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  • About this Book

Syllabus

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

Notes

  • 1. Welcome and Introduction
  • 2. Syllabus and Python Review
  • 3. Grading review, Pandas, and Iterables
  • 4. Pandas and Indexing
  • 5. Exploratory Data Analysis (EDA)
  • 6. Visualization
  • 7. Tidy Data and Reshaping Datasets
  • 8. Reparing values
  • 9. Merging and Databases
  • 10. Web Scraping
  • 11. Evaluating ML Algorithms
  • 12. Auditing with AIF360
  • 13. Intro to ML & Naive Bayes
  • 14. Predictions with niave Bayes and Decision Trees
  • 15. Clustering
  • 17. Comparing Classification and Clustering Data
  • 18. Regression Preview
  • 19. Regression
  • 20. ML Task Review and Cross Validation
  • 21. SVM and Model Optimization
  • 22. Model Comparison
  • 23. Intro to NLP- representing text data
  • 24. ML with Text

Assignments

  • 1. Assignment 1: Portfolio Setup, Data Science, and Python
  • 2. Assignment 2: Practicing Python and Accessing Data
  • 3. Assignment 3: Exploratory Data Analysis
  • 4. Assignment 4:
  • 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

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
  • Letters to Future students
  • Repository
  • Open issue

Index

A | B | C | D | G | I | K | L | P | R | S | T | W

A

  • aggregate
  • anonymous function

B

  • BeautifulSoup

C

  • conditional
  • corpus

D

  • DataFrame
  • dictionary
  • document

G

  • gh
  • git
  • GitHub

I

  • index
  • interpreter
  • iterable
  • iterate

K

  • kernel

L

  • lambda

P

  • PEP 8

R

  • repository

S

  • Series
  • Split Apply Combine
  • stop words
  • suffix

T

  • test accuracy
  • Tidy Data Format
  • token
  • TraceBack
  • training accuracy

W

  • Web Scraping

By Professor Sarah M Brown

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