Course curriculum
Stage 1
Pre-Foundations
- 1120 min
Data Science Self-Assessment
- 2120 min
Math & Programming Readiness
Built for working data scientists targeting product, growth, and ML-DS loops at Meta, Netflix, Stripe, and Airbnb. You move from SQL window functions and A/B-test reads through causal inference (DiD, RDD, propensity scores), classical ML up to XGBoost and embedding-based matching, and the production statistics habits that separate senior DS from DA and MLE. Every module ends with an interview hook and a real artifact you can drop into a portfolio.
Data Science Self-Assessment
Math & Programming Readiness
Statistics Fundamentals
Python for Data Science
SQL for Analytics
Data Visualization & Storytelling
Hypothesis Testing & Inference
Regression Analysis
A/B Testing & Experimentation
Business Metrics & KPIs
A/B Testing Pitfalls & Variance Reduction
Classification & Clustering
Causal Inference
Stakeholder Communication & Influence
ML Pipelines & Production
Feature Engineering & Data Leakage
Interview Preparation for Data Scientists
Model Evaluation Beyond Accuracy
Bayesian Methods
Time Series Analysis & Forecasting
Deep Learning & NLP for Data Science
DS Case Study Frameworks
Clustered Randomization & Network-Effect Experiments
Heterogeneous Treatment Effects & Uplift Modeling
ML Observability & Drift Detection
Multi-Armed Bandits & Contextual Bandits
Privacy-Preserving Analytics: DP, k-Anonymity, Federated Learning
Sequential Testing & mSPRT
Two-Sided Marketplace Metrics & Switchback Experiments
LLM & RAG Evaluation