Business Data Management
How real companies organise, model and reason about their data — from operational systems to analytical warehouses.
§ 1Calendar
- Quiz 1—
- Quiz 2—
- End-term examination—
§ 2Previous Year Questions
No papers uploaded yet.
§ 3Resources
Course material2
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Supplementary content (Drive folder)
drive.google.comIITM login required
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Course YouTube channel
youtube.com
Notes3
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Course notes
Notes and study material for this course.
iitmbsdegree.inby IITM BS Students' Corner -
Topic-wise notes
Topic-wise notes on ML Study Hub.
degreedost.comby ML Study Hub -
BDM notes on Medium
Written notes for the Business Data Management course.
medium.comby Pratham Bhalla
Syllabus
The official course syllabus, transcribed from the IITM BS Data Science & Applications course booklet.
§ 1Course Description
A significant share of data and problem statements in data science arises from the business domain. This course provides a foundational understanding of how businesses are structured and operate from a data perspective.
Lectures-Tutorials-Practicals-Credits: 4-0-0-4
§ 2Faculty
- Prof. G Venkatesh, Director, School of Technology, Dhirubhai Ambani University
- Dr. Milind Gandhe, Machine Intelligence and Robotics (MINRO) COE, IIIT Bangalore
§ 3Week-by-week
Week 1: Introduction to economics - economic flow, demand and supply; Spreadsheets; Spreadsheet functions; Economics applications using Excel.
Week 2: External data sources and survey methods; Exploring demographic data; Analysing demographic data.
Week 3: Market size estimation.
Week 4: Pivot tables; Data visualisation; Market share and aspirational data analysis.
Week 5: E-commerce - case study and data analysis; Key questions and data review; Pareto analysis (volume and revenue); Scatter plots; Trend analysis.
Week 6: Sales and trend analysis; Inventory and ledger metrics; VLOOKUP and pivot tables; Key learnings.
Week 7: Manufacturing case study - data (sales, production, inventory); Revenue and portfolio analysis; Regional planning.
Week 8: Production and inventory analytics - scheduling, scrap, OEE; Profitability and margins; Safety stock and reordering.
Week 9: HR and recruitment - sourcing, selection, ranking; Channel effectiveness; Diversity analysis; Onboarding.
Week 10: Fintech and payments; Credit case study; Nudge economics; Data and segment analysis; Behavioral insights.
Week 11: A/B testing; Credit risk analysis; Dataset exploration and review; Monthly trend analysis.
Week 12: Business operations and management; Spreadsheet-based data processing.