Courses ×


Corporate Training Hire From Us Explore Courses

Advanced Certification in Data Analytics for Business

Learn from IIT Madras Faculty & Industry Experts with Campus Immersion at IIT Madras Research Park

Jumpstart your career with IITM Pravartak (A Technology Innovation Hub of IIT Madras) & Intellipaat’s advanced certification in Data Science & Business Analytics course. Master the domain with multiple business case studies and industry-relevant projects under the guidance of the esteemed IIT Madras faculty.

Only Few Seats Left No Prior Coding Experience Required!

Ranked #1 Data Science and Analytics Program by India TV

Learning Format

Online Bootcamp

Live Classes

7 Months

Campus Immersion

at IITM Pravartak

IITM Pravartak



Hiring Partners

About Program

The program led by the IIT Madras faculty aims at helping learners develop a strong skillset including descriptive statistics, probability distributions, predictive modeling, time series forecasting, data architecture strategies, business analytics, and other skills to excel in this field.

Key Highlights

400 Hrs of Applied Learning
218 Hrs of Self-Paced Learning
50+ Industry Projects & Case Studies
One-on-One with Industry Mentors
24*7 Support
1:1 Mock Interview
50+ Live sessions for 7 months
Learn from IIT Madras Faculty & Industry Practitioners
Placement Assistance
Resume Preparation and LinkedIn Profile Review
Designed for Working Professionals & Freshers
No-Cost EMI Option
2 Days Campus immersion at IIT Madras Research Park

Free Career Counselling

We are happy to help you 24/7

About IITM Pravartak Digital Skills Academy

IITM Pravartak, a Technology Innovation Hub of IIRM is funded by the Department of Science and Technology, GoI under its National Mission on Interdisciplinary Cyber-Physical Systems (NM-ICPS), focuses on application-oriented research and innovation in the areas SNACS. BharOS, India’s first mobile operating system is developed by an IITM Pravartak incubated company.

Key Achievements of IIT Madras:

  • Ranked No: 1 in India in both ‘Overall’ and ‘Engineering’ Categories in NIRF 2022 for Four consecutive years.
  • IIT Madras has been identified as an ‘Institution of Eminence’ by the Government of India.
  • Ranked No. 4 Indian Institute in QS World University Ranking and Ranked #250 in the International QS World Rankings 2023.
Note: All certificate images are for illustrative purposes only and may be subject to change at the discretion of the IITM Pravartak.

Career Transition

55% Average Salary Hike

$1,20,000 Highest Salary

12000+ Career Transitions

400+ Hiring Partners

Career Transition Handbook

*Past record is no guarantee of future job prospects

Who Can Apply for the Course?

  • Individuals with a bachelor’s degree and a keen interest to learn Data Science and Business Analytics
  • IT professionals looking for a career transition to Data Scientists and Business Analysts
  • Professionals aiming to move ahead in their IT career
  • Data Science and Business Analysis professionals willing to validate and develop skills in the domain
  • Developers and Project Managers
  • Freshers who aspire to build their careers in the field of Business Analysis and Data Science
Who can aaply

What roles can a Data Science & Business Analysis professional play?

Data Scientist

Use data analysis and data processing to understand business challenges and offer the best solutions to the organization.

Business Analyst

Extract data from the respective sources to perform business analysis, and generate reports, dashboards, and metrics to monitor the company’s performance.

Data Architect

Create blueprints for managing data to facilitate easy integration, centralization, and protection of the database along with due security precautions.

Data Analyst

Build a cross-brand and robust strategy of data acquisition and analytics, along with designing raw data transformation for analytical application.

Applied Scientist

Design and build machine learning models to derive intelligence for the numerous services and products offered by the organization.

Machine Learning Engineer

With the help of several Machine Learning tools and technologies, build statistical models with huge chunks of business data.

View More

Skills to Master


Data Wrangling

Data Analysis

Prediction algorithms

Data visualization

Time Series

Machine Learning


Advanced Statistics

Data Mining

R Programming

View More

Tools to Master

intellipaat intellipaat intellipaat intellipaat intellipaat intellipaat intellipaat
View More

Meet Your Mentors

Interested in This Program? Secure your spot now.

The application is free and takes only 5 minutes to complete.


Live Course Self Paced Industry Expert Academic Faculty
  1. Introduction to SQL
  2. Database Normalization and Entity Relationship Model(self-paced)
  3. SQL Operators
  4. Working with SQL: Join, Tables, and Variables
  5. Deep Dive into SQL Functions
  6. Working with Subqueries
  7. SQL Views, Functions, and Stored Procedures
  8. Deep Dive into User-defined Functions
  9. SQL Optimization and Performance
  10. Advanced Topics
  11. Managing Database Concurrency
  12. Practice Session

Case Study

Writing comparison data between the past year and to present year concerning top products, ignoring the redundant/junk data, identifying the meaningful data, and identifying the demand in the future(using complex subqueries, functions, pattern matching concepts).

