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 1 | # Readings
 2 | 
 3 | A (work in progress) curated list of suggested reading materials relating to Data Science.
 4 | 
 5 | 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:
 6 | 
 7 | - 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/)
 8 | - 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.
 9 | 
10 | Some older [Tutorials](https://github.com/COGS108/Tutorials) for this class exist if you'd like to see them.  
11 | 
12 | ## Articles
13 | 
14 | - 50 Years of Data Science, D Donoho
15 | - Exploratory Data Analysis, JW Tukey
16 | - Depth of Learning, M Buchanan
17 | - Points of View: Storytelling, M Krzywinski & A Cairo
18 | - Data Organization in Spreadsheets, K Broman & K Woo
19 | - Good Enough Practices in Scientific Computing, G Wilson et al.
20 | 
21 | ## Writing Code / Programming Practices
22 | 
23 | - Programming Principles
24 |   - https://www.makeuseof.com/tag/basic-programming-principles/
25 |   - https://www.makeuseof.com/tag/weird-programming-principles/
26 | - [Software development skills for data scientists](http://treycausey.com/software_dev_skills.html), Trey Causey (2015)
27 | 
28 | 
29 | ## Blogs
30 | 
31 | - [Better Explained](https://betterexplained.com)
32 |   - A good resource for accessible math explainers
33 | - [Simply Statistics](https://simplystatistics.org)
34 |   - Covers statistics, data science, and AI
35 | - [What's the Big Data](https://whatsthebigdata.com)
36 |   - Curates a lot of quick, interesting things on data science in general, largely industry focused
37 | - [Probability and Statistics for Data Science](https://cims.nyu.edu/~cfgranda/pages/stuff/probability_stats_for_DS.pdf)
38 |   - "These notes were developed for the course Probability and Statistics for Data Science at the
39 | Center for Data Science in NYU."
40 | - [Datawrapper](https://blog.datawrapper.de/)
41 |   - A data-visualization blog chock full of great knowledge and examples
42 | 
43 | ## Textbooks
44 | 
45 | - Data Science from Scratch, Joel Grus
46 | - Python Data Science Handbook, Jake VanderPlas
47 | 
48 | ## Journals
49 | 
50 | - Harvard Data Science Review
51 |   - https://hdsr.mitpress.mit.edu/ 
52 | - Distill
53 |   - https://distill.pub/about/
54 | - GigaScience
55 |   - https://academic.oup.com/gigascience
56 | - Journal of Open Source Software
57 |   - https://joss.theoj.org/about
58 | - Scientific Data
59 |   - https://www.nature.com/sdata/
60 |   
61 | 


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