Learning Data Science is not just about building models.
The real skill is knowing which problem to solve, which data to use, which approach to choose, and how to turn model output into a business decision.
This self-paced program is designed to give you that experience through 6 industry-inspired Data Science case studies.
Instead of following generic tutorials, you’ll work on problems inspired by real business scenarios across EdTech, Retail, FoodTech, Mobility, Search & E-commerce, and FinTech.
01 — Personalized Learning Path Recommender
Build a recommendation system that identifies what a learner should study next based on their learning behaviour and performance.
02 — Smart Inventory Demand Predictor
Predict future product demand and help businesses make better inventory decisions.
03 — Restaurant Failure Prediction Engine
Analyze restaurant/business factors and build a predictive model to identify potential failure risks.
04 — Dynamic Pricing Optimizer for Ride Platforms
Use data-driven modelling to understand demand patterns and design smarter pricing strategies.
05 — Search Result Ranking Quality Predictor
Predict and evaluate search-result quality to understand how ranking systems can improve user experience.
06 — BNPL Default Risk Predictor
Build a financial risk model to identify customers who may have a higher probability of defaulting.
Most Data Science learners ask: “Which algorithm should I use?”
Industry professionals first ask: “What problem are we actually trying to solve?”
This program is designed around that mindset.
You won't just build models.
You'll practice thinking like a Data Scientist solving business problems.
Learn → Analyze → Build → Evaluate → Recommend
Build projects that demonstrate not just your technical skills, but your Data Science thinking.