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Programming for Data Science at URI Fall 2021
  • About this Book

Syllabus

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

Notes

  • 1. Welcome to Programming to Data Science
  • 2. Jupyter Notebook Tour & Python Review
  • 3. Getting help, object inspection, loading data
  • 4. Pandas DataFrames
  • 5. More Loading Data, Indexing, and Iterables
  • 6. Exploratory Data Analysis
  • 7. Visualization
  • 8. Exploratory Data Analysis
  • 9. Reshaping Data
  • 10. More Reshaping
  • 11. Missing Data and Inconsistent coding
  • 12. Building Datasets From multiple Sources
  • 13. Reviewing Merges & Databases
  • 14. Web Scraping
  • 15. Intro to Machine learning
  • 16. Interpetting and Evaluating Naive Bayes
  • 17. Making Predictions in Generative Model
  • 18. Midsemester feedback and Decision Trees
  • 19. Decision Tree Setting and more Evaluation
  • 20. Linear Regression
  • 21. Interpretting Regression
  • 22. Clustering
  • 23. Clustering
  • 24. Evaluating Clustering
  • 25. ML Task Review and Cross Validation
  • 26. SVM and Parameter Optimizing
  • 27. Model Comparison
  • 28. Model Selection
  • 29. Learning Curves
  • 30. Intro to NLP- representing text data
  • 31. More NLP & Solving problems with ML
  • 32. Neural Networks
  • 33. Predicting with Neural Networks
  • 34. Review, IDEA, & Preparing for Deep Learning
  • 35. Neural Networks with Keras
  • 36. Convolutional Neural Netowrks

Assignments

  • 1. Portfolio Setup, Data Science, and Python
  • 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: Understanding Classification
  • 7. Assignment 7: Decision Trees
  • 8. Assignment 8: Regression
  • 9. Assignment 9: Clustering
  • 10. Assignment 10: Tuning Model Parameters
  • 11. Assignment 11: Model Comparison
  • 12. Assignment 12: Fake News

Portfolio

  • Portfolio Dates and Key Facts
  • Submission Introductions
  • 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
  • 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
  • Letters to Future students
Powered by Jupyter Book
  • 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

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.