Introduction to Python and IDEs – The basics of the Python programming language, and how you can use various IDEs for Python development like Jupyter, Pycharm, etc.

Python Basics – Variables, Data Types, Loops, Conditional Statements, functions, decorators, lambda functions, file handling, exception handling ,etc.
Object Oriented Programming – Introduction to OOPs concepts like classes, objects, inheritance, abstraction, polymorphism, encapsulation, etc.

Data Manipulation with Numpy, Pandas, and Visualization – Using large datasets, you will learn about various techniques and processes that will convert raw unstructured data into actionable insights for further computations i.e. machine learning models, etc.

Case Study – The culmination of all the above concepts with real-world problem statements for better understanding.

Descriptive Statistics

  1. The measure of central tendency, the measure of spread, five points summary, etc.


  1. Probability Distributions, Probability in Business Analytics
  2. Probability Distributions, Binomial distribution, Poisson distribution, Bayes’ Theorem, central limit theorem

Inferential Statistics

  1. Correlation, covariance, confidence intervals, hypothesis testing, F-test, Z-test, t-test, ANOVA, chi-square test, etc.

Case Study

This case study will cover the following concepts:

  1. Building a statistical analysis model that uses quantification, representations, and experimental data
  2. Reviewing, analyzing, and drawing conclusions from the data

Introduction to Machine Learning

  1. Supervised, Unsupervised learning.
  2. Introduction to scikit-learn, Keras, etc.


  1. Introduction classification problems, Identification of a regression problem, dependent and independent variables
  2. How to train the model in a regression problem?
  3. How to evaluate the model for a regression problem?
  4. How to optimize the efficiency of the regression model?


  1. Introduction to classification problems, Identification of a classification problem, dependent and independent variables
  2. How to train the model in a classification problem?
  3. How to evaluate the model for a classification problem?
  4. How to optimize the efficiency of the classification model?


  1. Introduction to clustering problems, Identification of a clustering problem, dependent and independent variables
  2. How to train the model in a clustering problem?
  3. How to evaluate the model for a clustering problem?
  4. How to optimize the efficiency of the clustering model?

Supervised Learning

  1. Linear Regression – Creating linear regression models for linear data using statistical tests, data preprocessing, standardization, normalization, etc.
  2. Logistic RegressionCreating logistic regression models for classification problems – such as if a person is diabetic or not, if there will be rain or not, etc.

Unsupervised Learning

  1. K-means – The K-means an algorithm that can be used for clustering problems in an unsupervised learning approach
  2. Dimensionality reduction – Handling multi dimensional data and standardizing the features for easier computation

Performance Metrics

  1. Classification reports – To evaluate the model on various metrics like recall, precision, f-support, etc.
  2. Confusion matrix – To evaluate the true positive/negative, and false positive/negative outcomes in the model.
  3. r2, adjusted r2, mean squared error, etc.

Time Series Forecasting – Making use of time series data, gathering insights and useful forecasting solutions using time series forecasting

Business Domains – Learn about various business domains and understand how one differs from the other.

  1. Finance
  2. Marketing
  3. Retail
  4. Supply Chain

Understanding the business problems and formulating hypotheses – Learn about formulating hypotheses for various business problems on samples and populations.

Exploratory Data Analysis to Gather Insights – Learn about exploratory data analysis and how it enables a foolproof producer of actionable insights.

Data Storytelling: Narrate stories in a memorable way – Learn to narrate business problems and solutions in a simple relatable format that makes it easier to understand and recall.

Case Study
This case study will cover the following concepts:

  1. Create actionable insights from raw unstructured data to solve real-world business problems.

Introduction to KNIME – Learn about the KNIME tool that can be quite efficient for data analytics, creating workflows, etc.

Working with data in KNIME – Learn about creating workflows, loading datasets in KNIME, etc.

Loops in KNIME – Learn about the loops in KNIME that enable efficient data transformation in KNIME

Web scraping in KNIME – Learn about techniques in KNIME that enable web scraping to collect data directly from the web.

Feature Selection, Hyperparameter optimization in KNIME – Learn about hyperparameter optimization, and feature selection in KNIME that will enable efficient machine learning models.

Case Study:
This case study will cover the following concepts:

  • Using KNIME to create end-to-end machine learning models with various algorithms like linear regression, logistic regression, decision tree, random forest, etc.

