50. Ken Jee - Building your brand in data science
Towards Data Science
09/09/20
•55m
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It’s no secret that data science is an area where brand matters a lot.
In fact, if there’s one thing I’ve learned from A/B testing ways to help job-seekers get hired at SharpestMinds, it’s that blogging, having a good presence on social media, making open-source contributions, podcasting and speaking at meetups is one of the best ways to get noticed by employers.
Brand matters. And if there’s one person who has a deep understanding of the value of brand in data science — and how to build one — it’s data scientist and YouTuber Ken Jee. Ken not only has experience as a data scientist and sports analyst, having worked at DraftKings and GE, but he’s also founded a number of companies — and his YouTube channel, with over 60 000 subscribers, is one of his main projects today.
For today’s episode, I spoke to Ken about brand-building strategies in data science, as well as job search tips for anyone looking to land their first data-related role.
Previous Episode
49. Catherine Zhou - The data science of learning
September 2, 2020
•56m
If you’re interested in upping your coding game, or your data science game in general, then it’s worth taking some time to understand the process of learning itself.
And if there’s one company that’s studied the learning process more than almost anyone else, it’s Codecademy. With over 65 million users, Codecademy has developed a deep understanding of what it takes to get people to learn how to code, which is why I wanted to speak to their Head of Data Science, Cat Zhou, for this episode of the podcast.
Next Episode
51. Adrien Treuille and Tim Conkling - Streamlit Is All You Need
September 16, 2020
•39m
We’ve talked a lot about “full stack” data science on the podcast. To many, going full-stack is one of those long-term goals that we never get to. There are just too many algorithms and data structures and programming languages to know, and not enough time to figure out software engineering best practices around deployment and building app front-ends.
Fortunately, a new wave of data science tooling is now making full-stack data science much more accessible by allowing people with no software engineering background to build data apps quickly and easily. And arguably no company has had such explosive success at building this kind of tooling than Streamlit, which is why I wanted to sit down with Streamlit founder Adrien Treuille and gamification expert Tim Conkling to talk about their journey, and the importance of building flexible, full-stack data science apps.
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