The Beginner Data Science Programming Book That Doesn't Exist (Yet?)

Hot take: the ideal beginner data science programming book doesn’t exist (yet) #

The beloved R for Data Science is a fantastic book, and yet I struggled immensely with following along with it. I think it’s because I can only care so much about flower irises, penguins, and NYC taxis: I quit halfway through the third chapter of R4DS on my first attempt in 2018ish, … because I got bored.

Since then, I’ve gotten treatment for ADHD (which probably explains a lot, tbh), but I think what really helped the most with learning programming was that I started working on projects that I actually cared about: fantasy football and NFL.

I’d work on something for a while, get stuck on something I didn’t know, Ctrl-F the R4DS book + dplyr/tidyr reference manuals, figure out what I needed to know and how it fit into what I already knew, and then keep going. Reading R4DS cover to cover was utterly unhelpful until I knew how to fit each concept into a mental model that I knew how to use.

My love of all things R and data has since taken off (enough to have made a whole career of it), and now I often get asked what books I’d recommend for learning NFL/sports analytics, R, data science, programming etc. I supply the easy answer (“you should read R4DS”), but I’m a little uncomfortable with it because the book I wish I could recommend doesn’t quite exist.

the ideal book for learning programming and NFL analytics #

There are a lot of introductory data science and programming books out there, and even ones that apply the context of NFL: both Eric Eager and Brad Congelio have great iterations of this (and the latter is free online)!

I’m so glad these books exist now and they’d have certainly helped a lot when I first got started, but I’m not quite sure these are the ones I’ve been wanting:

• how do they stay up to date with a fast-evolving programming landscape?
• how do they compare with the existing literature that is beloved by the community, like R4DS, and how should beginners proceed if the books disagree on best code approach?
• what happens if I want to learn another programming language other than R?

I think the book I want to exist would be structured as a companion to books like R4DS, but not include any language-specific code whatsoever. Instead, I’d focus on a few goals:

• create a series of case studies and exercises that challenge learners to write their own solutions based on domain-specific research questions
  • provide starter data of appropriate complexity/cleanliness in a data repository
• cover a full breadth of high-level data science / programming topics
  • kind of like R4DS’s visualize, transform, import, program, communicate sections
• maintain up-to-date links to readings for each case study that help learners find best-practice guidance on a given topic in a supported programming language
  • each case study should provide links to “prerequisite reading” to solve it, e.g. links to relevant R4DS chapters for R
  • should be easily extended to other languages by linking to equivalent material, e.g. pivoting data, group by + summarize are relational data concepts that have parallels in almost every language
• introduce the essential background knowledge for the domain in question
  • for NFL this should include concepts like EPA, fourth downs, completion probability, expected rushing yards etc - essentially covering the body of public research
• suggest further research questions that can be tackled beyond the controlled sandbox of the book/repository

I think something like this would be massively helpful for introducing people to the relevant public research in a domain, and with putting technical topics into practical use for learners. It would be robust to existing languages evolving (could approach the same case study in e.g. tidyverse, data.table etc), easily extended to new languages (e.g. Python, Julia, Rust), and even to new technologies (probably could do the whole thing in DuckDB one day). The format also would be easy to adopt in different sports (soccer, basketball, baseball, hockey?) and to other domains altogether.

Of course, I’ve been sitting on this idea for a while: I first started a repo for this idea ~ three years ago and convinced friends like Sebastian Carl to start thinking about it, but neither of us have made much progress, which I think is more due to just a lack of time/energy/expertise in it. I still think it’s a worthwhile idea and am writing this post mostly to set it free and hope someone is inspired to pick it up!

A similar idea has come up since then: the SCORE project led by CMU’s Statistics and Data Science department aims to provide case studies across all sports for learning statistics and data science, and has a set of modules already. I’m not sure if ~ learning to program in a given language is in the scope of the project - it seems to be very statistics-focused - but it could otherwise be an interesting parallel project?