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Top 3 Data Analytics Courses At NPTEL With Practical Training

Data is the world`s most valuable resource! Although the data is not new, it is expanding quite quickly. Huge new opportunities are emerging as a result of the growing interconnections among people, networked data, big data algorithms, and analytics. Now, businesses and even economies are creating goods and services using data-driven analogies. Given that data is the foundation of so many cutting-edge techniques, like AI technologies, machine learning, and deep learning, it is imperative to be able to provide an adaptable environment to support the data demand. They can surely improve or revamp nearly every aspect of their company model thanks to data. In this article, we are going to cover the Data Analytics Courses at NPTEL.

Top 3 Data Analytics Courses At NPTEL With Practical Training

Why Study Data Analysis?

More and more businesses are starting to see how data analytics may help them obtain insights and make wise decisions. There is a significant need for data analysts that can define and reveal hidden patterns, identify new possibilities, and produce insights for a business’s benefit. You may learn all the necessary skills to launch a career in data analytics with the help of this lesson.

Data analytics: What is it?

In the form of a file system, web servers, transaction records, and different customer-related data, businesses all over the world produce enormous amounts of data every day. Social media platforms also produce a huge quantity of data. In order to maximize value from their created data and make significant business choices, companies should use all of it. In order to achieve this goal, data analytics is the motivating factor.

Discovering hidden patterns, and undetected trends, finding connections, and gaining insightful knowledge from vast datasets are all part of the process of data analytics, which is used to create business forecasts. Because of this, your business operates more rapidly and efficiently. Modern tools and techniques are widely used by businesses for data analytics. In a word, this is data analytics for novices.

Implications of Data Analytics

Given that you now have a basic understanding of data analytics, let’s look at how to use it.

  • Enhancing Decision-Making

Data analytics eliminates hunches and manual effort. whether this involves choosing the right content, planning marketing campaigns, or making products. Insights from data analytics may be used by organizations to help them decide wisely leading to better outcomes and pleased customers.

  • Improved client services

You may customize client service to their requirements by using data analytics. It also provides personalization and improves relationships with customers. Information on the tastes, issues, and other characteristics of the customer may be obtained through data analysis. It provides you with the chance to suggest upgraded goods and services.

  • Operations Efficiency

Data analytics may be used to increase productivity, save costs, and streamline corporate processes. You spend less time producing adverts and material that isn’t relevant to your audience’s interests when you have a better knowledge of what they want.

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  • Effective Marketing

You can learn a lot about the effectiveness of your efforts via data analytics. For the best outcomes, this helps them become more perfect. Additionally, you can determine which potential customers are most likely to interact with advertising and develop into leads.

Data Analytics Process Steps

The next stage in understanding data analytics is to get familiar with how data is evaluated in firms. The data analytics journey consists of a number of phases. Let’s investigate it with the assistance of an analogy.

Think of yourself as the proprietor of an internet shop with a customer of about a million individuals. To help your organization grow, you should first pinpoint particular business-related problems before creating data-driven solutions.

The steps to address the issues are as follows:

  1. Identify the issue

Understanding business difficulties, determining corporate objectives, and developing a lucrative solution are all part of the first step of the analytics process. E-commerce businesses frequently struggle with problems including anticipating product returns, providing pertinent product suggestions, canceling orders, spotting fraud, maximizing track routing, etc.

  1. Data Gathering

To solve the issues your company is having, you must next gather transactional business information and customer-related data from the last several years. The information may contain specifics like the quantity of a product that was sold overall, the revenue and profit margins, and the date the transaction was placed. Historical data has a significant impact on a company’s future.

  1. Data Cleaning

Your whole collection of data will now frequently be unstructured, disorganized, and contain undesired missing values. Such information is neither relevant or appropriate for data analysis. Therefore, in order to prepare the data for analysis, you must clean it to eliminate unnecessary, redundant, and missing variables.

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  1. Data exploration and analysis

This makes them ideal and produces the highest performance. To evaluate, display, and forecast future effects from this data, employ BI tools, data mining techniques, and predictive analysis. These methods allow you to ascertain the relationship and effect between a specific trait and other variables.

You may observe the following findings from the analysis:

  • When a consumer purchases the subsequent item, you can know.
  • You can comprehend why it took so long to provide the thing.
  • You gain more understanding of what customers are looking for in terms of products, refunds, etc.
  • It will be possible to anticipate the sales and profits for the upcoming quarter.
  • You might lower order cancellations by simply shipping necessary products.
  • Among several other things, it will be decided which route will convey the goods the quickest.
  1. Interpreting the outcomes

The findings must be interpreted in order to determine whether the outcomes live up to your expectations. You could learn about future trends and mysterious patterns. Making educated, data-driven decisions will be possible for you as a result of the knowledge you gain from this.

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Data Analytics Tools

Data analytics has quickly advanced in terms of technological capabilities, in addition to a wide range of mathematical techniques for crunching numbers. Data analysts may now gather data, record transactions, process data, and publish conclusions with the use of a wide variety of software tools.

