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Jumpstart your career with CCE, 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.
Upskill for Your Dream Job
CCE, IIT Madras
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.
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About CCE, 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:
Marketing Data Analyst | India
Thanks to Intellipaat, I was able to shift from a Data Analyst to a Marketing Data Analyst with a 35% salary hike and gained a deep understanding of Analytics.
Marketing Data Analyst
Data Analyst | India
I successfully transitioned from an Electrical Engineer to a Data Scientist in a few months after enrolling in this training from Intellipaat. I received great learning support and rich study material. Thank you Intellipaat.
B. Tech — Electrical, EC Engineering
Executive | India
After developing an interest in Data Analysis, I enrolled in the training. I am glad I got a job in this domain and shift from a non-tech role to a Data Analyst. Also, I am now much more confident in data analysis than before.
Associate , Client support
Data Engineer | Bengaluru
Intellipaat helped me to acquire a solid job in the third year of BTech. I received seven job offers, with 30 LPA as the highest CTC. Thanks to Intellipaat for making my career successful.
Big Data Developer | Dallas
The course helped me make a career transition from Computer Technical Specialist to Big Data developer with a 60% hike. The online interactive sessions by trainers are the best thing about Intellipaat.
Computer Technical Specialist
Big Data Developer
Data Scientist | Bengaluru
Intellipaat offered me the best learning experience, and today, I have successfully transitioned from a Manager in Edutech to a Data Scientist. The comprehensive curriculum and the career guidance sessions helped me a lot.
Manager Analytics & Data Science
Program Manager | Pune
Thanks to Intellipaat, I was able to switch to the role of a Program Manager from a Microsoft Dynamics Consultant. Gaining knowledge of the latest technologies as per industry standards helped me the most.
Microsoft Dynamics Consultant
Data Scientist | Hyderabad
Today, I am a certified Data Scientist and this happened post I enrolled in this program. It covers all the necessary concepts and techniques in lucid language. I am thankful to Intellipaat for giving me this career growth.
Aircraft Maintenance Engineer
Senior Software Engineer | India
Post the training, I got a job as a Software Engineer- Data Scientist with a 150% salary hike in Wipro. This course helped me build strong foundations and exposure to real-time projects.
Senior Software Engineer
Product Manager | India
With this course, I switched to the Product Manager role with an 85% hike. They conducted so many interviews, live capstone projects, and resume-building strategies that gave me confidence.
Data Science Intern | Gurugram
I was able to achieve my goal of becoming a Data Science professional from being a lecturer, due to Intellipaat. It offered a platform to learn Data Science from the best trainers. The real-life projects were good.
Data Science Intern
55% Average Salary Hike
$1,20,000 Highest Salary
12000+ Career Transitions
400+ Hiring Partners
Use data analysis and data processing to understand business challenges and offer the best solutions to the organization.
Extract data from the respective sources to perform business analysis, and generate reports, dashboards, and metrics to monitor the company’s performance.
Create blueprints for managing data so as to facilitate easy integration, centralization, and protection of the database along with due security precautions.
Build a cross-brand and robust strategy of data acquisition and analytics, along with designing raw data transformation for analytical application.
Design and build Machine Learning models to derive intelligence for the numerous services and products offered by the organization.
With the help of several Machine Learning tools and technologies, build statistical models with huge chunks of business data.
Dr. Tangirala is a professor at IIT Madras with expertise in analytical chemistry and a keen interest in the fields of Controller Performance Monitoring, Time frequency analysis of Processes, and Fuel cells, etc.
He works as an assistant professor for the Biotech department at IIT Madras and holds a DSc from EPFL, Switzerland. His research interests span from modelling and optimization of biological networks to Machine Learning.
Dr. Babji Srinivasan is working as an assistant professor in the Department of Applied Mechanics at IIT Madras. He holds a Ph.D. from Texas Tech University, and his research interests span from Behavioral Informatics to Cognitive Systems Engineering.
Vinayak has over 13+ years of experience and specializes in Data Modeling , preparing KPIs, SQL query, Functional Dashboards with Qlik, Power BI, Sentiment Analysis, etc along with holding a CSM (Certified Scrum Master) certification.
A fellow of the Indian National Academy of Engg.,his research area includes data mining, process design & optimization, FDD etc. He has co-authored several reconciliation papers, a book and a course ‘Data Science for Engineers’ on the NPTEL platform.
Suresh Ramadurai is involved in teaching market research as a visiting faculty at various IIMs and in training analytics professionals. He is an IIT Delhi graduate with a degree in Chemical Engineering and holds a PG Dip. in Management from IIM Bangalore.
The application is free and takes only 5 minutes to complete.
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).
Excel For Data Analytics
Data Visualization with Excel
Ensuring Data and File Security
Getting started with VBA Macros
Statistics with Excel
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.
