datascience] [ 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?
My chat with James Le about my experience, leadership, agile, ML in production, writing, and more.
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.
Part II of the previous write-up, this time on applications and frameworks of Spark in production
Sharing my notes & practical knowledge from the conference for people who don't have the time.
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 learned about measuring diversity, novelty, surprise, and serendipity from 10+ papers.
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.
17 Sep 2015  ·  8 min  ·  [ datascience ]
Guest post of how DataKind SG worked with NGOs to frame their problems and suggests solutions