career] [ datascience machinelearning learning career python productivity production omscs engineering lazada writing til misc business survey leadership deeplearning recsys agile spark nlp informalmentors ]
Data cleaning, transfer learning, overfitting, ensembling, and more.
Interview questions you should ask and how to evolve your job scope.
A personal take on their deliverables and skills, and what it means for the industry and your team.
Chip shares openly about the setbacks she faced, overcoming them, and how writing changed her life.
Instead of "How to build a data science portfolio", we'll discuss the "Whys" and "Whats" around a portfolio.
As our careers grow, how does the balance between writing & coding change? Hear from 4 tech leaders.
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  ·  6 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.