineering projects to build your very own data projects! So, here are a few data engineering projects which beginners can work on: Data Engineering Projects You Should Know About To become a proficient data engineer, you should be aware of your sector’s latest and most popular tools. Working on a data engineer project will help you know the ins and outs of the industry. That’s why we’ll focus on the data engineering projects you should be mindful of: 1. Prefect Prefect is a data pipeline manager through which you can parametrize and build DAGs for tasks. It is new, quick, and easy-to-use, due to which it has become one of the most popular data pipeline tools in the industry. Prefect has an open-source framework where you can build and test workflows. The added facility of private infrastructure enhances its utility further because it eliminates many security risks a cloud-based infrastructure might pose.  Even though Prefect offers a private infrastructure for running the code, you can always monitor and check the work through their cloud. Prefect’s framework is based on Python, and even though it’s entirely new in the market, you’d benefit greatly from learning Prefect. Taking up a data engineering project on Prefect will be convenient for you due to the resources available on the internet, being an open-source framework. Explore our Popular Software Engineering Courses Master of Science in Computer Science from LJMU & IIITB Caltech CTME Cybersecurity Certificate Program Full Stack Development Bootcamp PG Program in Blockchain Executive PG Program in Full Stack Development View All our Courses Below Software Engineering Courses 2. Cadence Cadence is a fault-tolerant coding platform that gets rid of many complexities of building distributed applications. It secures the complete application state that allows you to program without worrying about the scalability, availability, and durability of your application. It has a framework as well as a backend service. Its structure supports multiple languages, including Java and Go. Cadence facilitates horizontal scaling along with a replication of past events. Such replication enables easy recovery from any sorts of zone failures. As you would’ve guessed by now, Cadence is undoubtedly a technology you should be familiar with as a data engineer. Using Cadence for a data engineer project will automate a lot of mundane tasks that you would otherwise need to perform to build your own data engineer project from scratch. 3. Amundsen Amundsen is a product of Lyft and is a metadata and data discovery solution. Amundsen offers multiple services to users that make it a worthy addition to any data engineer’s arsenal. The metadata service, for example, takes care of the metadata requests of the front-end. Similarly, it has a framework called data builder to extract metadata from the required sources. Other prominent components of this solution are the search service, the library repository named Common, and the front-end service, which runs the Amundsen web app.  4. Great Expectations Great Expectations is a Python library that lets you validate and define rules for datasets. After determining the rules, validating data sets becomes easy and efficient. Moreover, you can use Great Expectations with Pandas, Spark, and SQL. It has data profilers that can produce automated expectations, along with clean documentation for HTML data. While it’s relatively new, it is certainly gaining popularity among data professionals. Great Expectations automates the verification process for new data you receive from other parties (teams and vendors). It saves a lot of time in data cleaning, which can be a very exhaustive process for any data engineer.  Must Read: Data Mining Project Ideas In-Demand Software Development Skills JavaScript Courses Core Java Courses Data Structures Courses Node.js Courses SQL Courses Full stack development Courses NFT Courses DevOps Courses Big Data Courses React.js Courses Cyber Security Courses Cloud Computing Courses Database Design Courses Python Courses Cryptocurrency Courses Data Engineering Project Ideas You can Work on This list of data engineering projects for students is suited for beginners, intermediates & experts. These data engineering projects will get you going with all the practicalities you need to succeed in your career. Further, if you’re looking for data engineering projects for final year, this list should get you going. If you are keen on data engineering and want to write your final year thesis on data engineering topics, then you should definitely start looking up data engineering research topics online without any delay. So, without further ado, let’s jump straight into some data engineering projects that will strengthen your base and allow you to climb up the ladder. Here are some data engineering project ideas that should help you take a step forward in the right direction and strengthen your profile as a project data engineer. 1. Build a Data Warehouse One of the best ideas to start experimenting you hands-on data engineering projects for students is building a data warehouse. Data warehousing is among the most popular skills for data engineers. That’s why we recommend building a data warehouse as a part of your data engineering projects. This project will help you understand how you can create a data warehouse and its applications. A data warehouse collects data from multiple sources (that are heterogeneous) and transforms it into a standard, usable format. Data warehousing is a vital component of Business Intelligence (BI) and helps in using data strategically. Other common names for data warehouses are: Analytic Application Decision Support System Management Information System Data warehouses are capable of storing large quantities of data and primarily help business analysts with their tasks. You can build a data warehouse on the AWS cloud and add an ETL pipeline to transfer and transform the data into the warehouse. Once you’ve completed this project, you’d be familiar with nearly all aspects of data warehousing.  