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A Complete Guide To Data Analytics Courses at NPTEL

Data is dynamic and highly influential and organizations all around the world recognize the significance that data analytics holds a potential role when it comes to fostering organizational growth and profitability. The importance of data analytics has increased more even in today’s business environment. In order to meet consumer demand, data mastery skills are necessary so that useful information can be extracted by using various AI tools from both structured and unstructured data which makes data analytics course a vast field. In this article, we are going to provide the details of the Data Analytics courses at NPTEL.


Best data analytics courses at NPTEL


What is Data Analytics?

Data is the foundation of everything, if you want to explore something, what is the first thing you need that is data. Data analytics is the process of using data, methods, and tools to spot patterns and trends that lead to practical knowledge that can be used to make decisions. Data analytics’s main goal is to tackle specific issues or problems that are pertinent to a company in order to improve business outcomes or can lead to decision making which is necessary for the smooth functioning of the organization.

It is the study of analyzing unprocessed data to draw inferences about information. Numerous methods and procedures used in data analytics have been streamlined into mechanical procedures and algorithms that operate on raw data for human consumption. Making data-informed strategic decisions is essential in the business and workforce landscape of today.

Understanding how to use data effectively can be very beneficial for any organization, whether its goals are to better understand its customers, streamline operations, or create new business prospects. The demand for the data analytics field is tremendously increasing which makes this field very important to opt for. Data analytics can be applied to different fields like healthcare, insurance, finance, sports, industries, retail, and the list goes on.


Types of Data Analytics?

Data analytics is generally of four types, which are as follows:

Descriptive Analytics: This summarises or explains what has occurred over a specific time frame. For example: if we talk about Instagram, then we see that our views increased as compared to the last month, is the graph going up or down?

Diagnostic Analytics: This is more concerned with the reasons behind events that have occurred. More varied data inputs are used, along with some speculation.  For example: if we take the example of Instagram, what is the reason behind the views going down? Which content needs alteration?

Predictive Analytics: This section deals with what will probably occur soon. These methods analyze historical data to spot trends and assess their likelihood of recurrence. For example: How is the audience going to perceive the content according to the content shared in the past on Instagram?

Prescriptive Analytics: This suggests an approach or what action should be taken. For example:  we should collaborate with an ad agency in order to get more viewers according to the past history showing the viewers viewed the content on Instagram.


Various Steps Followed in Data Analytics


Data analysis covers a number of steps, including:

Problem Identification: The first step in the analytics process involves comprehending the company’s problems, defining the organizational goals, and planning a profitable solution. The challenges that e-commerce businesses face include anticipating product returns, making relevant product recommendations, canceling orders, spotting fraud, optimizing truck routing, etc.


Identification or grouping of data: Identifying the data needs or how the data is grouped is the first stage. Data might be categorized based on gender, income, age, or other factors. Data values might be numerical or categorical.


Collection of data: The process of gathering data is the second phase in data analytics. There are a number of sources or equipment, including computers, the internet, cameras, environmental sources, and human employees, that can be used to accomplish this data collection process.


Data arrangement or organizing: After being gathered, the data needs to be organized in order to be analyzed. A spreadsheet like Excel or another piece of software like SPSS that can handle statistical data may be used for this.


Correction of errors before implementation: After that, the data is cleaned up for analysis. It gets cleaned up and reviewed to make sure there are no duplicates, errors, or missing information. This phase assists in removing any inaccuracies before the data is sent to a data analyst for analysis.


Data exploration and analysis: Implementing exploratory data analysis is the next crucial step after collecting the appropriate data. To analyze, visualize, and forecast future effects from this data, employ business intelligence tools, data mining techniques, and predictive modeling. By using these data exploration techniques, you can ascertain the impact and relationship between a particular attribute and other variables.


Interpret the results: The results must be interpreted in order to determine whether the outcomes live up to your expectations. After interpreting the results, you might discover occult patterns and upcoming trends, which will help to make data-driven decisions.


Why Does Data Analytics Need to Be Studied?

After looking at what data analytics is, let’s discuss some applications for it and why it is necessary to be studied.


Helps in better decision-making: Manual labor and guesswork are eliminated by data analytics. It helps in developing the right content, generating or setting market campaigns, or product development. Businesses can leverage the insights uncovered by data analytics to help them make better or more informed decisions.


