Beyond getting that starting role, how does one continue growing in the field?
Daliana and I had a 2hr chat on all things data science and machine learning.
Why this is the first rule, some baseline heuristics, and when to move on to machine learning.
Why the Amazon applied scientist takes the time to break down his work for readers.
Show them the data, the Socratic method, earning trust, and more.
My favourite project, how I write weekly and how you can start, and content I would like to see more of.
Even high achieving individuals experience impostor syndrome; here's how Susan learned to manage it.
More education, achievements, and awards don't shoo away imposter syndrome. Here's what might help.
What do you deeply care about? What do you excel at? Build a career out of that.
Why did I start writing? What's my writing process? What's the writing culture at Amazon like?
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.
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.
Setbacks she faced, overcoming them, and how writing changed her life.
Not 'How to build a data science portfolio', but 'Whys' and 'Whats'.
As 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.
Becoming a senior after three years and dealing with imposter syndrome.
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.
Why you should give a talk and some tips from five years of speaking and hosting meet-ups.
Should I join a start-up? Which offer should I accept? A simple metaphor to guide your decisions.
12 Apr 2020  ·  6 min  ·  career
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.