Feature Selection – Feature selection techniques in Python that include recursive feature elimination, Recursive feature elimination using cross-validation, variance threshold, etc.

Feature Engineering – Feature engineering techniques that help in reducing the best features to use for data modeling.

Model Tuning – Optimization techniques like hyperparameter tuning to increases the efficiency of the machine learning models.

Introduction to Spark – Introduction to Spark, Spark overcomes the drawbacks of working on MapReduce, Understanding in-memory MapReduce, Interactive operations on MapReduce, Spark stack, fine vs. coarse-grained update, Spark Hadoop YARN, HDFS Revision, and YARN Revision, The overview of Spark and how it is better than Hadoop, Deploying Spark without Hadoop, Spark history server and Cloudera distribution

Spark Basics – Spark installation guide, Spark configuration, Memory management, Executor memory vs. driver memory, Working with Spark Shell, The concept of resilient distributed datasets (RDD), Learning to do functional programming in Spark, and architecture of Spark.

Spark SQL and Data Frames

1. Learning about Spark SQL
2. The context of SQL in Spark for providing structured data processing
3. JSON support in Spark SQL
4. Working with XML data
5. Parquet files
6. Creating Hive context
7. Writing data frame to Hive
8. Reading JDBC files
9. Understanding the data frames in Spark
10. Creating Data Frames
11. Manual inferring of schema
12. Working with CSV files
13. Reading JDBC tables
14. Data frame to JDBC
15. User-defined functions in Spark SQL
16. Shared variables and accumulators
17. Learning to query and transform data in data frames
18. Data frame provides the benefit of both Spark RDD and Spark SQL
19. Deploying Hive on Spark as the execution engine

Problem Statement and Project Objectives – You will learn how to formulate various problem statements and understand the business objective of any problem statement that comes as a requirement.

Approach for the Solution – Creating various statistical insights-based solutions to approach the problem will guide your learnings to finish a project from scratch.

Optimum Solutions – Formulating actionable insights backed by statistical evidence will help you find the most effective solution for your problem statements.

Evaluation Metrics – You will be able to apply various evaluation metrics to your project/solution. It will validate your approach and point towards shortcomings backed by insights, if any.

Gathering Actionable insights – You will learn about how a problem’s solution isn’t just creating a machine learning model, the insights that were gained from your analysis should be presentable in the form of actionable insights to capitalize on the solutions formulated for the problem statement.

Customer Churn – The case study involves studying the customer data for a given XYZ company, and using statistical tests and predictive modeling, we will gather insights to efficiently create an action plan for the same.

Sales Forecasting – By studying the various patterns and sales data for a firm/store, we will use the time series forecasting method to forecast the number of sales for the next time period(weeks, months, years, etc.)

Census – After studying the population data, we will gather insights and through predictive modeling try to create actionable insights on the same, it could be the average income of an individual, or most likely profession, etc.

Predictive Modeling – Various case studies on categorical and continuous data, to create predictive models that will predict specific outcomes based on the business problems.

HR Analytics – Based on the data provided by a firm, we will study the HR analytics data, and create actionable insights using various statistical tests and hypothesis testing.

Dimensionality Reduction – To understand the impact of multidimensional data, we will go through various dimensionality reduction techniques and optimize the computational time on the same that will eventually be used for various classification and regression problems.

Housing – A case study that will give you insight into how real estate firms can narrow down on pricing, customer choices, etc. using various predictive modeling techniques.

Customer Segmentation – Using unsupervised learning techniques, we will learn about customer segmentation, which can be quite useful for e-commerce sectors, stores, marketing funnels, etc.

Inventory Management – In this case study, you will learn about how meaningful insights can be used to drive a supply chain, using predictive modeling and clustering techniques.

Disease Prediction – A medical endeavor that is achieved through machine learning will give you an insight into how the predictive model can prove to be a great marvel in the early detection of various diseases.


Excel Fundamentals

  1. Reading the Data, Referencing in formulas , Name Range, and Logical
  2. Functions, Conditional Formatting, Advanced Validation, Dynamic Tables in Excel, Sorting and Filtering
  3. Working with Charts in Excel, Pivot Table, Dashboards, Data, and File Security
  4. VBA Macros, Ranges, and Worksheets in VBA
  5. IF conditions, loops, Debugging, etc.

Excel For Data Analytics

  1. Handling Text Data, Splitting, combining, data imputation on text data, Working with Dates in Excel, Data Conversion, Handling Missing Values, Data Cleaning, Working with Tables in Excel, etc.

