Food and beer. One you need and the other you don't – but if you're anything like me, you sure enjoy them both quite a bit. In this month's edition of the Made at Metis blog series, we're highlighting two recent student projects that look to improve the status quo surrounding food and beer recommendation engines.
There's no arguing it – deep learning can do some truly awesome stuff. But hear our Sr. Data Scientist Zach Miller out. When training to be an employable data scientist, these skills aren't necessary – at least not right away. Read his post to learn why.
Recommendation engines are an integral part of modern business and life. You see them (and probably use them) everywhere – Amazon, Netflix, Spotify – the list can go on forever. So, what really drives them? Join us 2/15 as Metis Sr. Data Scientist Zach Miller breaks down the complex topic. (In order to receive your exclusive invitation, apply to our data science bootcamp by 2/12.)
When our Sr. Data Scientists aren't teaching the intensive, 12-week bootcamps , they're working on a variety of other projects. This monthly blog series tracks and discusses some of their recent activities and accomplishments.
Paul Trowbridge, instructor of our upcoming Live Online Statistical Foundations for Data Science & Machine Learning course, discussed the need for a firm stats foundation, talked about his career, and more during a recent Q&A.
Trent Hauck is a Senior Data Scientist at Zymergen and the instructor of our upcoming Live Online Introduction to Data Science part-time professional development course, which kicks off January 22nd. Read about his career, his advice for new data scientists, and more.
Take a look at what's possible in just 12 weeks in this post, which features two final projects created by recent graduates of our data science bootcamp. James Cho predicted snowfall in California's Sierra Nevada mountains (which helps predict water supply) while Lauren Shareshian predicted home prices in Portland, Oregon.
While working as a software engineer at a consulting agency, Sravanthi Ponnana automated computer hardware ordering processes for a project with Microsoft, attempting to identify existing and/or potential loopholes in the ordering system. But what she discovered underneath the data caused her to rethink her career. Read her story.
We had the pleasure of interviewing Senthil Gandhi, Data Scientist at Autodesk in San Francisco, where he built Design Graph, an automated search and completion tool for 3D Design that leverages machine learning. He discusses his work and gives advice to both current and aspiring data scientists.