Charts and graphs, Microsoft Office Excel, and data analytics have a tenuous history together. Data analysts now routinely use unstructured programming languages when converting and altering datasets. Many people frequently utilize Python and other open-source languages. Graphical modeling or statistical analysis may be done using more specialized data analytics tools like R.

Data analysts get help while they’re reporting or presenting their findings. Data visualization and analysis tools like Tableau and Power BI are used to gather data, carry out data analytics, and then present the findings in dashboards and reports.

However, additional technologies are being developed to support data analysts. Data mining may be aided by SAS, an analytics platform while processing massive data sets can be facilitated using Apache Spark, an open-source platform. In order to increase the value they provide to their firm, data analysts today have access to a wide choice of technology tools.

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Who Should Study Data Analytics?

A job in data analytics could be the next big thing because of the enormous amount of data that is being created every single day and the enterprises that are trying to use it. You may enrol in the well-known Data Analytics courses at NPTEL if you have a considerable interest in data and understand how to extract usable information from data. This data analytics lesson will assist you in determining exactly the course you should begin with, regardless of your level of experience.

Prerequisites for Data Analytics Courses at NPTEL

Anyone may acquire the skills necessary to work in data analytics. However, the following requirements might be beneficial for selecting a job in data analytics:

  • Working knowledge of mathematics and quantitative concepts
  • Some programming experience
  • Willingness to learn statistical concepts
  • Passion for solving problems

Data Analytics Courses at NPTEL

There are 3 data analytics courses at NPTEL offered. Let’s have on the courses with the below details:

1.   Data Analytics with Python, IIT Roorkee

In order to offer top-notch education to everyone who aspires to attend an IIT, the MHRD-funded Data analytics courses at NPTEL initiative was started in 2003 by 7 IITs and the IISc in Bangalore.

Data analytics courses at NPTEL mentors are utilizing the Python programming language to share with you all a variety of fascinating stories and demonstrations of analytics. The goal of this course is to teach students how to apply analytics in their careers and daily lives by providing examples of the practice in a wide range of sectors. The fact that participants, the student, are constructing analytics models first-hand throughout this course is one of its most significant features. The course staff encourages you to engage in the discussion boards and to make use of all the resources accessible to you while taking the course.

Duration: 12 weeks

Level: Undergraduate/PostGraduate

Course Curriculum

Unit 1:

  • Introduction to data analytics
  • Python Fundamentals
  • Central Tendency

Unit 2:

  • Introduction to Probability
  • Probability Distributions

Unit 3:

  • Python Demo for Distributions
  • Sampling and Sampling Distribution
  • Distribution of Sample Means
  • Distribution of population, and Variance
  • Confidence interval estimation

Unit 4:

  • Hypothesis Testing
  • Errors in Hypothesis Testing
  • Hypothesis Testing

Unit 5:

  • Hypothesis Testing
  • ANOVA – I
  • Post Hoc Analysis

Unit 6:

  • Randomize block design (RBD)
  • Two Way ANOVA

Unit 7:

  • Estimation, Prediction of Regression Model
  • Residual Analysis
  • MULTIPLE REGRESSION MODEL I & II
  • Categorical variable regression

Unit 8:

  • Maximum Likelihood Estimation- I & II
  • LOGISTIC REGRESSION- I & II
  • Linear Regression Model
  • Logistic Regression Model

Unit 9:

  • Confusion matrix and ROC- I & II
  • Performance of Logistic Model
  • Regression Analysis Model Building

Unit 10:

  • Chi-Square Test of Independence – I & II
  • Chi-Square Goodness of Fit Test
  • Cluster analysis

Unit 11:

  • Clustering analysis Continued
  • K- Means Clustering
  • Hierarchical method of clustering -I

Unit 12

  • Hierarchical method of clustering- II
  • Classification and Regression Trees
  • Measures of attribute selection
  • Attribute selection Measures
  • Classification and Regression Trees

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

With a heavy emphasis on applications, this course seeks to teach students all of the fundamental and advanced ideas in linear algebra. One of the fundamental techniques, linear algebra is used in many different disciplines, including finance, organizational theory, data analytics, wireless technology, signal processing, machine learning, and signal processing. The course is appropriate for all undergraduate and graduate students as well as working engineers, scientists, and managers from the many above-mentioned professions who are curious to learn about the most cutting-edge uses of linear algebra in areas like machine learning, data analytics, signal processing, and wireless communication.

