This past Fall, Metis Senior Data Scientist Julia Lintern spearheaded an effort to revamp aspects of Metis’s Data Science Bootcamp curriculum in terms of both content and content delivery, which marked the first major curriculum overhaul in its 2.5-year history. This blog post is structured as some history/background on the contents of the Metis curriculum alongside an interview with Julia about the changes made.
As a Neural Engineering Ph. D candidate, Michael Palazzolo designed a virtual reality video game for primates. The data he collected was used to construct statistical models that charted the relationship between arm movement and vision to brain activity. While he enjoyed the interesting work – especially the parts that involved modeling and programming – at some point, it became evident that a career in academia wasn’t his desired path. He began seekings ways to fuse his academic expertise and experience with other desired skills.
So you've decided to take a huge step toward the career of your dreams and join a Data Science Bootcamp. Now is the time to start differentiating yourself for future employers. How can you do that? By focusing your upcoming data science projects on the one reason you enrolled in this program in the first place (getting a data science job!).
Metis graduate Devin Wieker is now a Data Scientist at Facebook’s Bay Area headquarters, where he’s focused specifically on Messenger growth and where he soaks in the highly technical work and environment.
Like many Metis alumni, Max Farago came from a career quite different from data science. He worked for nearly four years as a lawyer – even running his own practice – and is now a Data Engineer at PreciseTarget, where he’s one of two people with a data background at the retail-oriented startup.
This is the first entry in an ongoing series detailing the Metis approach to Data Science Education. The series will cover a variety of topics from strategies and philosophy to technologies and techniques, which have been cultivated through Metis’s firsthand experience instructing many aspiring data scientists.
Take home coding exercises are a common element of the data science interview process, particularly for entry level positions. One of our goals at Metis is to train individuals for career transitions into data science through completion of our 12-week data science bootcamp, which includes preparation for all stages of the job search process.
Now the bootcamp has ended. The daily rush of working in an intense, structured environment and collaborating with an amazing group of peers has been replaced by the hard truth that you need to find a job. No matter how incredible the career support is from your alma mater bootcamp, the onus is still on you to find that next role.
I came across a question on Quora that boiled down to: "How can I learn machine learning in six months?" I started to write up a short answer, but it quickly snowballed into a huge discussion of the pedagogical approach I used and how I made the transition from physics nerd to physics-nerd-with-machine-learning-in-his-toolbelt to data scientist. Here's a roadmap highlighting major points along the way.