Data Visualization with Excel

  1. Charts, Pie charts, Scatter, and bubble charts
  2. Bar charts, Column charts, Line charts, Maps
  3. Multiples: A set of charts with the same axes, Matrices, Cards, Tiles

Ensuring Data and File Security

  1. Data and file security in Excel, protecting row, column, and cell, the different safeguarding techniques.

Getting Started with VBA Macros

  1. Learning about VBA macros in Excel, executing macros in Excel, the macro shortcuts, applications, the concept of relative reference in macros, In-depth understanding of Visual Basic for Applications, the VBA Editor, module insertion and deletion, performing an action with Sub and ending Sub if condition not met.

Statistics with Excel


Introduction to Data Warehouse – Introducing Data Warehouse and Business Intelligence, understanding the difference between database and data warehouse, working with ETL tools, and SQL parsing.

Architecture of Data Warehouse – Understanding the Data Warehousing Architecture, system used for Reporting and Business Intelligence, understanding OLAP vs. OLTP, and introduction to Cubes.

Data Modeling Concepts – The various stages from Conceptual Model, Logical Model to Physical Schema, Understanding the Cubes, benefits of Cube, working with OLAP multidimensional Cube, creating Report using a Cube.

Data Normalization – Understanding the process of Data Normalization, rules of normalization for first, second, and third normal, BCNF, deploying Erwin for generating SQL scripts.

Dimension and Fact Table – The main components of Business Intelligence – Dimensions and Fact Tables, understanding the difference between Fact Tables & Dimensions, and understanding Slowly Changing Dimensions in Data Warehousing.

SQL Parsing, Cubes, and OLAP – SQL parsing, compilation and optimization, understanding types and scope of cubes, Data Warehousing Vs. Cubes, limitations of Cubes and evolution of in-memory analytics.

Power BI Basics

  • Introduction to PowerBI, Use cases and BI Tools, Data Warehousing, Power BI components, Power BI Desktop, workflows and reports, and Data Extraction with Power BI.
  • SaaS Connectors, Working with Azure SQL database, Python, and R with Power BI
  • Power Query Editor, Advance Editor, Query Dependency Editor, Data Transformations, Shaping and Combining Data, M Query, and Hierarchies in Power BI.


  • Data Modeling and DAX, Time Intelligence Functions, DAX Advanced Features

Data Visualization with Analytics

  • Slicers, filters, Drill Down Reports
  • Power BI Query, Q & A and Data Insights
  • Power BI Settings, Administration and Direct Connectivity
  • Embedded Power BI API and Power BI Mobile
  • Power BI Advance and Power BI Premium

Case Study:

This case study will cover the following concepts:

  • Creating a dashboard to depict actionable insights in sales data
  • Job Search Strategy
  • Resume Building
  • Linkedin Profile Creation
  • Interview Preparation Sessions by Industry Experts
  • Mock Interviews
  • Placement opportunities with 400+ hiring partners upon clearing the Placement Readiness Test.
  • Introduction to R
  • R Packages
  • Sorting DataFrame
  • Matrices and Vectors
  • Reading Data from External Files
  • Generating Plots
  • Analysis of Variance (ANOVA)
  • K-Means Clustering
  • Association Rule Mining
  • Regression in R
  • Analyzing Relationship with Regression
  • Advanced Regression
  • Logistic Regression
  • Advanced Logistic Regression
  • Receiver Operating Characteristic (ROC)
  • Kolmogorov–Smirnov Chart
  • Database Connectivity with R
  • Integrating R with Hadoop
  • R Case Studies
View More
Intellipaat reserves the right to modify, amend or change the structure of module & the curriculum, after due consensus with the university/certification partner.

Program Highlights

50+ Live sessions for 7 months
218 Hrs of Self-Paced Learning
30+ Industry Projects & Case Studies
24*7 Support

Interested in This Program? Secure your spot now.

The application is free and takes only 5 minutes to complete.


Projects will be a part of your Certification in Data Science & Business Analytics to consolidate your learning. It will ensure that you have real-world experience in Data Science & Business Analytics.

Practice 20+ Essential Tools

Designed by Industry Experts

Get Real-world Experience


5 ( 3,123 )

Hear From Our Hiring Partners

Career Services By Intellipaat

Career Services

Career Oriented Sessions

Throughout the course

Over 10+ live interactive sessions with an industry expert to gain knowledge and experience on how to build skills that are expected by hiring managers. These will be guided sessions and that will help you stay on track with your up skilling objective.

Resume & LinkedIn Profile Building

After 70% of course completion

Get assistance in creating a world-class resume & Linkedin Profile from our career services team and learn how to grab the attention of the hiring manager at the profile shortlisting stage

Mock Interview Preparation

After 80% of the course completion.

