Three documents I write (one-pager, design doc, after-action review) and how I structure them.
Access, serving, integrity, convenience, autopilot; use what you need.
A public roadmap to track and share my progress; nothing mission or work-related.
Wrapping up 2020 with writing and site statistics, graphs, and a word cloud.
Time to clear the cache, evaluate existing processes, and start new threads.
Building prototypes helped get buy-in when roadmaps & design docs failed.
For years I've refined my routines and found tools to manage my time. Here I share it with readers.
My tools for organization and creation, autopilot routines, and Maker's schedule
Why read papers, what papers to read, and how to read them.
Why (and why not) be more end-to-end, how to, and Stitch Fix and Netflix's experience
Ever revisit a project & replicate the results the first time round? Me neither. Thus I adopted these habits.
It's not enough to have a good strategy and plan. Execution is just as important.
I thought giving it my all led to maximum outcomes; then I learnt about the 85% rule.
After this article, we'll have a workflow of tests and checks that run automatically with each git push.
Haste makes waste. Diving into a data science problem may not be the fastest route to getting it done.
Initially, I didn't like it. But over time, it grew on me. Here's why.
Using a Zettelkasten helps you make connections between notes, improving learning and memory.
Writing begins before actually writing; it's a cycle of reading -> note-taking -> writing.
Automate your experimentation workflow to minimize effort and iterate faster.
Taking the best from agile and modifying it to fit the data science process (Part 2 of 2).
A deeper look into the strengths and weaknesses of Agile in Data Science projects (Part 1 of 2).