More effective customer service:  You may customize client service to meet their needs using data analytics. Additionally, it adds personalization and strengthens connections with clients. It enables you to make suggestions for goods and services that are better which is necessary for growth and profitability.


Efficiencies in operations: You can improve production, reduce costs, and streamline business processes with the use of data analytics. You might spend less time creating advertisements and other content that doesn’t appeal to your audience if you have a better understanding of what they want.


Constructive or successful marketing: You can learn a lot about the effectiveness of your efforts via data analytics Furthermore, you can figure out which prospective clients are most likely to affix themselves to a campaign and develop into leads.


Sectors of Data Analytics?

Let’s examine a few of the commercial fields where data analytics is applied:


IT Sector: The IT sector holds the largest market share for the data analytics sector. Due to the great innovation in the IT sector, the demand for the data analytics sector makes it a great career to pursue.

Banking and Financial Sector: This sector is the second largest sector. Banking and Financial Sectors have used AI and data analytics to expand their businesses.

E-Commerce-Retail Sector: Retailers can forecast trends, suggest new items, and grow their businesses by using data analytics to better understand their customers’ wants and purchasing patterns.

Healthcare Sector: To deliver vital diagnosis and treatment alternatives, the healthcare industry analyses patient data. Additionally, data analytics aid in the creation of novel strategies for drug development.

Manufacturing Sector: Manufacturing industries can find fresh cost-saving potential by using data analytics. They may deal with challenging issues with the supply chain, labor shortages, and equipment breakdowns.

Logistics Sector: Data analytics is used by logistics organizations to create new business models and improve routes. This in turn ensures that the delivery will happen quickly and on schedule.

The topics that were covered here are just a few instances of data analytics applications.


In this article, we are going to provide all the details related to Data Analytics courses at NPTEL. But, before going further to the Data Analytics courses at NPTEL, let’s discuss what NPTEL is all about.


NPTEL is a project funded by the Ministry of Human Resources Development (MHRD) that was started by seven Indian Institutes of Technology (Bombay, Delhi, Kanpur, Kharagpur, Madras, Guwahati, and Roorkee) in order to offer top-notch education to everyone interested in studying at an IIT, along with the Indian Institute of Science, Bangalore, in 2003. The main objective was to provide undergraduate and graduate management courses as well as web and video courses in all of the major engineering and physical scientific fields. NPTEL has the largest global online resource for engineering, fundamental science, and a few humanities and social science courses.


Data Analytics Courses at NPTEL

Basically, there are three Data Analytics courses at NPTEL. Let’s discuss in detail the Data Analytics courses at NPTEL.


1.  NOC: Data Analytics with Python

This article titled Data Analytics courses at NPTEL offers a title data analytics course with Python under NPTEL, run by MHRD. The Data Analytics courses at NPTEL are not limited to getting the output but also help in detailed interpretation. The uniqueness of this course is it explains the statistical aspect of analytics. Students of engineering and management programs as well as suitable for entry and middle-level managers in analytics companies.

The course starts with an introduction to fundamentals and gradually moves towards advanced analytics topics with a focus on both “why and how” and uses examples to introduce the ideas of statistical inference.

All the data analytics tools are explained with the help of Python programming. The students completing this course will be confident in both conceptual understanding of the analytical methods and applying them using Python.


Duration: 12-week course spread over 60 lectures each of 30 minutes.

Level: UG, PG, and PhD students can all benefit from this course.

Course Fee: Rs. 3000 + GST and optional exam fee applicable.


Course Curriculum


Unit 1:

  • Introduction to data analytics
  • Python Fundamentals
  • Central Tendency and Dispersion


Unit 2:

  • Introduction to Probability
  • Probability Distributions


Unit 3:

  • Python demo for students
  • Sampling and Sampling Distribution
  • Confidence interval estimation


Unit 4:

  • Hypothesis Testing
  • Errors in Hypothesis Testing


Unit 5:

  • Hypothesis Testing
  • Post Hoc Analysis


Unit 6:

  • Randomisation Block Design
  • Two-way ANOVA
  • Linear Regression


Unit 7:

  • Estimation and Prediction of Regression Model
  • Residual Analysis
  • Multiple Regression Model
  • Categorical Variable Regression


