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Programming for Data Science at URI Fall 2022

About this Book

Syllabus

About
Tools and Resources
Data Science Achievements
Grading
Grading Policies
Support
General URI Policies
Help Hours and Course Communications

Notes

1. Welcome to Programming to Data Science
2. Reading Docstrings, Object Inspection, and Loading Data
3. Data Frames and other iterables
4. Iterables and Indexing
5. Getting Started with Exploratory Data Analysis
6. Visualization
7. More EDA
8. Intro to Data Cleaning
9. Fixing Data representations
10. Cleaning Data: fixing values
11. Building Datasets from Multiple Sources
12. Web Scraping
13. Getting Data from Databases
14. Intro to Machine Learning: Evaluation
15. Performance Metrics continued
16. Modeling and Naive Bayes
17. Classification with Naive Bayes
18. Decision Trees
19. Feedback & Regression
21. Interpretting Regression
22. Sparse and Polynomial Regression
23. Clustering
24. Clustering with Sci-kit Learn
30. Clutering Evalutation
31. ML Task Review Cross Validation
32. Model Optimization
33. Model Comparison
34. Model Comparison: when do differences matter?
35. Learning Curves
36. Intro to NLP- representing text data
37. Classification of Text Data
38. Neural Networks
39. Predicting with NN

Assignments

1. Portfolio Setup, Data Science, and Python
2. Assignment 2: Practicing Python and Accessing Data
3. Assignment 3: Exploratory Data Analysis
4. Assignment 4: Cleaning Data
5. Assignment 5: Constructing Datasets
6. Assignment 6: Auditing Algorithms
7. Assignment 7: Classification
8. Assignment 8: Linear Regression
9. Assignment 9
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 4 Ideas

FAQ

FAQ
Syllabus and Grading FAQ
Git and GitHub
Code Errors

Resources

Glossary
References on Python
How Tos
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
Letters to Future students

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References on Python

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Cheatsheet

By Professor Sarah M Brown
© Copyright 2022.