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About this Book

Syllabus

About
Tools and Resources
Data Science Achievements
Grading
Grading Policies
Course Style Guide
Support
General URI Policies
Communications & Office Hours

Notes

1. Welcome & What is Data Science
2. Iterables and Pandas Data Frames
3. DataFrames from other sources
4. Exploratory Data Analysis
5. Visualization
6. Tidy Data and Structural Repairs
7. Reparing values
8. Merging Data
9. Web Scraping
10. Evaluating ML Algorithms
11. Intro to ML & Naive Bayes
12. Understanding Classification
13. Clustering
14. Clustering Metrics
15. Regression
16. Interpretting Regression
17. Cross Validation
18. Model Optimization
19. Model Comparison
20. Learning Curves and more Model Comparison
21. Representing Text
22. More text representations
23. Neural Networks

Assignments

1. Assignment 1: Setup, Syllabus, and Review
2. Assignment 2: Practicing Python and Accessing Data
3. Assignment 3: Exploratory Data Analysis
4. Assignment 4: Cleaning Data
5. Assignment 5: Constructing Datasets and Using Databases
6. Assignment 6: Auditing Algorithms
7. Assignment 7
8. Assignment 8: Clustering
9. Assignment 9: Linear Regression
10. Assignment 10: Tuning Model Parameters
11. Assignment 11: Model Comparison
12. Assignment 12: Fake News

Portfolio

Portfolio
Formatting Tips
Portfolio Check 1 Ideas
Check 2 Ideas
Check 3 Ideas

FAQ

FAQ
Syllabus and Grading FAQ
Git and GitHub
Code Errors

Resources

Glossary
References on Python
Cheatsheet
Data Sources
General Tips and Resources
How to Study in this class
Getting Help with Programming
Terminals and Environments
Getting Organized for class
Advice from FA2020 Students
Advice from FA2021 Students

Repository
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By Professor Sarah M Brown

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