đź“š Resources

Table of contents

  1. Past Exams
  2. Guides
  3. Readings
  4. Topic-Specific Resources
    1. Python
    2. pandas
    3. Introductory Statistics and Probability
    4. Mathematics
    5. Visualization
    6. Missing Values
    7. Web Scraping
    8. Regular Expressions
    9. Machine Learning
  5. Finding Datasets
    1. Generic Sources of Data
    2. Domain-Specific Sources of Data
    3. University of Michigan Library Guides

Past Exams

Past exams can be found at 🧠 Study Site, the same site discussion worksheets will be posted at.

If you’d like some additional practice from similar classes Suraj taught at UC San Diego, you can refer to:

  • practice.dsc80.com – most similar to our course.
  • practice.dsc40a.com – more theoretical than our course, but some problems will be relevant.
  • practice.dsc10.com – more introductory-level than our course, but some DataFrame-related problems will be relevant.

Guides

This semester, we’re


Readings


Topic-Specific Resources

There are lots of readings linked on the course website. Here, we’re collecting other helpful resources that will help explain ideas in the course. If you found something online that was super helpful, let us know and we’ll add it here!

Python

pandas

Introductory Statistics and Probability

Mathematics

Visualization

Missing Values

Web Scraping

  • STATS 701 notes – these are in R, but are still helpful for giving you a general idea of what you can scrape and how.

Regular Expressions

Machine Learning


Finding Datasets

Generic Sources of Data

These sites allow you to search for datasets (in CSV format) from a variety of different domains. Some may require you to sign up for an account; these are generally reputable sources.

Domain-Specific Sources of Data

Tip: if a site only allows you to download a file as an Excel file, not a CSV file, you can download it, open it in a spreadsheet viewer (Excel, Numbers, Google Sheets), and export it to a CSV.

Here’s a tutorial on how to download JSON data from data.gov, for example.

University of Michigan Library Guides

The university library system maintains several guides on how to conduct research and where to find information. They contain lots of links to local data sources. Here are a few guides of interest:

If you have questions about how to use any of these guides, or how to use any of the other resources our library has to offer, contact Sarah Barbrow (sbarbrow@umich.edu), our Engineering librarian (who also recorded this video, of interest to students who are looking to get into social sciences research)!