This case study will cover the following concepts:
Introduction to Machine learning
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.
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 the exploratory data analysis and how it enables a fool proof producer of actionable insights.
Data Storytelling: Narrate stories in a memorable way – Learn to narrate business problems and solutions in simple relatable format that makes it easier to understand and recall.
This case study will cover the following concepts:
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 enables 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, feature selection in KNIME that will enable efficient machine learning models.
This case study will cover the following concepts:
Feature Selection – Feature selection techniques in python that includes 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 increase the efficiency of the machine learning models.
Introduction to Data Warehouse – Introducing Data Warehouse and Business Intelligence, understanding difference between database and data warehouse, working with ETL tools, SQL parsing.
Architecture of Data Warehouse – Understanding the Data Warehousing Architecture, system used for Reporting and Business Intelligence, understanding OLAP vs. OLTP, 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, 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
Data Visualization with Analytics
This case study will cover the following concepts:
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, The 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
Introduction to Presto – Learn about presto, what it is used for, and how the distributed SQL query engine works towards analytics and big data.
Data Transformation using Presto –
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 given 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 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 the 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 early detection of various diseases.
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
This project is made to predict the class of a flower. By analyzing the data about the petal and their dimensions, the class of the flower would be determined using a trained Machine Learning model that has optimum accuracy.
The project is meant to analyze and predict whether an individual has an annual income of more than Rs 50000 or less using various machine learning techniques, some of which are supervised and unsupervised learning.
The project will be creating a model which would help in detecting Credit Card fraud using Machine Learning subcategories like supervised, unsupervised, and reinforcement learning techniques like Neural Network and Random Forest.
The goal of this project is to find the factors that have the maximum impact on the preferences of the pre-paid model. It also aims at identifying the variables that are highly correlated with these impacting factors.
Select a pokemon whose primary type is “Grass”, secondary type is “Poison” with the maximum speed. It creates a model that predicts whether the selected pokemon is Legendary or not using Machine Learning and data analysis techniques.
This project focuses on Machine Learning by creating a predictive data model to predict the future stock prices. It also analyzes the current trends in the stock market using algorithms like Linear Regression and k-Nearest Neighbors.
Work on the comment dataset of Yelp to build a multi-headed model that can identify & categorize various types of toxic comments (obscenity, insults, threats etc.). It must allow the user to find and select the required type
This project aims at building a book recommendation engine based on past reading and buying history of an individual. It will analyze the dataset using Machine Learning techniques, R programming and its various libraries.
The project aims to analyze whether the applied loan would be approved or not by identifying the variables correlated with impacting factors. It is predicted using techniques such as Logistic Regression, Decision Tree and Random Forest.
The recommendation engine is built using Netflix prize dataset that will collect the data from various sources of a user, anticipate their preferences and will recommend movies according to the user’s behavior and choices made earlier.
I am happy to be able to complete this certification from Intellipaat. I appreciate the entire team for providing me with amazing learning experience through real-time projects and case studies.
Intellipaat provides a great learning environment inclusive of new-age technologies. I worked on many Data Science tools through live training and self-paced videos. The trainers are very supportive.
After much research and pondering, I started learning from Intellipaat. Given the great tutorials and trainers, with quick query resolution, the decision proves to be best. The experience was wonderful.
Since Data Science is a booming industry, learning it from Intellipaat has been the wisest decision. The curriculum covers all the current topics with efficient trainers and an effective support team.
The Data Science and Machine Learning course from Intellipaat was taught from the basics. The trainers are knowledgeable with clarity of practical concepts. The support team is prompt with the response.
The candidates from Intellipaat were very good. They are better than experienced people from the same domain. The learners had hands-on experience. The product managers were very happy with the job-ready recruits.
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.
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
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.
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.
Placement opportunities are provided once the learner is moved to the placement pool. Get noticed by our 400+ hiring partners.
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.
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!
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.
Tell us a bit about yourself and why you want to join this program
An admission panel will shortlist candidates based on their application
Selected candidates will be notified within 1–2 weeks
Total Admission Fee
Admissions are closed once the requisite number of participants enroll for the upcoming cohort. Apply early to secure your seat.
|Program Induction||28th Jan 2023||08:00 PM IST||Weekend (Sat-Sun)|
|Regular Classes||28th Jan 2023||08:00 PM IST||Weekend (Sat-Sun)|
|Program Induction||5th Feb 2023||10:00 AM - 01:00 PM IST||Weekend (Sat-Sun)|
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.
Intellipaat provides career services that includes Guarantee interviews for all the learners enrolled in this course. CCE IIT Madras 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 CCE, IIT Madras.
This certification in Data Science and Business Analytics is conducted by leading experts from CCE, 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 CCE, 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.
To be eligible for getting into 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 PRT (Placement Readiness Test) to get into the placement pool and get access to our job portal as well as the career mentoring sessions.
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