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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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  • repository
  • open issue

Index

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

A

  • aggregate
  • anonymous function

B

  • BeautifulSoup

C

  • corpus

D

  • DataFrame
  • dictionary
  • document

G

  • git
  • GitHub

I

  • index
  • interpreter
  • iterable
  • iterate

K

  • kernel

L

  • lambda

P

  • PEP 8

R

  • repository
  • residual

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
© Copyright 2022.