Career Back To Women: Certified Professional in Basics of Data Science and Big Data Analytics

10 weeks English

Program Description

Course is targeted at learners who are interested in learning and understanding data science and what big data is all about in sufficient depth and breadth. The course will provide an overview of how to pose meaningful data analytics problems for real-life applications. At the end of the course, participants will develop a structured thinking approach to transition from data to data science problem definitions and also learn to address challenges in handling Big Data.

Introduce the participants to Python an easy to use tool for high level data analytics.

Introduce the participants to a comprehensive overview of linear algebra, statistics and optimization concepts critical concepts for the understanding of data science. 

Introduce the participants to in-depth explanation of data science algorithms supported by hands-on work in Python from an application viewpoint.

Introduce participants to visualization

Introduce the participants to the field of Big Data background and key concepts

Introduce the participants to real-life applications of data science a case study approach.

Program Fee

  • 41300.00 (inclusive of tax)

Educational Qualification

For Indian Participants - Graduates or Diploma Holders (10+2+3) from a recognized university (UGC/AICTE/DEC/AIU/State Government) in any discipline.

For International Participants - Graduation or equivalent degree from any recognized University or Institution in their respective country.

Suggested Prerequisites

Basic understanding of technology, networks and security, while not mandatory, will be an added advantage.

Successful completion of BTCUBE is a pre-requisite


10 weeks

Course Date

From 25/07/2020 to 04/10/2020


Certificate of Completion by IIT-MADRAS

Teaching Hours

40 hours

Lead Faculty

  • Prof. Raghunathan Rangaswamy and Prof. Shankar Narasimhan, IITM

Course offered by

Learning Schedule

S.No Modules
1 Python Programming
2 Introduction To Statistical Modelling
3 Data Preparation
4 Framework For Solving Data Science Problems
5 Visualization
6 Introduction To Big Data