2. Perform Data Modeling for a Streaming Platform One of the best ideas to start experimenting you hands-on data engineering projects for students is performing data modeling. In this project, a streaming platform (such as Spotify or Gaana) wants to analyze its user’s listening preferences to enhance their recommendation system. As the data engineer, you have to perform data modeling so they can explain their user data adequately. You’ll have to create an ETL pipeline with Python and PostgreSQL. Data modeling refers to developing comprehensive diagrams that display the relationship between different data points.  Some of the user points you would have to work with would be: The albums and songs the user has liked The playlists present in the user’s library The genres the user listens to the most How long the user listens to a particular song and its timestamp Such information would help you model the data correctly and provide an effective solution to the platform’s problem. After completing this project, you’d have ample experience in using PostgreSQL and ETL pipelines.  3. Build and Organize Data Pipelines If you’re a beginner in data engineering, you should start with this data engineering project which is one of the best data engineering research topics. Our primary task in this project is to manage the workflow of our data pipelines through software. We’re using an open-source solution in this project, Apache Airflow. Managing data pipelines is a crucial task for a data engineer, and this project will help you become proficient in the same. Apache Airflow is a workflow management platform and started in Airbnb in 2018. Such software allows users to manage complex workflows easily and organize them accordingly. Apart from creating workflows and managing them in Apache Airflow, you can also build plugins and operators for the task. They will enable you to automate the pipelines, which would reduce your workload considerably and increase efficiency. Automation is one of the key skills required in the IT industry, from Data Analytics to Web/ Android Development. Automating pipelines in a project will surely give your resume the upper hand when applying as a project data engineer. Read our Popular Articles related to Software Development Why Learn to Code? How Learn to Code? How to Install Specific Version of NPM Package? Types of Inheritance in C++ What Should You Know? 4. Create a Data Lake  This is an excellent data engineering projects for beginners. Data lakes are becoming more critical in the industry, so you can build one and enhance your portfolio. Data lakes are repositories for storing structured as well as unstructured data at any scale. They allow you to store your data as-is, i.e., and you don’t have to structure your data before adding it to the storage. This is one of the trending data engineering projects. Because you can add your data into the data lake without needing any modification, the process becomes quick and allows real-time addition of data. Many popular and latest implementations such as machine learning and analytics require a data lake to function correctly. With data lakes, you can add multiple file-types in your repository, add them in real-time, and perform crucial functions on the data quickly. That’s why you should build a data lake in your project and learn the most about this technology. You can create a data lake by using Apache Spark on the AWS cloud. To make the project more interesting, you can also perform ETL functions to better transfer data within the data lake. Mentioning data engineering projects can help your resume look much more interesting than others. 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. 5. Perform Data Modeling Through Cassandra This is one of the interesting data engineering projects to create. Apache Cassandra is an open-source NoSQL database management system that enables users to use vast quantities of data. Its main benefit is it allows you to use the data spread across multiple commodity servers, which mitigates the risk of failure. Because your data is spread across various servers, one server’s failure wouldn’t cause your entire operation to shut down. This is just one of the many reasons why Cassandra is a popular tool among prominent data professionals. It also offers high scalability and performance.  In this project, you’d have to perform data modelling by using Cassandra. However, when modelling data through Cassandra, you should keep a few points in mind. First, make sure that your data is spread evenly. It is one of the trending data engineering projects. While Cassandra helps in ensuring an even spread of your data, you’d have to double-check this for surety.  Data Science Advanced Certification, 250+ Hiring Partners, 300+ Hours of Learning, 0% EMI Secondly, use the smallest amount of partitions the software reads while modelling. That’s because a high number of reading partitions would put an added load on your system and hamper overall performance. After finishing this project, you’d be familiar with multiple features and applications of Apache Cassandra.  