Unit 8:

  • Maximum Likelihood Estimation
  • Logistic Regression
  • Linear Regression Model vs. Logistic Regression Model


Unit 9:

  • Confusion Matrix and ROC
  • Performance of Logistic Model
  • Regression Analysis Model Building


Unit 10:

  • Chi-Square Test of Independence
  • Goodness of Fit Test
  • Cluster Analysis


Unit 11:

  • Cluster Analysis
  • K- Means Clustering
  • Hierarchical Methods of Clustering-I


Unit 12:

  • Hierarchical Method of Clustering-II
  • Classification and Regression Trees (CART: I)
  • Measures of attribute selection in CART: II
  • Attribute selection measures in CART: II
  • Classification and Regression Trees (CART – III)


2. NOC: Applied Linear Algebra for Signal Processing, Data Analytics and Machine Learning, IIT Kanpur

It is a very general and fundamental course to opt for among the Data analytics Courses at NPTEL. This course seeks to teach students all of the fundamental and advanced ideas in linear algebra, with a strong emphasis on applications. This course is suitable for the fields of engineering, science, management, and social sciences.

This course is just not a theoretical course but special emphasis on applications like machine learning, data science, signal processing, and wireless technology, the list does not end here.  The faculties help you to gain maximum with the latest information and guide you in the best way.


Duration: 12 weeks


Pre-requisites for this course:

  • Basic Knowledge of Calculus
  • Some familiarity with Probability
  • Basic Matrix Analysis
  • Willingness to Learn on the part of students or participants


Course Curriculum:

  • Introduction and Vector Properties
  • Inner product application – Beamforming
  • Matrices – Introduction and applications
  • Electric circuits, traffic flows
  • Matrix
  • Null Space of Matrix
  • Gram – Schmidt Orthogonalization
  • Gaussian Random Variable
  • Linear Transformation of Gaussian Random vectors
  • Machine Learning and applications
  • Eigenvalue and Decomposition of Eigenvalue
  • Special Matrices
  • Positive Semi-Definite Matrices
  • Computer Vision Application
  • Least Square Solution
  • Wireless Application
  • Computation Mathematics Applications
  • Least Norm Solution
  • Singular Value Decomposition
  • SVD Application in MIMO Wireless Technology, wireless optimization, Machine learning
  • Multiple Signal Classification (MUSIC) Algorithm
  • Linear Minimum mean square error (LMMSE) principle and application
  • Time series prediction via auto-regressive model
  • Recommender System
  • Fast Fourier Transform
  • OFDM (Orthogonal Frequency Division Multiplexing (OFDM)
  • Linear Dynamic system
  • Machine Learning application
  • Support Vector Machines
  • Sparse Regression
  • K-Means Clustering algorithm
  • Stochastic Process and Markov Chains
  • Discrete-Time Markov Chains
  • Least Squares
  • Woodbury Matrix
  • Conditional Gaussian Density


Other best data analytics courses:


3.  NOC: Introduction to Data Analytics, IIT Madras

The focus of this course is to introduce students to the tools and techniques that are used currently for understanding data and to drive useful knowledge from it. This Data Analytics courses at NPTEL follows the broad aspects of Data Analytics. The course starts with data analysis through statistical methods, and descriptive statistics, which gives the idea of how to describe data, and how to summarize data.

This course also covers Visualization techniques. Inferential statistics which guide making generalizations or inferences on the basis of data not just summarization means more emphasis is on practical knowledge. More emphasis on the learning of the Algorithm model and learning to find the interesting pattern in the data.

In short, data analytics courses at NPTEL will help the students to learn prediction and to find interesting patterns in data. Learn to make inferences and also how to use the information to make concrete decisions.


Pre-requisites for this course:

You must be proficient in calculus and linear algebra at the high school level to successfully complete this course. It would be ideal to have some knowledge of programming, statistics, and probability theory.


