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 The daily rush of working in an intense, structured environment and collaborating with an amazing group of peers has been replaced by the hard truth that you need to find a job.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.
We recently hosted a virtual alumni panel featuring four recent graduates of our Data Science bootcamp. These grads are now working for the Philadelphia 76ers, Elsevier Labs, and Booz Allen Hamilton in a variety of data science roles, with a range of interesting responsibilities. During the hour-long event, Metis Director ...
"Find your passion, think about what you're interested in – and it's not just data science, it's something else. We all have interests and passions and things that really excite us, and there's always a way to apply data science to it. That's really cool, there's a lot of data out there."
"It's more than just a deep learning or machine learning framework. It's a mathematical computation library. That means that it has the flexibility to be able to create and experiment with brand new models instead of just being stuck with out of the box models that some other libraries provide."
At Metis, we have the privilege of teaching data science to people from around the world. Our students, alumni, and staff benefit greatly from the diverse range of perspectives and talents brought into the classroom every day by people of all nationalities, religions, orientations, and backgrounds.