49. Catherine Zhou - The data science of learning
Towards Data Science
09/02/20
•56m
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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.
Previous Episode
Data science is about much more than jupyter notebooks, because data science problems are about more than machine learning.
What data should I collect? How good does my model need to be to be “good enough” to solve my problem? What form should my project take for it to be useful? Should it be a dashboard, a live app, or something else entirely? How do I deploy it? How do I make sure something awful and unexpected doesn’t happen when it’s deployed in production?
None of these questions can be answered by importing sklearn and pandas and hacking away in a jupyter notebook. Data science problems take a unique combination of business savvy and software engineering know-how, and that’s why Emmanuel Ameisen wrote a book called Building Machine Learning Powered Applications: Going from Idea to Product. Emmanuel is a machine learning engineer at Stripe, and formerly worked as Head of AI at Insight Data Science, where he oversaw the development of dozens of machine learning products.
Our conversation was focused on the missing links in most online data science education: business instinct, data exploration, model evaluation and deployment.
Next 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.
If you like this episode you’ll love
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