Introduction to Data Science, Spring 2022
1. Introduction
2. Markdown Basics
3. Project Management
4. Building a Python Module
5. Python Refreshment
6. SciPy for Data Science
7. Data Manipulation with Pandas
8. Visualization
8.1. Matplotlib: Visualization with Python
8.2. Plotnine
8.3. Cartopy
8.4. GM Plot
8.5. Interactive Visualizations Using Bokeh
9. Statistical Tests and Models
9.1. Describing Models with Patsy
9.2. Hypothesis Tests
9.3. Linear and Generalized Linear Models
9.5. Generalized Additive Models
9.6. Stepwise Regression
9.7. Ridge Regression
10. Supervised Learning
10.2. Decision Trees
10.3. Random Forests
10.4. Bagging versus Boosting
10.5. Support Vector Machines
10.16. K Nearest Neighbor
10.17. XGBoost
10.19. Tensorflow
11. Unsupervised Learning
11.1. K-means Clustering
11.2. Gaussian Mixture Models
12. Miscellaneous
12.1. Web Scraping with Beautiful Soup
13. Exercises
14. I Fixed It
15. Bibliography
.ipynb
.md
.pdf
Binder
Miscellaneous
12.
Miscellaneous
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Gaussian Mixture Models
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Web Scraping with Beautiful Soup