In February, Metis Sr. Data Scientist Damien Martin wrote a post on how to foster a data literate and empowered workforce, which allows your data science team to then work on projects rather than ad hoc analyses. In this post, he explains how to carefully scope those data science projects for maximum impact and benefit.
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.
As an undergraduate with a concentration in pre-med, bootcamp graduate, Ana-Elisa Gentle was on her way towards a career in the medical field. Or so she thought. Learn how she ended up pursuing data science instead.
Big data is growing exponentially. To keep up with it, data engineering — a discipline focused on collecting, funneling, and organizing big data into accessible data pipelines — is in urgent demand.
Data scientists and other data professionals can fill the gap by extending their capabilities into the world of data engineering with the Data Engineering for Data Scientists Course by Metis Corporate Training. In this course, data science professionals will learn advanced programming, database management, distributed computing, and cloud engineering.
Today, as we enter the second quarter of 2021, I am excited to share the next step in our evolution. We are building upon our recently launched specialized Live Online bootcamps and short immersive courses by announcing a new Online Flex format that will make our programs more accessible to professionals who want to continue working during the programs, need to prioritize work-family balance, and/or live in a non-U.S. time zone.