Practical Data Science with Python

Table of Contents

1. Introduction to Data Science
2. Getting Started with Python
3. SQL and Built-in File Handling Modules in Python
4. Loading and Wrangling Data with Pandas and NumPy
5. Exploratory Data Analysis and Visualization
6. Data Wrangling Documents and Spreadsheets
7. Web Scraping
8. Probability, Distributions, and Sampling
9. Statistical Testing for Data Science
10. Preparing Data for Machine Learning: Feature Selection, Feature Engineering, and Dimensionality Reduction
11. Machine Learning for Classification
12. Evaluating Machine Learning Classification Models and Sampling for Classification
13. Machine Learning with Regression
14. Optimizing Models and Using AutoML
15. Tree-Based Machine Learning Models
16. Support Vector Machine (SVM) Machine Learning Models
17. Clustering with Machine Learning
18. Working with Text
19. Data Storytelling and Automated Reporting/ Dashboarding
20. Ethics and Privacy
21. Staying Up to Date and the Future of Data Science