Duration: 12 weeks

Level: Undergraduate/PostGraduate

Course Curriculum

  • Introduction to vectors
  • Properties and applications
  • Introduction to matrices and Applications Circuits
  • Graphs, Social Networks, Traffic flow
  • Eigenvalue decomposition
  • Properties and Applications
  • Principal component analysis (PCA)
  • Eigenfaces for facial recognition
  • Singular value decomposition (SVD) and Applications
  • Beamforming in MIMO
  • Dimensionality reduction
  • Rate maximization in wireless
  • MUSIC algorithm
  • Linear regression and Least Squares.
  • Applications: System identification
  • Linear regression
  • Support vector machines (SVM)
  • Kernel SVMs
  • Optimal linear MMSE estimation.
  • Applications MMSE Receiver
  • Market prediction and forecasting
  • ARMA models
  • NETFLIX Issue
  • Structure of FFT/ IFFT matrices, properties,
  • System model for OFDM/ SC-FDMA
  • Signal processing in OFDM systems
  • Modeling of Dynamical systems Like Robots & Chemical plants.
  • Solution of autonomous linear dynamical systems
  • Unsupervised learning: Centroid-based clustering,
  • Probabilistic model-based clustering, and EM algorithm
  • Linear perceptron. Training a perceptron stochastic gradient.
  • Compressive Sensing
  • Orthogonal matching
  • The pursuit for sparse signal estimation
  • Discrete-time Markov chains ApplicationsForecasting
  • Operations
  • Inventory management.

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3.   Introduction to Data Analytics, IIT Madras

Data analytics is the study of data analysis to transform information into practical understanding This information could be helpful. better comprehend our reality, and in many cases allow us to

make more informed choices. Although this is a wide and lofty goal, Over the past 20 years, the cost of gathering has dramatically decreased. storing and processing data to increase motivation for using empirical methods to solve problems. This course aims to expose you to a variety of data analytics methods and is organized to follow the general shapes of the several data analytics techniques, including descriptive, inferential, analytics that is predictive and prescriptive

Pre-requisites:

You must have a basic understanding of calculus and linear algebra from high school to be successful in this program. It would be ideal to have some familiarity with programming, statistics, and probability.

Course Curriculum

  1. Descriptive Statistics
  • Introduction to the course
  • Descriptive Statistics
  • Probability Distributions
  1. Inferential Statistics
  • Inferential Statistics through hypothesis tests
  • Permutation & Randomization Test
  1. Regression & ANOVA
  • Regression ANOVA(Analysis of Variance)
  1. Machine Learning: Introduction and Concepts
  • Differentiating algorithmic
  • Model-based frameworks Regression
  • Ordinary Least Squares
  • Ridge Regression
  • Nearest Neighbours
  • Regression & Classification
  •  Lasso Regression
  1. Supervised Learning with Regression and
  • Bias-Variance Dichotomy
  • Model Validation Approaches
  • Logistic Regression
  • Linear discriminant analysis & Quadratic discriminant analysis
  • Regression and Classification Trees
  • Support Vector Machines
  1. Supervised Learning with Regression and Classification
  • Ensemble Methods: Random Forest
  • Neural Networks
  • Deep learning
  1. Unsupervised Learning
  • Clustering
  • Associative Rule Mining
  • Challenges for big data analytics
  1. Prescriptive analytics
  • Creating data for analytics through designed experiments
  • Creating data for analytics through Active learning
  • Creating data for analytics through Reinforcement learning

Criteria For Obtaining A Certificate

  • Enrollment in and use of the course are both free. However, in order to receive a certificate, you must register and take the proctored exam in person at one of the authorized testing locations.
  • The exam costs Rs. 1000 and is optional.
  • The average grade for an assignment is equal to 25% of the average of the top 8 of the total of 12 assignments that were given over the course.
  • The test score is 75 percent of the total score for the proctored certification exam.
  • The exam score added to the average assignment score yields the final grade.
  • You will not be considered for a certificate unless your average assignment grade and exam grade are both more than 10/25.
  • Even if your final score is more than 40/100, you will not receive a certificate if one of the two requirements is not completed.

FAQs

●     Are data analytics courses at NPTEL effective?

Data analytics courses at NPTEL is a great resource for expanding your knowledge base and learning new skills. The course is far more engaging and intriguing. Overall, the course is beneficial and excellent.

●     Are the data analytics courses at NPTEL exam simple?

First of all, remember that the NPTEL test is not very difficult, and that obtaining the certification of data analytics courses at NPTEL is relatively simple if you have completed the video lectures and assignments. But even if you haven’t finished all of the lessons, there are ways to pass the NPTEL test without studying.

●     Can a recent graduate work as a data analyst?

Obtain a certificate from data analytics courses at NPTEL. A systematic, structured method to obtaining the relevant skills is required, but a full-fledged degree is not required for the position of a data analyst. A project-based course is the most effective (and adaptable) approach to accomplish this.

Conclusion

We reached to the end of the article for Data Analytics Courses at NPTEL. Hope that the above article on Data Analytics Courses at NPTEL helps you to give clarity on the courses offered by NPTEL. Data analytics is one of the most desired fields by aspirants and organizations looking for professional expertise in data analytics. Data Analytics Courses at NPTEL offer top-notch education to the participants which helps them to grow in the data analytics field.

There is a severe scarcity of digital skills, as any business would confirm. One of the most in-demand talents at the moment among companies is data analytics in particular. Despite the continued demand for conventional data scientists with their in-depth knowledge, individuals with a wide awareness of data analytics concepts are now increasingly needed, regardless of the position they hold. In other words, data analytics is quickly turning into a necessary 21st-century ability. Whether you want to focus on data analytics or just safeguard your career.

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