Duration: 9 weeks


Week 1. Descriptive Statistics

  • Introduction to the course
  • Descriptive Statistics
  • Probability Distributions


Week 2. Inferential Statistics

  • Inferential Statistics through hypothesis tests
  • Permutation & Randomization Test


Week 3. Regression & ANOVA

  • Regression
  • ANOVA (Analysis of Variance)


Week 4. Machine Learning: Introduction and Concepts

  • Differentiating algorithmic and
  • Model-based frameworks
  • Regression
  • Ordinary Least Squares
  • Ridge Regression
  • Lasso Regression
  • Regression & Classification


Week 5. Supervised Learning with Regression and Classification

  • Bias-Variance Dichotomy
  • Model Validation Approaches
  • Logistic Regression
  • Linear Discriminant Analysis
  • Quadratic Discriminant Analysis
  • Regression and Classification of Trees
  • Support Vector Machines


Week 6. Supervised Learning with Regression and Classification

  • Ensemble Methods: Random Forest
  • Neural Networks
  • Deep learning


Week 7.  Unsupervised Learning and Big Data Analytics Challenges

  • Clustering Associative
  • Rule Mining
  • Challenges for big data analytics


Week 8: Prescriptive analytics

  • Learn to create data for analytics through designed experiments
  • Learn to create data for analytics through active learning
  • Learn to Create data for analytics through Reinforcement learning


Week 9: Summary and insights into the final exam

  • It concludes with a summary of insights into the exam


Criteria for getting a certificate in the Data Analytics courses at NPTEL:

The criteria for getting the certificates in the following three courses are as follows, If we talk about the course data analytics with Python and Applied Linear Algebra for Signal Processing, Data Analytics, and Machine Learning.


The certification is divided into four parts, i.e., elite, gold, silver and successfully completed.

The certificate will be provided based on the final score and the final score will be calculated on the basis of the exam score and assignment score.

The average assignment score should be more than 10 out of 25 and the exam score would be more than 30 out of 75.

The final score will be the addition of the assignment score and exam score. Based on the calculated marks the students will become eligible for the certificate.


After calculating the final score, the Criteria for the certificate are as follows:

  • More than or equal to 90 helps to get an Elite + Gold Certificate
  • Marks ranging from 75 to 89 help to get an Elite + Silver Certificate
  • More than or equal to 60 helps to get an Elite Certificate only
  • Marks between 40 – 59 help to get a successfully completed certificate.


If we talk about the introduction to data analytics, then the final score will be calculated on the basis of assignment score i.e. (50 % best 3 out of 5 assignments) and exam score (50 % of certification exam score).

This certification also includes the criteria for certification in three categories after calculating the score, like Elite + Gold, Gold, successfully completed, and certificate of participation.

The certificates that will be issued include the name, picture, and final exam score with the breakdown on the certificate.

The issued certificates will have the logos of NPTEL and IIT. The students can E-Verify at the NPTEL website.




What are the chances of getting a job, after taking the data analytics courses at NPTEL?

One can obtain positions such as data engineer, data scientist, data architect, database administrator, and data analyst with a starting salary of Rs. 4 lakh or more after completing a data analytics course.


What is NOC in the Data Analytics courses at NPTEL?

NOC means NPTEL Online Certification. NPTEL offers online certification courses through its online portal. You will get the opportunity to learn under the guidance of IITs and IISc, which is funded by MoE, Govt. of India.


What qualifications must I possess to work as a business analyst?

You must have strong writing skills, knowledge of MS Office, Domain Knowledge and Moreover, and consistency and accuracy while preparing reports.


Is there any need to have a technical background to pursue the data analytics course?

No, there is no need to have a technical background for the data analytics course, even with non – a technical background can go for this course.


What is data analytics’ future?

Future developments in data analytics are anticipated to be significantly influenced by developments in fields like artificial intelligence, machine learning, and cloud computing, which makes this field grow and expand continuously.



In conclusion, this article tried to include all the details related to Data Analytics courses at NPTEL. Organizations wishing to gain insight into their operations and make data-driven choices can benefit greatly from using data analytics. Data Analytics uses statistical, operations research, and management tools to drive business performances. There is a tremendous increase in the demand for data Analytics expertise across all domains throughout all major organizations across the globe.

It’s critical to take your objectives, existing knowledge, and preferred learning style into account while selecting a course or resource to study data analytics. While intermediate and advanced courses can help you develop more specialized skills and expertise, beginner-level courses and resources can give you a solid foundation in the fundamentals of data analytics. Additionally, keep in mind that the subject of data analytics is constantly evolving, therefore it’s critical to keep studying and upgrading your knowledge even after finishing a course or program.

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