Data Science Blog Posts

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.

Ever wondered how to tackle the pesky issue of duplicate data in Microsoft Excel? Well, you’re not alone! Excel has become a powerhouse tool, especially in Business Analysis, empowering users to handle vast amounts of information effortlessly. However, as the datasets grow, so does the likelihood of encountering duplicate entries. This can lead to redundancy, confusion, and inaccurate analyses. You need to know more Excel shortcuts to work efficiently and with ease In this article, we’ll walk you through the step-by-step process of identifying and deleting duplicates in Excel, streamlining your data, and saving you precious time and effort. So, let’s get started on our journey to a cleaner and more efficient Excel experience! Find Duplicates in Excel Duplicate data can be both useful and burdensome, but it often hinders data understanding. Before removal, reviewing and finding duplicates in Excel is better than deleting it immediately. To identify duplicates in Excel, use conditional formatting as follows: Select the data you want to check for duplicates. From the Home tab, go to Conditional Formatting > Highlight Cell Rules > Duplicate Values. In the Conditional Formatting window, choose a colour scheme to highlight duplicates (opt for high contrast colours like Light red fill for better readability). Click “Done” to apply the formatting. Review the highlighted duplicate data and decide whether to remove any redundant information. This process ensures a more informed decision regarding data cleanup. How to Remove Duplicate Values? Here’s a step-by-step guide on how to remove Excel duplicates: Step 1: Open your Excel file and select the cell or cell comprising the dataset from which you wish to eliminate the duplicate details. Step 2: Navigate to the DATA tab at the top of the Excel window. Step 3: Look for the “Remove Duplicates” option in the Data Tools section and click on it. Step 4: A dialogue box will open, showcasing your dataset’s detailed list of columns. Here, you can choose the columns in Excel where you wish to identify and remove duplicates. If your data has headers (column names), check the “My data has headers” option. Step 5: After selecting the appropriate columns, click on the “OK” button to proceed. Step 6: Excel will now analyse your data depending on the specifically selected columns and eliminate the duplicate rows. Once the process is complete, a dialogue box will pop up, summarising the number of duplicate values found and removed and the count of unique values. Step 7: Congratulations! Your duplicate records have now been successfully removed, leaving you with a cleaner and more streamlined dataset. Let’s now explore another method for deleting duplicates in Excel by utilizing the Advanced Filter option.  Understand Filtering for Unique Values or Removing Duplicate Values in Excel Before proceeding to remove duplicates, it is highly recommended to double-check your data. You can use filtering or conditional formatting to identify unique values and ensure you get the expected results before making any changes to your dataset. This cautious approach will help maintain data accuracy and prevent unintended data loss. How to Filter Specific Unique Values in Excel Here’s a detailed guide on how to filter unique values in Excel: Step 1: Select the column or columns comprising the data you aim to filter for unique values in your Excel sheet. Step 2: Visit the “Data” tab in the Excel ribbon, and select the “Filter” button present within the “Sort & Filter” section. This will update filter arrows to the column headers. Step 3: Click on the filter arrow in the column header to open the filter options for that column. Step 4: Take your cursor to the “Number Filters,” “Date Filters,” or “Text Filters” options depending on the type of data you are working with. Step 5: Select “Does Not Equal” in the pop-out menu. If you don’t find this option, choose “Custom Filter”, located at the bottom part of the menu. Step 6: A new dialogue box will appear. Ensure that the initial drop-down shows the message “does not equal,” then fill in the specific value you want to filter out in the box on the right. Step 7: Click “OK” to apply the filter. Step 8: Your Excel sheet will now display only the data containing the unique values based on your filter criteria. This means you’ll see data that does not match your specified value. Step 9: When you’re done using the filter, click the “Filter” button again in the Excel ribbon to turn it off. The sheet will return to its normal view, showing all the data without filtering. Using the Advanced Filter Option The Advanced Filter option in Excel is a powerful tool that allows you to filter duplicate values and extract unique values to a different location. Here’s a step-by-step guide on how to use the Advanced Filter: Begin by selecting a cell or range within the dataset from which you want to remove duplicates. If you select a single cell, Excel will automatically determine the range when you access the Advanced Filter. Locate the Advanced Filter option in the DATA tab under the Sort & Filter section. Click on “Advanced” to open the dialogue box containing various options for advanced filtering. In the dialogue box, choose the “Copy to another location” option. This selection will enable you to copy the unique values to a different location. Verify that the “List Range” field contains the correct range for your records. In the “Copy to:” field, specify the range where you want the resultant unique values to be copied. Crucially, inspect the box with the label “Unique records only.” This step ensures that only the unique values will be copied to the new location. Click “OK” to apply the Advanced Filter. After executing the filter, you will find the unique values copied to the specified location, such as cell G1. These built-in functionalities in Excel effectively remove duplicates and work with unique data. Now, let’s move on to explore how to use formulas to remove excel duplicates.  