career] [ datascience learning machinelearning python career productivity omscs production engineering lazada til business leadership communication agile recsys misc spark nlp deeplearning ]
What's an average day like? What's great about the role? How's working in Amazon?
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
My chat with James Le about my experience, leadership, agile, ML in production, writing, and more.
Why read papers, what papers to read, and how to read them.
How not to become an expert beginner and to progress through beginner, intermediate, and so on.
Why (and why not) be more end-to-end, how to, and Stitch Fix and Netflix's experience
Crocker's Law, cognitive dissonance, and how to receive (uncomfortable) feedback better.
What I Learnt about evaluating ideas from first-hand participation in a hackathon.
Why you should give a talk and some tips from five years of speaking and hosting meet-ups.
12 Apr 2020  ·  5 min  ·  [ career ]
Should I join a start-up? Which offer should I accept? A simple metaphor to guide your decisions.
How hard work, many failures, and a bit of luck got me into the field and up the ladder.
No, you don't need a PhD or 10+ years of experience.
What's the difference between a data scientist, data engineer, and ML engineer? A panel at Google.
What is data science, how to pick it up, and how to enter the field? A discussion with SMU undergrads.
Sharing about why data science, data science myths, a typical day, and more with TIA.
Tools and skills to pick up and how to practice them. An Invited Talk with Masters in IT candidates.
Tools and skills to pick up, and how to practice them.