Apart from the ones mentioned here, you can also choose to take up projects about data engineering examples used in the real world. Here’s a list of some other projects on data engineering examples: Event Data Analysis Aviation Data Analysis Forecasting Shipping and Distribution Demand Smart IoT Infrastructure 6. IoT Data Aggregation and Analysis The IoT Data Aggregation and Analysis project involves constructing a robust and scalable data pipeline to collect, process, and derive valuable insights from several Internet of Things (IoT) devices. The objective is to create a seamless data flow from sensors, smart devices, and other connected endpoints into a centralized repository. This repository serves as the foundation for further analysis and visualization. The project encompasses several key components, starting with a data ingestion system design capable of handling real-time data streams. Efficient data storage, utilizing databases optimized for time-series data, is essential to accommodate the high influx of information. Preprocessing steps are data cleansing, transformation, and enrichment to ensure data quality and consistency. For analysis, various techniques such as anomaly detection, pattern recognition, and predictive modeling can uncover meaningful insights. These insights might include identifying operational inefficiencies, predicting maintenance needs, or understanding usage patterns. Ultimately, the project aims to empower stakeholders with actionable insights through interactive dashboards, reports, and visualizations. By successfully executing this project, one gains a deep understanding of data engineering principles, real-time processing, and the complexities of managing diverse IoT data sources. Learn More about Data Engineering These are a few data engineering projects that you could try out! Now go ahead and put to test all the knowledge that you’ve gathered through our data engineering projects guide to build your very own data engineering projects! Becoming a data engineer is no easy feat; there are many topics one has to cover to become an expert. However, if you’re interested in learning more about big data and data engineering, you should head to our blog. There, we share many resources (such as this one) regularly.  If you’re interested to learn python & want to get your hands dirty on various tools and libraries, check out Executive PG Program in Data Science. We hope that you liked this article. If you have any questions or doubts, feel free to let us know through the comments below.

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.

Capstone projects have become a cornerstone of modern education, offering students a unique opportunity to bridge the gap between academic learning and real-world application.  In this article, we will discuss why capstone projects have become an indispensable part of education, shaping students into well-rounded, capable, and adaptable individuals ready to tackle the challenges of the professional world. In addition to this, you will also get to learn about some of the most interesting Capstone project ideas of 2023. What Exactly is a Capstone Project? Capstone projects are an integral part of the university curriculum. Although the format for these projects varies, the purpose remains the same. Simply put, a capstone project can be defined as a comprehensive culminating assignment that serves as the final demonstration of a student’s academic learning and skills in any particular field.  In addition to this, capstone projects also serve as an excellent opportunity for students to devise innovative solutions to some of the most common challenges haunting the real world.  Why is the Capstone Project Important? Before we delve into the details of capstone project ideas, let’s first understand their importance. Capstone projects are important for the overall growth of a student for various reasons. Such include: It allows students to combine all the knowledge and skills they have gained throughout their academic journey and apply them to real-world projects. It opens doors for students to apply their skills in a practical avenue, thus demonstrating their ability to handle complex situations. Depending on the project, students might also need to collaborate with mentors or professionals, thus enhancing their communication skills. It is a significant addition to a student’s portfolio, bringing them one step closer to landing their dream jobs. It helps to boost students’ confidence in their abilities and enhances their sense of accomplishment. What is the Purpose of a Capstone Project? The purpose of a capstone project is multifaceted and serves various educational and professional objectives. Some of them include: It Hones Skills Considered Highly Valuable By Employers A well-executed Capstone project is a great way to hone specific skill sets such as creativity, innovation, and problem-solving abilities, all of which are considered in high regard by employers.  It Prepares You For The Workforce Many capstone projects are collaborative efforts that involve working together in teams. This is similar to the collaborative nature of most workplaces, helping students develop essential interpersonal skills and the ability to work harmoniously in diverse teams.  It Boosts Your CV and Helps You To Stand Out As A Candidate Adding your capstone projects to your resume can be a great way to showcase your skills and knowledge in your respective field. It helps to demonstrate your hard-working nature and experience working in a professional, active environment.  Check out our free technology courses to get an edge over the competition. How To Choose Great Topic Ideas For Capstone Projects Choosing the right topic for your capstone project requires careful consideration of multiple factors, such as your goals, interests, and skills. Here is a step-by-step guide to help you select an excellent topic for your capstone project. Identify Your Interests The first step is to identify your interest areas. You can begin by creating a list of fields that interest you and then select accordingly. Remember, doing this is very important since your enthusiasm for the topic will ultimately keep you going throughout the complex and long journey of finishing your capstone project. Research Current Trends Stay up-to-date with current trends and recent advancements in your subject of interest. Choosing a relevant and current topic also helps bring value to your project work. Defining A Clear Problem All capstone projects address a specific problem or a question. Therefore, whichever topic you choose, ensure that the problem you wish to address has been defined properly and concisely, as this will guide your research and solutions. 