productivity] [ datascience learning machinelearning python career productivity omscs production engineering lazada til business leadership communication agile recsys misc spark nlp deeplearning ]
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).