PROGRAM OBJECTIVES
The program is a unique blend of the disciplines of Business Analytics and Management Consulting, carefully designed by academic and industry professionals. The program has the following key features:
- To cover essentials of art and science in applying Analytics for Businesses.
- To provide managerial skills with analytical thinking in solving real-world business problems through data-driven insights.
- Practical training on analytical tools, methodologies, and technologies.
PROGRAM HIGHLIGHTS
- Comprehensive curriculum consisting of globally benchmarked additional reading materials like HBR cases and uniquely designed assignments to promote practical rigor in every subject.
- Electives across various domains that are highly industry-relevant, intricately linked to job requirements and include the latest tools and techniques utilized by various industries on global platforms.
- Opportunities for relevant industry projects and internships across different domains with a focus on analytics.
Course Structure
|
YEAR I |
|
|
TERM I (June - Sep) |
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|
Sno |
Course Title |
|
1 |
Statistics for Data Analytics |
|
2 |
Marketing Management |
|
3 |
Artificial Intelligence for Business |
|
4 |
Data Wrangling with Python |
|
5 |
Story Telling with Data |
|
6 |
Mathematics for Business Analytics |
|
7 |
Effective Presentation Skills |
|
8 |
Structured Thinking |
|
TERM II (Sep - Dec) |
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|
Sno |
Course Title |
|
1 |
Data Analysis for Decision Making |
|
2 |
Economics for Managers |
|
3 |
Managing People at Work |
|
4 |
Machine Learning Techniques |
|
5 |
Advanced Python Programming |
|
6 |
Data Management |
|
7 |
Soft Skills and Email Etiquette |
|
8 |
Data Analytics with R |
|
TERM III (Jan - Mar) |
|
|
Sno |
Course Title |
|
1 |
Multivariate Techniques |
|
2 |
Managerial Accounting |
|
3 |
Operations Management |
|
4 |
Datapreneurship |
|
5 |
Natural Language Processing |
|
6 |
Deep Learning |
|
7 |
Advanced Machine Learning Techniques |
|
8 |
Big Data Analytics |
|
9 |
Analytics Project-1 |
|
10 |
Report Writing for Analytics Project |
|
11 |
Open Source Emerging Tools for Analyticians |
|
YEAR II |
|
|
TERM IV (June - Sep) |
|
|
Sno |
Course Title |
|
1 |
Corporate Finance |
|
2 |
Strategic Management |
|
3 |
Machine Learning for Time Series Forecasting |
|
|
Data Governance |
|
5 |
Advanced Deep Learning |
|
6 |
Advanced Big Data Analytics |
|
7 |
Analytics Project-2 |
|
8 |
Data Analysis with Analytical Software Suite |
|
TERM V (Sep - Dec) |
|
|
Sno |
Course Title |
|
1 |
Stochastic Processes for Analyticians |
|
2 |
Analytics using Simulation Models |
|
3 |
Web Analytics |
|
|
BlockChain for Managers |
|
5 |
Optimization Analytics |
|
6 |
Functional Elective-1 |
|
7 |
Functional Elective-2 |
|
8 |
Analytics Project-2 |
|
TERM VI (Jan - Mar) |
|
|
Sno |
Course Title |
|
1 |
Functional Elective-3 |
|
2 |
Cross Functional Elective-1 |
|
3 |
Cross Functional Elective -2 |
|
4 |
CAPSTONE LIVE PROJECT~ |
|
SUMMER TERM (April to June) - 2.5 months |
|
|
Sno |
Course Title |
|
1 |
SUMMER INTERNSHIP PROGRAM |
|
Cross Functional Electives (CE) |
|
Functional Elective Track (FE) |
|
HR Track |
||
|
CFE : Track - 1 |
Work Force Analytics |
|
|
Business Negotiation |
Compensation Analytics |
|
|
International Business |
People analytics |
|
|
CFE : Track -2 |
Finance Track |
|
|
Strategic Implementation and Control |
Risk Analytics |
|
|
Managing Strategic Change |
Fraud Analytics |
|
|
Functional Elective Track (FE) |
Portfolio Analytics |
|
|
GENERAL Track |
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|
Marketing Track |
Healthcare Analytics |
|
|
Retail Analytics |
Pharma Analytics |
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|
Pricing Analytics |
Sports Analytics |
|
|
CRM Analytics |
Telecom Analytics |
|
|
Operations Track |
Energy Analytics |
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|
SCM Analytics |
|
|
|
Logistics Analytics |
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|
Service Analytics |
Eligibility Criteria and Intake
- For the MBA - Business Analytics, Mumbai, Bachelor's Degree (10+2+3 or more years of graduation) in Engineering, Mathematics, Statistics, Commerce, Economics, Management Studies, or Computer Science with a minimum of 50% marks in aggregate (all the subjects for which the candidate has taken & appeared) ) plus two-years of industry experience is desirable. Mathematics or Statistics must be a compulsory subject in graduation.
- * Sub clauses 3.1 to 3.7 are also applicable
- Preferred 1 year of full-time and relevant work experience as on commencement of program.
- Qualifying NMAT Scores, Group Discussion (GD) and Personal Interview (PI).
- Total intake of 120-140