Galvanize Data Science: Week 023 May 2016
View of pioneer square from Galvanize’s headquarters in Downtown Seattle.
In the data science program at Galvanize, you sign up for a 13 week, intensive course in Python, machine learning, statistics and more. It is meant to be a highly efficient means of transitioning into the data science and analytics field; a transition I’ve been excited to make for some time now.
It turns out that Galvanize offers a Week 0, voluntary week, specifically focused on getting the members of the cohort up to snuff when it comes to python programming and linear algebra.
As I knew, going into the program, Galvanize was going to be an intense academic challenge. Already on Day 1 of week 0, I was having to work quite hard, thinking back to my undergraduate days when I worked with vector spaces and matrix algebra. Luckily, nothing was too taxing as of yet.
I’ve been enjoying playing around with the atom text editor which is a very powerful and flexible way of writing in many different languages. In fact, I’m writing this entry using markdown right now. One of my favorite things about it is the fact that I can use LaTeX math notation right in the editor meaning I can write out complex equations, arrays and the like quite easily using the text editor interface.
The location and setting of Galvanize are both quite awesome. It is located in the heart of Seattle’s Pioneer Square in an renovated brick building (which apparently used to be NBBJ’s headquarters). Housed in the building, in addition to the Galvanize’s education programs are many startups, making the atmosphere busy and exciting. Because this week is voluntary, only part of my future cohort is here, but so far everyone, including the teachers seem very intelligent, motivated, and friendly.
I’m excited for the next 14-ish weeks of my life and the challenges and opportunities that this fellowship will bring me. My plan is to write a blog entry for each week of the program so people can track my progress, and see what a programming bootcamp is really like.