Things I Think You Should Read and Watch
·6 mins
tech
notes
This is a curated, non-exhaustive list of 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 #
- 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.
learning and teaching programming #
- 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 #
- 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 #
- 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. Advice I used early on, and then somewhat discarded by joining Zelus, but it served me super well and arguably got me the job at Zelus. 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.
technical topics #
- 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.