Summary

This textbook introduces the fundamental concepts and methods of statistical learning. It uses Python and provides a unique approach by blending theory, data examples, software code, and exercises from beginning to end for a profound yet practical introduction to statistical learning.

The book consists of three parts: The first one presents data in the framework of probability theory, exploratory data analysis, and unsupervised learning. The second part on inferential data analysis covers linear and logistic regression and regularization. The last part studies machine learning with a focus on support-vector machines and deep learning. Each chapter is based on a dataset, which can be downloaded from the book’s homepage.

This book is the right choice for everyone who wants to understand and apply concepts and methods of statistical learning. Typical readers are graduate and advanced undergraduate students in data-intensive fields such as computer science, biology, psychology, business, and engineering, and graduates preparing for their job interviews.

Highlights

A careful selection of topics ensures rapid progress
An opening question at the beginning of each chapter leads the reader through the topic
Expositions are rigorous yet based on elementary mathematics
More than two hundred exercises help digest the material
A crisp discussion section at the end of each chapter summarizes the key concepts and highlights practical implications
Numerous suggestions for further reading guide the reader in finding additional information

The book comprises eight chapters:

Fundamentals of Data
Exploratory Data Analysis
Unsupervised Learning
Linear Regression
Logistic Regression
Regularization
Support-Vector Machines
Deep Learning

Get the book

Follow this link to get a hardcopy at the best price directly through Springer, and follow the same link to get the PDF through the library system of your university.

Data files 📡

Chapter 1: Gambling.csv
Chapter 2: Photons.csv
Chapter 3: WoodyPlants.csv
Chapter 4: PowerSpeed.csv
Chapter 5: Challenger.csv
Chapter 6: Smartphone.csv
Chapter 7: Soccer.csv

Cite

Lederer, J., 2025. A First Course in Statistical Learning: With Data Examples and Python Code. Springer Statistics and Computing.

@book{2025Lederer, author="Lederer, Johannes", title="A First Course in Statistical Learning: With Data Examples and Python Code", year="2025", publisher="Springer Statistics and Computing", pages="1--282", isbn="978-3-031-30275-6", url="https://doi.org/10.1007/978-3-031-30276-3"}