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Hands-on Introduction to Data Science with Python

Introduction

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

Data Science Basics

5. Data and Data Types
6. Data - Information - Knowledge
7. Data Science Workflow

Data Acquisition and First Exploration

8. Data Acquisition & Preparation
9. Data Pre-Processing
10. First Data Exploration

In-depth Data Exploration

11. Correlation Analysis
12. Clustering
13. Outlier Detection
14. Dimensionality Reduction

Supervised Machine Learning

15. Supervised Machine Learning - Introduction
16. Common Algorithms - k-Nearest Neighbors (k-NN)
17. Common Algorithms II - Linear Models
18. Common Algorithms III - Decision Trees
19. Supervised Machine Learning - Key Techniques
20. Ensemble Models and Outlook

Working with Text Data

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

Look at the Networks

25. Networks / Graph Theory
26. Visualizing Graphs
27. Bottlenecks, Hubs, Communities

Next Steps

28. What are the next steps?

References

Acknowledgements
Bibliography

Source Code and Contributions

Source Code on GitHub

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By Florian Huber

© Copyright 2024.