Python Data Science Essentials: Become an Eefficient Data Science Practitioner By Understanding Python's Key Concepts

Part I. First steps
1. Introducing data science and Python
2. Installing Python
3. Introducing Jupyter
4. Datasets and code used in the book

Part II. Data munging
5. Data Munging
6. The data science process
7. Data loading and preprocessing with pandas
8. Working with categorical and text data
9. Data processing with NumPy
10. Creating NumPy arrays
11. NumPy's fast operations and computations

Part III. The data pipeline
12. The Data Pipeline
13. Introducing EDA
14. Building new features
15. Dimensionality reduction
16. The detection and treatment of outliers
17. Validation metrics
18. Testing and validating
19. Cross-validation
20. Hyperparameter optimization
21. Feature selection
22. Wrapping everything in a pipeline
23. Machine Learning

Part IV. Machine learning
24. Preparing tools and datasets
25. Linear and logistic regression
26. Naive Bayes
27. K-Nearest Neighbors
28. Nonlinear algorithms
29. Ensemble strategies
30. Dealing with big data
31. Approaching deep learning
32. A peek at Natural Language Processing (NLP)
33. An overview of unsupervised learning
34. Social Network Analysis

Part V. Social network analysis
35. Introduction to graph theory
36. Graph algorithms
37. Graph loading, dumping, and sampling
38. Visualization, Insights, and Results

Part VI. Visualization, Insights, and Results
39. Introducing the basics of matplotlib
40. Wrapping up matplotlib's commands
41. Interactive visualizations with Bokeh
42. Advanced data-learning representations