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Advanced Certification in Data Analytics for Business

Jumpstart your career with 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 Programming Knowledge Required

Ranked #1 Data Science Program by India TV

Upskill for Your Dream Job

Learning Format

Online Bootcamp

Live Classes

7 Months

Career Services

by Intellipaat

CCE IIT Madras



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
Soft Skills Essential Training
50+ Live sessions for a period of 7 months
Learn from IIT Madras Faculty & Industry Practitioners
Career Services by Intellipaat
3 Guaranteed Interviews by Intellipaat
Designed for Working Professionals & Fresher's
No Cost EMI Option
2 Days campus immersion at IIT Madras

Free Career Counselling

We are happy to help you 24/7

About IIT Madras Digital Skills Academy

IIT Madras Digital Skills Academy has initiated various programs in partnership with NASSCOM. The courses offered by them aim to upskill millions of students and professionals in trending technologies through a blend of theoretical and hands-on knowledge and are taught by leading academicians.

Upon Completion of this course, you will:

  • Receive an Advanced Certification in Data Analytics for Business from IIT Madras center for continuing education
  • Receive live lectured from IIT Madras Faculty & Industry Experts

Career Transition

55% Average Salary Hike

$1,20,000 Highest Salary

12000+ Career Transitions

400+ Hiring Partners

Career Transition Handbook

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
  • Fresher’s who aspire to build their career 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 so as 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.

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Skills to Master


Data Wrangling

Data Analysis

Prediction algorithms

Data visualization

Time Series

Machine Learning


Advanced Statistics

Data Mining

R Programming

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Tools to Master

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Meet Your Mentors

Interested in This Program? Secure your spot now.

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


Live Course

Learning about various Data Manipulation and Data Handling techniques with the help of the following: 

1. MS-Excel – 

  • Excel Fundamentals 
    1. Reading the Data, Referencing in formulas , Name Range, Logical Functions, Conditional Formatting, Advanced Validation, Dynamic Tables in Excel, Sorting and Filtering 
    2. Working with Charts in Excel, Pivot Table, Dashboards, Data And File Security 
    3. VBA Macros, Ranges and Worksheet in VBA 
    4. 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

2. Python

  • Introduction to Python and IDEs – The basics of the python programming language, 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.

1. SQL Basics – 

  • Fundamentals of Structured Query Language
  • SQL Tables, Joins, Variables 

2. Advanced SQL –  

  • SQL Functions, Subqueries, Rules, Views
  • Nested Queries, string functions, pattern matching
  • Mathematical functions, Date-time functions, etc. 

3. Deep Dive into User Defined Functions

  • Types of UDFs, Inline table value, multi-statement table. 
  • Stored procedures, rank function, SQL ROLLUP, etc. 

4. SQL Optimization and Performance

  • Record grouping, searching, sorting, etc. 
  • Clustered indexes, common table expressions.

Hands-on exercise:

Writing comparison data between past year to present year with respect to 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). 

1. What is Data ScienceSignificance of Data Science in today’s data-driven world, its applications of the lifecycle, and its components Introduction to R programming and RStudio

Case Study 

This case study will cover the following concepts: 

  • Implementing simple mathematical operations and logic using R operators, loops, if statements, and switch cases.

2. Data Exploration – 

  • Introduction to data exploration
  • Importing and exporting data to/from external sources
  • What are data exploratory analysis and data importing?
  • DataFrames, working with them, accessing individual elements, vectors, factors, operators, in-built functions, conditional and looping statements, user-defined functions, and data types

Case Study

This case study will cover the following concepts:

  1. Accessing individual elements of customer churn data.
  2. Modifying and extracting results from the dataset using user-defined functions in R.

3. Data Manipulation 

  • Need for data manipulation
  • Introduction to the dplyr package
  • Selecting one or more columns with select(), filtering records on the basis of a condition with filter(), adding new columns with mutate(), sampling, and counting
  • Combining different functions with the pipe operator and implementing SQL

Case Study 

This case study will cover the following concepts:

  1. Implementing dplyr.
  2. Performing various operations for manipulating data and storing it.

1. Descriptive Statistics – 

  • Measure of central tendency, measure of spread, five points summary, etc. 

2. Probability 

  • Probability Distributions, Probability in Business Analytics
  • Probability Distributions, Binomial distribution, Poisson distribution, bayes theorem, central limit theorem. 

3. Inferential Statistics –  

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

4. Case Study

This case study will cover the following concepts:

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

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

  • Finance
  • Marketing
  • Retail
  • Supply Chain

2. Understanding the business problems and formulating hypothesesLearn about formulating hypotheses for various business problems on samples and populations. 

3. Exploratory Data Analysis to Gather insightsLearn about the exploratory data analysis and how it enables a fool proof producer of actionable insights. 

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

5. Case Study

This case study will cover the following concepts:

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

1. Regression

  1. Linear & Logistic Regression, Exploratory Data Analysis, Handling unbalanced data.
  2. Dimensionality reduction techniques, Model validation, Bias variance trade-off.

