What should be the subjects & course structure for teaching Data Analytics / Data Science in MBA?

Data Science & Analytics including Operations / Decision Science are evolving fields which are in demand currently for various reasons. Most companies are experimenting and creating projects / products around analytics / data science. I am listing the subjects & courses that an MBA student should take to cover Data Science / Analytics:

1. Mathematics — Intermediate level statistics, linear algebra, discreet mathematics & basic calculus
2. Introduction to Business Analytics & Data Science — covering basics of the subjects like what is machine learning, artificial intelligence, major software / products, data science / analytics basics including various types of data, sentiment analysis, basics of algorithms and contemporary topics
3. BigData ecosystem — Concepts of Hadoop, Spark, MapReduce, NoSQL and the ecosystem around it
4. Business Intelligence — covering reporting, dash-boarding, visualization and contemporary topics around it
5. Business Analysis — covering concepts of how to collect requirements, build a project plan / statement of work, proposals, proof of concepts, concepts of AGILE / DevOps, data analysis, business process re-engineering and similar
6. Programming in R & Python for managers — Intermediate level topics including data manipulation / cleaning, charting / visualization, running major machine learning algorithms, mathematics functions and libraries
7. Data warehousing — covering the introduction of it and multi-dimensional cubes, business dimensions, star / snowflake schema, process of ETL and similar
8. Data mining — covering major algorithms in supervised / unsupervised / semi-supervised areas and their implementation
9. Cloud computing — covering cloud architecture, offerings & major product companies
10. Operations subjects — which should include Operations management, Operations research, Project Management, Logistics & Supply Chain management, Total Quality Management
11. Case study, use case and industry driven internships and projects which give exposure to students using proprietary / open source tools & products mapped to domains like Digital marketing, Financial analytics, HR analytics, Web / Mobile analytics, Advertising, Operational Analytics, eCommerce, Manufacturing, Banking, etc. used in industry to join all of the above together into implementation
12. Above goes with an assumption that students already have intermediate level skills in productivity tools like MS-Office / Google Docs/Sheets, Linux, Year 1 general management subjects like Finance, HR, Marketing, etc.

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