Python for Data Science in 100 Exercises: Beginner to Practitioner

Turn curiosity into capability—one exercise at a time. This hands-on workbook takes you from zero to confident data practitioner through 100 carefully sequenced challenges that mirror real data-science tasks. Each exercise includes a clear Problem, a concise Explanation, and a clean Python Solution written one statement per line for easy reading and review. Syntax-colored code and a practical, no-fluff style help you learn faster and retain more.

What you’ll learn
Core Python & NumPy: array creation, reshaping, masking, vectorization, statistics

Pandas essentials: DataFrames, joins, grouping, pivoting, missing data, time series

Visualization: Matplotlib & Seaborn for line, bar, hist, box, heatmaps, pair plots, FacetGrid

Data prep & feature engineering: scaling, encoding, imputation, polynomial features, TF-IDF

Modeling with scikit-learn: linear/logistic regression, kNN, trees, random forests, SVM, boosting, clustering (KMeans, Agglomerative, DBSCAN), PCA, t-SNE

Evaluation & validation: train/val/test splits, cross-validation, confusion matrix, ROC-AUC, PR-AUC, regression metrics, leakage pitfalls

Why this book works
Learn by doing: 100 bite-sized, real-world exercises instead of long theory chapters

Clarity first: one-statement-per-line solutions; consistent, modern Python style

Progressive structure: start simple, build to full ML workflows and analysis patterns

Production habits early: pipelines, ColumnTransformer, stratified splits, reproducibility

Who this is for
Absolute beginners aiming for a practical start in data science

Students who want more practice than lectures provide

Developers from other languages seeking a fast, pragmatic Python refresh

What’s inside
KDP-friendly formatting and code blocks with syntax highlighting

Compact explanations that emphasize why a solution works, not just what to type

Coverage of everyday analyst/ML tasks—from cleaning CSVs to tuning models with GridSearchCV

Prerequisites: Basic computer literacy. No prior data science required. A modern Python environment (e.g., Jupyter/Colab/VS Code) is recommended.

If you learn best by rolling up your sleeves and building skill through repetition, this book is your structured path from first steps to practical confidence in Python for data science.