Hard-won lessons on how to start data science projects effectively.
I'm heading into a team lead role and would like to define the vision and roadmap.
What to consider for in terms of data, roadmap, role, manager, tooling, etc.
Daliana and I had a 2hr chat on all things data science and machine learning.
My favourite project, how I write weekly and how you can start, and content I would like to see more of.
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.
How to increase the chances of getting called up by recruiters?
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'.
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??
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.
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
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