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A curated list of suggested reading materials.

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# Readings

A (work in progress) curated list of suggested reading materials relating to Data Science.

There are no required readings for this course; however, if you’re interested in learning more and reading about data science topics, we recommend the following texts as supplementary to the main elements of the course:

- Grus J (2019, 2nd ed) Data Science from Scratch. This book takes you into HOWs and WHYs, rather than just learning to use a library you don't really understand.  This is the harder book, but you will grow tremendously working though it. [Can be accessed for free through your UCSD login](https://library.ucsd.edu/news-events/oreilly-for-higher-education/)
- Vanderplas, J (2023, 2nd ed) [Python Data Science Handbook](https://github.com/jakevdp/PythonDataScienceHandbook). Short and too the point. Both the text and the code are freely available on Github. Learn to use standard libraries to get things done.

Some older [Tutorials](https://github.com/COGS108/Tutorials) for this class exist if you'd like to see them.

## Articles

- 50 Years of Data Science, D Donoho
- Exploratory Data Analysis, JW Tukey
- Depth of Learning, M Buchanan
- Points of View: Storytelling, M Krzywinski & A Cairo
- Data Organization in Spreadsheets, K Broman & K Woo
- Good Enough Practices in Scientific Computing, G Wilson et al.

## Writing Code / Programming Practices

- Programming Principles
  - https://www.makeuseof.com/tag/basic-programming-principles/
  - https://www.makeuseof.com/tag/weird-programming-principles/
- [Software development skills for data scientists](http://treycausey.com/software_dev_skills.html), Trey Causey (2015)

## Blogs

- [Better Explained](https://betterexplained.com)
  - A good resource for accessible math explainers
- [Simply Statistics](https://simplystatistics.org)
  - Covers statistics, data science, and AI
- [What's the Big Data](https://whatsthebigdata.com)
  - Curates a lot of quick, interesting things on data science in general, largely industry focused
- [Probability and Statistics for Data Science](https://cims.nyu.edu/~cfgranda/pages/stuff/probability_stats_for_DS.pdf)
  - "These notes were developed for the course Probability and Statistics for Data Science at the
Center for Data Science in NYU."
- [Datawrapper](https://blog.datawrapper.de/)
  - A data-visualization blog chock full of great knowledge and examples

## Textbooks

- Data Science from Scratch, Joel Grus
- Python Data Science Handbook, Jake VanderPlas

## Journals

- Harvard Data Science Review
  - https://hdsr.mitpress.mit.edu/ 
- Distill
  - https://distill.pub/about/
- GigaScience
  - https://academic.oup.com/gigascience
- Journal of Open Source Software
  - https://joss.theoj.org/about
- Scientific Data
  - https://www.nature.com/sdata/

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