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 Best Capstone Project Ideas & Topics Mentioned below are a few interesting capstone project topics for you to explore. A Study Determining The Imperativeness Of Computers In Education From easy access to information and enhanced learning experiences to digital literacy and remote learning, the advantages computers have brought are endless. A study highlighting how this integration of computers into education can be an interesting topic for your next Capstone project. Check out the MS in Data Science course offered by Liverpool John Moores University in collaboration with upGrad to further strengthen your work on data science capstone projects. An Assessment of The Importance of Visuals In Your Advertising Campaigns Visuals are an effective tool for storytelling. Their ability to capture attention, evoke emotions, convey messages, and foster engagement has made them a crucial part of a marketer’s toolbox. A recent study claimed that as much as 91% of consumers prefer visual content to written content. Another intriguing capstone project topic is understanding the significance of visuals in advertising campaigns and why they play such a crucial role in capturing audience interest. A Study On SaaS Technologies of The Modern Times Software as a Service, or SaaS as it is more frequently known, has revolutionised how businesses access and use software applications. You can throw light on several critical facets of this topic, such as the revolutionary impact of SaaS on various industries, its benefits, and problems. Understanding the Design and Implementation of Sensor-Guided Robotics Sensory-guided systems have paved the way for intelligent and versatile machines capable of interacting with their environment in complex ways. These systems utilise diverse sensors, enabling robots to perform tasks with enhanced adaptability, accuracy, and efficiency. An in-depth research on this topic, highlighting the components, design considerations, applications, and challenges of sensor-guided robotics, is yet another interesting Capstone project idea for you to explore. A Study On Diversity Management in The Age Of Globalization The concept of diversity management has gained significant momentum, especially in this era of globalisation. As business enterprises continue to expand their global reach, the need for understanding, valuing, and effectively managing diversity has become a crucial ingredient for success. With this topic, you can discuss the globalisation-diversity nexus, benefits of diversity management, best practices, and challenges. 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 Cycle of Doing A Capstone Project Now that you have explored some of the most relevant capstone project examples of 2023 let’s take a look at the steps involved in completing a capstone project. Project Selection Your journey commences with starting a project topic that aligns with your interests, skills, and career goals. However, while selecting your topic, you must ensure that it is relevant to your field of interest, specific in nature, and must address any issue or concern. Research and Planning Once you have short-listed your topic, it is time for you to conduct extensive research on the same to understand the existing methodologies and potential solutions related to the chosen subject. You can also create a detailed outline highlighting the research methodology, required resources, and timeline. Data Collection and Analysis Gather relevant data on your research topic through surveys, interviews, experiments, or any other method, depending on the project’s nature. Once you have collected all the data, you can analyse the same using appropriate tools and techniques to draw meaningful conclusions. Presentation and Reporting After going through the provided steps, it is now time for you to compile all your findings and conclusions into a single report or document for presentation. Please note that your presentation must convey the significance and impact of your work properly. In order to gain more insight into opting for the right capstone project for your specialisation, we recommend enrolling in the Master of Science in Computer Science from upGrad to further expand your knowledge and enhance your candidature.  Capstone Project vs. Thesis While both capstone projects and theses aim to showcase students’ mastery in their field of study, they differ in structure and focus. Capstone Project Thesis Capstone projects can be done by high school students or college students. A thesis requires a higher level of academia, such as an undergraduate or master’s degree. Capstone projects take multiple forms, such as reports, presentations, or practical applications. A thesis usually follows a strict structure comprising multiple chapters, including an introduction, literature review, methodology, etc. A capstone project enables students to apply their theoretical knowledge to solve real-world problems. The thesis focuses on conducting original research and contributing new insights. Learn data science courses online from the World’s top Universities. Earn Executive PG Programs, Advanced Certificate Programs, or Masters Programs to fast-track your career. Conclusion A capstone project is a vital educational experience that prepares students for the complexities of the professional world. It fosters a comprehensive skill set, personal growth, and a deeper understanding of applying knowledge in real-life contexts. Hopefully, the list of capstone project ideas mentioned above has helped you narrow down your selection process to some extent. Remember, selecting a topic that resonates with you or perfectly syncs with your goals sets the stage for a successful and fulfilling project experience. With courses like upGrad’s Master of Science in Machine Learning and Artificial Intelligence, you get to explore the depth of the evolving realm of machine learning and artificial intelligence while getting an opportunity to work on real-time capstone projects. FAQs