📚 Resources

Table of contents

1. Past Exams
2. Readings
   1. Textbooks
   2. Articles
   3. Other Links
3. Regular Expressions
4. Finding Datasets
   1. Generic sources of data
   2. Domain-specific sources of data

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Past Exams

While this class specifically hasn’t been offered yet, it is inspired by a few different courses that have been offered many times, many of which have banks of old exams available online. The most relevant problems will be posted at our brand-new 🧠 Study Site, which you’ll use in discussion section.

If you’d like some additional practice, 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.

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Readings

Textbooks

• Principles and Techniques of Data Science, the textbook for Berkeley’s Data 100 course.
  • These are also supplemented by a set of Course Notes.
• DSC 80 Course Notes. These notes were originally written for UCSD’s version of this course, but have not been updated in a few years.
• Python for Data Analysis, an online textbook by Wes Mickinney, the original developer of pandas.
• DSC 10 Course Notes. These notes were written for UCSD’s more introductory data science course, which introduces Python and Jupyter Notebooks. You’ll find a lot of the material here useful, too.
• Stanford’s Python Reference.
• EECS 201: Computer Science Pragmatics. This class covers “the essentials of using a computer effectively for EECS students,” and covers Unix-like systems, shells, version control, build systems, debugging, and scripting.

Articles

• Facts and myths about Python names and values – good to read if you’re confused about how variables and mutability work in Python.
• Views and Copies in pandas – a great read if you’d like to learn more about the infamous SettingWithCopyWarning.
• A Visual Introduction to Machine Learning and Model Tuning and the Bias-Variance Tradeoff, excellent visual descriptions of the last few weeks of the course (some terminology is different, but the ideas are the same).
• MLU Explain, a collection of interactive articles (prepared by Jared Wilber) that explain core machine learning ideas, like cross-validation, random forests, and precision and recall.

Other Links

• pythontutor.com, a tool to visualize the execution of Python programs.
• pandastutor.com, the equivalent of pythontutor.com for DataFrame manipulation.

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Regular Expressions

• regex101.com, a helpful site to have open while writing regular expressions.
• Python re library documentation and how-to.
• regex “cheat sheet” (taken from here).

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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.

• Kaggle Datasets.
• Google’s dataset search.
• FiveThirtyEight.
• DataHub.io.
• Data.world.
• CORGIS.
• R datasets.
• Wikipedia. (use this site to extract and download tables as CSVs)
• Awesome Public Datasets GitHub repo.
• Links to even more sources

Domain-specific sources of data

• Sports: Basketball Reference, Baseball Reference, etc.
• US Government Sources: census.gov, data.gov, data.ca.gov, data.sfgov.org, FBI’s Crime Data Explorer, Centers for Disease Control and Prevention.
• Global Development: data.worldbank.org, databank.worldbank.org, WHO.
• Transportation: New York Taxi trips, Bureau of Transportation Statistics, SFO Air Traffic Statistics.
• Music: Spotify Charts.
• COVID: Johns Hopkins.
• Any Google Forms survey you’ve administered! (Go to the results spreadsheet, then go to “File > Download > Comma-separated values”.)

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.

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