Students will go through a number of mock interviews conducted by technical experts who will then offer tips and constructive feedback for reference and improvement.

1 on 1 Career Mentoring Sessions

After 90% of the course completion

Attend one-on-one sessions with career mentors on how to develop the required skills and attitude to secure a dream job based on a learner’s educational background, past experience, and future career aspirations.

Placement Assistance

Upon movement to the Placement Pool

Placement opportunities are provided once the learner is moved to the placement pool upon clearing Placement Readiness Test (PRT)

Exclusive access to Intellipaat Job portal

After 80% of the course completion

Exclusive access to our dedicated job portal and apply for jobs. More than 400 hiring partners’ including top start-ups and product companies hiring our learners. Mentored support on job search and relevant jobs for your career growth.

Our Alumni Works At

Master Client Desktop

Peer Learning

Via Intellipaat PeerChat, you can interact with your peers across all classes and batches and even our alumni. Collaborate on projects, share job referrals & interview experiences, compete with the best, and make new friends – the possibilities are endless and our community has something for everyone!


Admission Details

The application process consists of three simple steps. An offer of admission will be made to selected candidates based on the feedback from the interview panel. The selected candidates will be notified over email and phone, and they can block their seats through the payment of the admission fee.

Submit Application

Submit Application

Tell us a bit about yourself and why you want to join this program

Application Review

Application Review

An admission panel will shortlist candidates based on their application


Application Review

Selected candidates will be notified within 1–2 weeks

Program Fee

Total Admission Fee

$ 1,799

Upcoming Application Deadline 1st Oct 2023

Admissions are closed once the requisite number of participants enroll for the upcoming cohort. Apply early to secure your seat.

Program Cohorts

Next Cohorts

Date Time Batch Type
Program Induction 1st Oct 2023 08:00 PM IST Weekend (Sat-Sun)
Regular Classes 1st Oct 2023 08:00 PM IST Weekend (Sat-Sun)

Frequently Asked Questions

Will IITM Pravartak help with the Career Services?

Intellipaat provides career services that include Guarantee interviews for all the learners enrolled in this course. IITM Pravartak is not responsible for the career services.

Upon completion of the Data Science and Business Analytics training and execution of the various projects in this program, you will receive a joint Advanced Certification in Data Science and Business Analytics from Intellipaat and IITM Pravartak.

The certification in Data Science and Business Analytics is conducted by leading experts from IITM Pravartak and Intellipaat who will assist you in kick-starting your career in these domains through their vast industry-relevant experience.

Also, the course curriculum along with videos, live sessions, and assignments will help you gain in-depth knowledge in Data Science and Business Analytics, apart from providing hands-on experience in these domains through real-time projects.

If you fail to attend any of the live lectures, you will get a copy of the recorded session in the next 12 hours. Moreover, if you have any other queries, you can get in touch with our course advisors or post them on our community platform.

To register for the program, you can reach out to our learning consultants or contact us through the above-given details on this page.

There will be a two-day campus immersion module at IITM Pravartak during which learners will visit the campus. You will learn from the faculty as well as interact with your peers. However, this is subject to the COVID-19 situation and guidelines provided by the Institute. The cost of travel and accommodation will be borne by the learners. However, the campus immersion module is optional.

To be eligible for the placement pool, the learner has to complete the course along with the submission of all projects and assignments. After this, he/she has to clear the Placement Readiness Test (PRT) to get into the placement pool and get access to our job portal as well as the career mentoring sessions.

You will undergo below sessions:

  • Job Search Strategy Sessions
  • Resume Building
  • Linkedin Profile Creation
  • Interview Preparation Sessions by Industry Experts
  • Mock Interviews
  • Placement opportunities with 400+ hiring partners upon clearing the Placement Readiness Test.

Please note that the course fees is non-refundable and we will be at every step with you for your upskilling and professional growth needs.

Due to any reason you want to defer the batch or restart the classes in a new batch then you need to send the batch defer request on [email protected] and only 1 time batch defer request is allowed without any additional cost.

Learner can request for batch deferral to any of the cohorts starting in the next 3-6 months from the start date of the initial batch in which the student was originally enrolled for. Batch deferral requests are accepted only once but you should not have completed more than 20% of the program. If you want to defer the batch 2nd time then you need to pay batch defer fees which is equal to 10% of the total course fees paid for the program + Taxes.

View More

What is included in this course?

  • Non-biased career guidance
  • Counselling based on your skills and preference
  • No repetitive calls, only as per convenience
  • Rigorous curriculum designed by industry experts
  • Complete this program while you work

I’m Interested in This Program

Select Currency