51. Adrien Treuille and Tim Conkling - Streamlit Is All You Need
Towards Data Science
09/16/20
•39m
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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.
Previous Episode
50. Ken Jee - Building your brand in data science
September 9, 2020
•55m
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.
Next Episode
52. Sanyam Bhutani - Networking like a pro in data science
September 23, 2020
•54m
Networking is the most valuable career advancement skill in data science. And yet, almost paradoxically, most data scientists don’t spend any time on it at all. In some ways, that’s not terribly surprising: data science is a pretty technical field, and technical people often prefer not to go out of their way to seek social interactions. We tend to think of networking with other “primates who code” as a distraction at best, and an anxiety-inducing nightmare at worst.
So how can data scientists overcome that anxiety, and tap into the value of network-building, and develop a brand for themselves in the data science community? That’s the question that brings us to this episode of the podcast. To answer it, I spoke with repeat guest Sanyam Bhutani — a top Kaggler, host of the Chai Time Data Science Show, Machine Learning Engineer and AI Content Creator at H2O.ai, about the unorthodox networking strategies that he’s leveraged to become a fixture in the machine learning community, and to land his current role.
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