Daliana and I had a 2hr chat on all things data science and machine learning.
How to generate labels from scratch with semi, active, and weakly supervised learning.
My favourite project, how I write weekly and how you can start, and content I would like to see more of.
How to go from knowing machine learning to applying it at work to drive impact.
We discussed about how to build and run data teams and engage better with business.
Short vs. long-term gain, incremental vs. disruptive innovation, and resume-driven development.
What the top teams did to win the 36-hour data hackathon. No, not machine learning.
What I learned about hiring and training, and fostering innovation, discipline, and camaraderie.
Stop procrastinating, go off the happy path, learn just-in-time, and get your hands dirty.
How to increase the chances of getting called up by recruiters?
How he switched from engineering to data science, what "senior" means, and how writing helps.
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.
What questions do they answer? How do they compare? What open-source solutions are available?
Not 'How to build a data science portfolio', but 'Whys' and 'Whats'.
Building prototypes helped get buy-in when roadmaps & design docs failed.
What's an average day like? What's great about the role? How's working in Amazon?
My chat with James Le about my experience, leadership, agile, ML in production, writing, and more.
Becoming a senior after three years and dealing with imposter syndrome.
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.
Does DS have business requirements? When does it make sense to split DS and DE??
A curious discussion made me realize my expert blind spot. And no, Airflow is not late.
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.
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.
Automate your experimentation workflow to minimize effort and iterate faster.
How hard work, many failures, and a bit of luck got me into the field and up the ladder.
Keynote on how Asia's tech giants scale and their SuperApp strategy.
No, you don't need a PhD or 10+ years of experience.
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).
What's the difference between a data scientist, data engineer, and ML engineer? A panel at Google.
Yes, Agile can be adopted by data science teams. Moderating a panel at GovTech STACK.
Technical challenges easy compared to business and people issues. Sharing at the BDA Summit.
Culture >> Hierarchy, Process, Bureaucracy.
And my idiosyncratic journey to VP of Data Science at Lazada (Alibaba). A Lunchtime chat at INSEAD.
How being a Lead / Manager is different from being an individual contributor.
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.
If things are not failing, you're not innovating enough. - Elon Musk
Cleaning up text and messing with ascii (urgh!)
Parsing json and formatting product titles and categories.