Data Analysis & Feature Engineering
Master exploratory data analysis, data cleaning, feature engineering, and production sklearn pipelines for real-world ML projects.
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Course Content
Foundations
Core concepts and groundwork
The EDA Workflow and Checklist
Reading
Univariate Analysis — Continuous and Categorical
Reading
Bivariate Analysis — Scatter, Correlation, Crosstab
Reading
Outlier Detection — IQR, Z-Score, Isolation Forest
Reading
Automated EDA with Profiling Tools
Reading
Practice — Full EDA on a Housing Dataset
Exercise
Core Skills
Essential techniques and patterns
Missing Data — Mechanisms and Strategies
Reading
Encoding Categorical Variables
Reading
Feature Scaling — Standard, MinMax, Robust
Reading
Imbalanced Data — Resampling and SMOTE
Reading
Duplicate Detection and Data Consistency
Reading
Practice — Build a Data Cleaning Pipeline
Exercise
Applied Practice
Hands-on, real-world scenarios
Creating Features from Domain Knowledge
Reading
Date-Time Feature Extraction
Reading
Polynomial and Interaction Features
Reading
Binning, Log, and Power Transforms
Reading
Text Feature Extraction — Bag-of-Words and TF-IDF
Reading
Practice — Feature Engineering for Churn Prediction
Exercise
Advanced Topics
Deeper, more complex material
Filter Methods — Correlation and Chi-Square
Reading
Wrapper Methods — Recursive Feature Elimination
Reading
Embedded Methods — Lasso and Tree Importance
Reading
PCA — Principal Component Analysis
Reading
t-SNE and UMAP for Dimensionality Reduction
Reading
Practice — Build a Feature Selection Pipeline
Exercise
Production & Scale
Building for the real world
Pipeline and ColumnTransformer
Reading
Custom Transformers with BaseEstimator
Reading
Saving and Loading Pipelines with joblib
Reading
Data Versioning with DVC
Reading
Testing ML Pipelines with pytest
Reading
Practice — Build a Reusable Production Pipeline
Exercise
Mastery & Capstone
Projects and final review
Capstone Project Brief — Cricket Streaming Churn
Reading
Phase 1 — EDA and Data Quality Audit
Exercise
Phase 2 — Clean, Encode, and Engineer Features
Exercise
Phase 3 — Feature Selection and Pipeline Construction
Exercise
Phase 4 — Final Submission and Course Reflection
Project
100% Free · All lessons unlocked
Topic Overview
What you'll learn
- Core concepts and fundamentals of Data Analysis & Feature Engineering
- Industry best practices and design patterns
- Hands-on exercises with real-world scenarios
- Performance optimization and advanced techniques
- Building production-ready applications