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. How to Use Formulas to Delete Duplicates in Excel? Removing duplicates in Excel can be easily accomplished using built-in options in the Excel ribbon.  Simply go to the “Data” tab and select “Remove Duplicates” in the “Data Tools” group. This method works well for a one-time operation. However, if you want a dynamic solution that automatically updates when you insert or delete values in the table, consider using the =UNIQUE() function. This formula takes a range of values and returns only the unique values, eliminating the need to redo the operation every time. Another approach involves identifying duplicate values using the IF() and COUNTIF() functions. You can create a formula like =IF(COUNTIF($A$2:$A$7, A2) > 1, “Duplicate”, “Unique”) to mark duplicates as “Duplicate” and unique values as “Unique”.  After finding the duplicate values, you can use the Home tab’s Filter option to segregate, delete, or save them separately. Check out our free datascience courses to get an edge over the competition. Conditionally Format Unique or Duplicate Values To highlight duplicate cells in Excel, follow these steps: Select the data you wish to check for duplicates: a single column, a row, or a range of cells. Visit the “Home” section, and in the “Styles” group, select the “Conditional Formatting” option. After that, select “Highlight Cells Rules” and click on “Duplicate Values.” The “Duplicate Values” dialogue box will open, with the default format of Light Red Fill and Dark Red Text already selected. Click “OK” to apply this default format. Alternatively, choose other predefined formats from the dropdown list or click “Custom Format” to select your desired fill and font colours. If you want to highlight unique values instead, choose “Unique” from the left-hand box in the “Duplicate Values” dialogue box. The built-in rule can highlight duplicates in one column or across multiple columns. Note that when using the built-in rule for multiple columns, Excel highlights all duplicate instances in the range without comparing values in those columns. To highlight duplicate rows or find matches and differences between two columns, you must create custom conditional formatting rules based on specific criteria. Remember that the built-in rule highlights all duplicate occurrences, including their first instances. You can create a conditional formatting rule based on a formula if you want to highlight duplicates except for the first occurrences. 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 Use the Power Query Tool to Remove Duplicates in Excel? Power Query is an advanced Excel tool for Extract, Transform, and Load (ETL) operations. With Power Query, you can import data from various sources and apply transformations, including data cleansing and reshaping. Here’s a step-by-step guide on removing duplicates using Power Query: Step 1: Create a table by selecting the rows you want to work with, then go to the “Insert” tab and choose “Table.” Alternatively, you can press “CTRL+T” to create a table and make sure to check the option “My table has headers.” Step 2: Now, navigate to the “Data” tab and click on “Get & Transform Data,” then select “From Table/Range.” Step 3: The Power Query Editor will open, allowing you to perform the necessary data transformations. Step 4: In the Power Query Editor, go to the “Home” tab and click on “Remove Rows,” then choose “Remove Duplicates.” Step 5: After removing duplicates, you’ll see a “Query Settings” message stating ‘Removed Duplicates.’ Step 6: Once you’ve confirmed the duplicates are removed, click “Home” again and select “Close & Load.” Step 7: Power Query will load the cleaned data into a new sheet, with the duplicates successfully removed. Following these steps, you can efficiently clean your data and remove duplicate records using Power Query in Excel. This helps ensure data accuracy and enhances your data analysis and reporting capabilities. 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 Microsoft Excel is an indispensable tool that offers a multitude of functionalities, making it highly useful for various sectors. Its ability to handle complex data, perform calculations, and visualise information efficiently benefits businesses, students, and professionals alike.  To unlock the full potential of data management and analysis, upGrad presents you with the opportunity to pursue a Master of Science in Data Science from Liverpool John Moores University. This comprehensive program equips students with advanced skills and knowledge to excel in the dynamic field of data science opening doors to exciting career opportunities.   FAQs

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. 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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

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

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