Learning-Programming on TanHo.ca

Things I Think You Should Read and Watch

Tue, 21 Jul 2026 00:00:00 +0000

This is a curated, non-exhaustive list of tech-adjacent content I’ve really enjoyed and shared with friends and colleagues.

Mostly general thoughts on life as a data scientist, learning/teaching programming, technical leadership, career advice, and other tech-adjacent topics, and not specific to R or sports analytics (will probably tackle that another time).

Inspired by Derek Sivers’s book notes and Vicki Boykis’s favourite essays, and organized loosely by topic (click to jump to section):

life as a programmer & data scientist
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Michael Lynch: How to Do Code Reviews Like A Human and part two, blog posts (18 and 15 min reads, respectively)
- Code reviews are daily interactions that shape both the code itself and, maybe even more importantly, the relationships between programmers - these posts really help with how to approach these in a constructive and positive way.
Hank Green: The Secret to My Productivity, YouTube video (4 min)
- Invaluable advice on when to stop working on creative projects: stop working on it when you have reached 80% of “the best you can do”, release it to the world, and then go do your next project. Applies in hobbies and tbh to professional projects as well.
Randy Au: Succeeding as a Data Scientist in Small Companies / Startups, blog post (8 min read)
- How to succeed in a small company where you are the first “data” person there: helping the company succeed TODAY and setting up the company to be data driven TOMORROW. Also has a very nice “hierarchy of needs” graphic that explains what it means to set up a company to be data driven. Immediately relevant for people in small companies and also for understanding the core needs for a data team (and seeing how that scales as you add more people.)
Alberto Savoia: The Way of Testivus, PDF blog post
- Advice on developer and unit testing, packaged up as pseudo-Confucian tenets worth living by.
Dan McKinley: Boring Technology, talk/slides and original blogpost
- Core idea: you get “innovation tokens” to spend as a (developer, team) and you don’t have that many even across a team. In order to keep those innovation tokens spent on the team’s core mission (e.g. solving soccer), you should spend less time innovating on the core technologies (fancy database stuff, programming paradigms etc), because fixing bleeding edge database stuff takes up “innovation tokens” that you want to spend doing other things.
David Robinson: Unreasonable Effectiveness Of Public Work, YouTube talk recording (40 min)
- Anything you put out into the world is going to be far better and far more useful than anything that sits in your drafts folder waiting for some arbitrary threshold of “good” before it gets published. Go out there and do things!

learning and teaching programming
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Greg Wilson: what everyone in tech should know about teaching and learning, YouTube video (41 min)
- Massively useful! Especially the ideas right at the beginning about mental models and the differences between how beginners, competent users, and experts learn. The whole video is incredible, but I recommend at least the first 8:23 to cover these ideas.
Andy Harris: how to begin thinking like a programmer, YouTube video (61 min)
- A riveting talk (given at a Python conference, which should surprise you if you know me). which covers how to think like a programmer. Especially profound: comments are not for explaining code to other programmers - instead, code is there to explain comments to the computer!

technical leadership
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Tanya Reilly: Being Glue, annotated talk slides and YouTube video (28 min)
- A brutally honest talk about the non-promotable “glue” work that programmers can get silently stuck with, and that hurts your technical development and ultimately your career development if you’re not prepared. Can disproportionately affect teams, especially women and/or introverted/non-assertive personalities.
Julia Evans: Help, I Have A Manager!, zine (~$12)
- Having a good relationship with your manager is probably the most important factor in your happiness at work. This zine is full of great advice on how to make that relationship better - how to handle 1:1s, asking for feedback, figuring out what they’re good at, building a support system for areas they aren’t as good, and more.
Vasily Zubarev: How to hire sane engineers 2.0 (~15 min read)
- A pragmatic Russian’s take on hiring engineers in a “sane” and effective way. Would love to implement this one day to help with bypassing some of the technical interview steps that are so common now.

career advice
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Derek Sivers: How To Do What You Love And Make Good Money, blog post with audio recording (4 min read/listen)
- This is the advice I share when folks ask me about getting their dream job and making a career from sports analytics or other hobbies - you don’t need to force sports analytics to be both your livelihood and your art! The dream job that does both may or may not come along, but you can be happy in both career and art without it. Part of a larger book, Hell Yeah or No, which is also very worth reading (~$15).
Austin Kleon: Show Your Work, short book (~$15)
- The book I share when people ask me how to build their public portfolio and personal networks: share something small every day without being human spam, teach what you know, and tell good stories. A lot of insights packed into a small and easy to read format. Also great are the other books in the Steal trilogy, including Steal Like An Artist on how to be creative and Keep Going on how to sustain the creative effort
Jacqueline Nolis & Emily Robinson: Build A Career In Data Science, book (~$24) and podcast series (~20 episodes each about 60min)
- A great resource for finding a job in data science. Really like the definitions of data science as an industry - essentially the intersection of programming, statistics, and domain knowledge. Book was available online for free at one point but now seems to be only paid - very worth it, but listening to each of the podcast episodes is a pretty good equivalent.
Haseeb Qureshi: Rules for Negotiating a Job Offer and part two, blog posts (24 and 34 minute reads)
- More people should negotiate job offers and simply don’t. This is good, not-too-kitschy advice on how to approach negotiating.

specific technologies
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Vasily Zubarev: Machine Learning For Everyone, blog post (~40 min read)
- “Machine Learning is like sex in high school. Everyone is talking about it, a few know what to do, and only your teacher is doing it.” An impressively nontechnical and yet surprisingly comprehensive overview of machine learning that I found useful early on in my data science career as someone with neither a CS nor a stats background.
Julia Evans: Hell Yes CSS, zine (~$12) and comics
- I’ve always found CSS to be incredibly frustrating, but reading this zine was super helpful. Still refer back to the CSS Selectors comic to this day whenever I need to format something.
OhShitGit.com (reference page)
- For the times I manage to fuck up using git, this page consistently saves me every single time.
data.table vs base vs dplyr (reference page)
- An awesome syntax lookup page to go between dplyr, base, and data.table in R - a worthy successor to the original post by atrebas

changelog
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2026-07-21: added data.table vs base vs dplyr reference page
2025-09-04: added ohshitgit.com
2025-03-17: added David Robinson’s Unreasonable Effectiveness Of Public Work talk
2024-11-29: first version

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

Tue, 10 Dec 2024 00:00:00 +0000

Ideas are worth nothing unless executed, and I’m tired of adding ideas to my backlog only to never get to them. This series aims to set those ideas free - let me know if you take it on!

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

Learning-Programming on TanHo.ca

Things I Think You Should Read and Watch

life as a programmer & data scientist #

learning and teaching programming #

technical leadership #

career advice #

specific technologies #

changelog #