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Hands-on Introduction to Data Science with Python

Introduction

Introduction to this book
What is Data Science?
Data Science, Ethics, and Society
How to use this book (… if you ask us)

From Data to Knowledge

Data and Data Types
Data Acquisition – Where Do We Get Our Data?
Data - Information - Knowledge
Data Science Workflow

First Data Exploration

Data Cleaning and Preprocessing
Statistical Measures and Distributions

In-depth Data Exploration

Correlation Analysis
Clustering
Clustering beyond the basics
Outlier Detection
Dimensionality Reduction

Supervised Machine Learning (Basics)

Supervised Machine Learning - Introduction
Common Algorithms - k-Nearest Neighbors (k-NN)
Common Algorithms II - Linear Models
Common Algorithms III - Decision Trees
Supervised Machine Learning - Key Techniques
Ensemble Models and Outlook

Working with Text Data

Introduction to Working with Text Data
NLP - Basic Techniques to Analyze Text Data
Computing with Text: Counting words
Beyond Counting Individual Words: N-grams

Look at the Networks

Networks / Graph Theory
Visualizing Graphs
Bottlenecks, Hubs, Communities

Next Steps

What are the next steps?

References

Acknowledgements
Complete Bibliography

Source Code and Contributions

Source Code on GitHub

Index

By Florian Huber

© Copyright 2026, Florian Huber.