Hard-won lessons on how to start data science projects effectively.
Met most of my goals, adopted a puppy, and built ApplyingML.com.
Three documents I write (one-pager, design doc, after-action review) and how I structure them.
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.
20 Sep 2020  ·  15 min  ·  productivity
My tools for organization and creation, autopilot routines, and Maker's schedule
13 Sep 2020  ·  10 min  ·  productivity
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.
Using a Zettelkasten helps you make connections between notes, improving learning and memory.
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).