2. Trees(Decision Tree And Random Forest)

  1. Modeling the relationship within data using linear predictor functions.
  2. Implementing linear and logistics regression in R by building a model with ‘tenure’ as the dependent variable.
  3. Implementing predictive analytics by describing data.
  4. Explaining the relationship between one dependent binary variable and one or more binary variables.
  5. Using glm() to build a model, with ‘Churn’ as the dependent variable.
  6. Implementing random forest for both regression and classification problems.
  7. Building a tree, pruning it using ‘churn’ as the dependent variable, and building a random forest with the right number of trees.
  8. Using ROCR for performance metrics.
  9. k means clustering, Applications of Unsupervised (Market Basket Analysis, Segmentation).

3. Case Study

This case study will cover the following concepts:

  1. Deploying unsupervised learning with R to achieve clustering and dimensionality reduction.
  2. K-means clustering for visualizing and interpreting results for the customer churn data.
  3. Hyperparameter optimization and Deploying association analysis as a rule-based Machine Learning method.
  4. Identifying strong rules discovered in databases with measures based on interesting discoveries.

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

2. Working with data in KNIMELearn about creating workflows, loading datasets in KNIME, etc.

3. Loops in KNIMELearn about the loops in KNIME that enables efficient data transformation in KNIME

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

5. Feature Selection, Hyperparameter optimization in KNIMELearn about hyperparameter optimization, feature selection in KNIME that will enable efficient machine learning models.

6. Case Study:

This case study will cover the following concepts:

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

1. Introduction to Machine learning 

  • Supervised, Unsupervised learning.
  • Introduction to machine learning libraries in R programming. 

2. Regression 

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

3. Classification 

  • Introduction to classification problems, Identification of a classification problem, dependent and independent variables.
  • How to train the model in a classification problem.
  • How to evaluate the model for a classification problem.
  • How to optimize the efficiency of the classification model. 

4. Clustering 

  • Introduction to clustering problems, Identification of a clustering problem, dependent and independent variables.
  • How to train the model in a clustering problem.
  • How to evaluate the model for a clustering problem.
  • How to optimize the efficiency of the clustering model.

5. Supervised Learning – Linear regression, logistic regression, decision tree, random forest, support vector machine, gradient descent, K-nearest neighbor, time series forecasting, etc. 

6. Unsupervised LearningK-means clustering, dimensionality reduction, linear discriminant analysis, principal component analysis, etc. 

7. Performance Metrics – Classification reports, recall, precision, f-support, confusion matrix, true positive, false positive, true negative, false negative, r2, adjusted r2, mean squared error, etc.

8. Case Study:

This case study will cover the following concepts:

  • Predictive machine learning models in a supervised and unsupervised learning setup using algorithms like linear regression, logistic regression, random forest, decision tree, k-means, KNN, time series forecasting, LDA, PCA, etc.

1. Power BI Basics

  • Introduction to PowerBI, Use cases and BI Tools , Data Warehousing, Power BI components, Power BI Desktop, workflows and reports , 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.

2. DAX 

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

3. 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

Hands-on Exercise:

This case study will cover the following concepts:

  • Creating a dashboard to depict actionable insights in sales data.

1. Introduction to Presto – Learn about presto, what it is used for, and how the distributed SQL query engine works towards analytics and big data. 

2. Data Transformation using Presto – 

  1. Combined ETL and AI workloads. 
  2. Querying Data from the source, etc. 

1. 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. 

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

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

4. Evaluation MetricsYou 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. 

5. 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.

1. 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. 

2. 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 given time period(weeks, months, years, etc.)

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

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

5. HR AnalyticsBased 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. 

6. Dimensionality ReductionTo 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. 

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

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

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

10. Disease PredictionA 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 early detection of various diseases. 

11. Image Classification – The case study will entail working with image data, and how simple machine learning techniques can be useful to recognize image data at the behest of well trained and optimized machine learning models.

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Program Highlights

50+ Live Sessions for a period of 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



Career Services By Intellipaat

Career Services

Career Oriented Sessions

Throughout the course

Over 20+ 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 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 learners’ educational background, past experience, and future career aspirations.


3 Guaranteed Interviews

After 80% of the course completion

Guaranteed 3 job interviews upon submission of projects and assignments. Get interviewed by our 400+ hiring partners.


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, 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 26th June 2022

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 26th June 2022 08:00 PM IST Weekend (Sat-Sun)
Regular Classes 26th June 2022 08:00 PM IST Weekend (Sat-Sun)

Frequently Asked Questions

What is the ranking of IIT Madras?

IIT Madras has been ranked no.1 as per the NIRF 2020 ranking list in both the ‘overall’ and ‘engineering’ colleges category. The institute has been receiving 1st rank for 5 consecutive years.

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 IIT Madras.

This certification in Data Science and Business Analytics is conducted by leading experts from IIT Madras and Intellipaat who will assist you in kick-starting your career in these domains through the vast industry-relevant experience that they carry.

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 2-day campus immersion module at IIT Madras 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 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.

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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

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