What you'll walk away with
Six outcomes this course is actually built around — not a syllabus dump.
Statistical thinking
Hypothesis testing and inference that hold up to scrutiny.
Data wrangling
Clean and reshape real, messy datasets with pandas.
Machine learning
Regression, classification and ensembling, and when to use which.
Visualization & storytelling
Turn analysis into charts and narratives people act on.
Experimentation
Design and read A/B tests without fooling yourself.
Ship a capstone
An end-to-end analysis and model, presented like a real stakeholder deck.
Your learning path
Five stages, one flowing track — click a stage in the curriculum below and watch it light up here.
Curriculum
5 modules. Click any module to expand it — and to highlight its stage on the roadmap above.
- Descriptive statistics and probability foundations
- Hypothesis testing and confidence intervals
- Common pitfalls: p-hacking and correlation vs causation
- Lab: analyze a real survey dataset
- Cleaning, merging and reshaping messy data
- Handling missing values and outliers
- Exploratory data analysis workflows
- Lab: wrangle an open government dataset
- Regression and classification fundamentals
- Model evaluation, overfitting and cross-validation
- Ensembling: random forests and gradient boosting
- Lab: build and tune a predictive model
- Designing valid A/B tests
- Reading results without fooling yourself
- Communicating uncertainty to stakeholders
- Lab: design an experiment for a product change
- Data visualization principles that persuade
- Building a stakeholder-ready presentation
- Deploying a simple model demo
- Capstone: end-to-end analysis and model
Weekly schedule
Live sessions run evenings IST. Filter by day, or just watch — today's slot highlights itself.
Your instructor
Nisha Mehta
Ex-Data Scientist, retail & fintech analyticsNisha has led analytics teams at two fintech companies and has mentored over 500 aspiring data scientists.