Still building models with default settings and hoping they hold up? Feature engineering and proper validation are what separate a model that works in a notebook from one that survives contact with real data. This is the course that gets you there.
Go from dashboards to decisions — feature engineering, statistical inference, A/B testing, and applied ML with scikit-learn, building toward a capstone that mirrors a working data scientist's first real project.
By the time you finish this course, you will have feature-engineered a real dataset, designed and interpreted an A/B test, built and validated a model with scikit-learn, and delivered a stakeholder-facing report defending your findings — the same workflow data scientists run in their first few months on the job. This course is how you get there in 8 weeks, building directly on Data Analytics Foundations rather than re-teaching it.
Syllabus
Phase 1: Feature Engineering, Inference & Experimentation (Weeks 1–4)
Modules
Feature Engineering
Advanced Pandas
Statistical Inference
A/B Testing
Applied ML for Data Science
Phase 1 Applied Lab
Phase 2: Modeling, Storytelling & Capstone (Weeks 5–8)
Modules
Model Building & Validation
Data Storytelling
Capstone Project
Full session-by-session breakdown (all 24 sessions, 2-hour format) is in the downloadable curriculum PDF linked from the hero.
Outcome
By the end of this course, you will be able to:
- Engineer features from raw data that meaningfully improve model performance
- Design and analyze A/B tests, including sample size, significance, and common pitfalls
- Apply hypothesis testing correctly — knowing which test fits which question
- Build and validate regression and classification models with scikit-learn
- Handle real-world data problems: imbalanced classes, missing data, outliers
- Communicate model output and statistical findings as a business narrative, not a notebook dump
- Build lightweight, stakeholder-facing dashboards with Streamlit or Plotly
- Deliver a capstone project that mirrors what data scientists actually do on the job
- Career roles you'll be ready for: Data Scientist, Product/Insights Analyst, Advanced Data Analyst
Tools
Python
Pandas
NumPy
Scikit-learn
Jupyter Notebook
SciPy
Streamlit
GitHub
Who Should Enrol
Already through Data Analytics Foundations?
This is exactly where you pick up: no repeated SQL or Excel, straight into feature engineering and modelling.
Comfortable with SQL/Excel, self-taught in Python?
We'll formalize what you've picked up ad hoc and take it into feature engineering, A/B testing, and real ML workflows.
Working as an analyst, want to move into a data scientist role?
This course is built around that exact transition — the statistical and ML skills that separate the two titles on a job description.
Product manager or growth marketer who keeps hearing “run an A/B test”?
Learn to design and read one properly, instead of trusting whichever number looks best.
Market Growth
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FAQs
Analysts and Data Analytics Foundations graduates who want to move into a data science role — building models, running experiments, and working from statistical evidence, not just dashboards.
It’s recommended. This course assumes you’re already comfortable with SQL, Excel/Python basics, and descriptive statistics, and builds directly on top of that — it doesn’t re-teach the fundamentals.
No. Roughly half the course is feature engineering, statistical inference, and A/B testing — the skills that make a model or an experiment trustworthy, not just a model that runs.
A capstone project that mirrors what data scientists do on the job: feature-engineer a real dataset, build and validate a model or analysis, and deliver a stakeholder-facing report defending your recommendation.
The AI & ML Pathway is a deep-learning and deployment track (Python, classical ML, deep learning, model deployment). Applied Data Science is analytics-first — feature engineering, statistical inference, A/B testing, and business storytelling — built for data science and product analytics roles, not ML engineering.