Why I'm Learning Data Science
Tags: machinelearning, python
Published: 2019-09-06 11:44:35

So I've started learning my journey into learning data science and machine learning... and the way I've chosen to start down this path is by attending a 15 week bootcamp at the Flatiron school. While I'm not much of a writer (brevity above all!), one component of the course is to express the learning process through a sequence of blogs. And here's my attempt.

## Why data science?

My background is in math, and I spent a great deal of time (up to a doctorate) studying it. When people ask me what it means to have a Ph.D, I like to joke that it means that I went to school for too long. But really, once I started studying it, the math bug bite me... it couldn't stop. Studying math can be bittersweet, you struggle for long periods wrapping your head around things followed by a brief "aha!" moment... until you sink back into confusion.  But I loved the challenge it presented and I dug into it as deeply as I could... until the day came that I needed to graduate and go out into the world. Then I quickly realized that finding a job in math was going to very difficult. This might sound the exact opposite to everything anyone has ever said about math and getting a technical job, but you'd have to understand, I studied pure math... As pure as I could find. The closest I got to any kind of application was how one theory could be used in another.

So my job search began until I finally found a job... doing something I knew nothing about. When I started, I pursued it, worked at it, and tried to master as much of it as I could. While it was still technical in nature, the further I got into my new career, the more distant my math felt and the less passionate I felt for what I now found myself doing. I sank into a daily cycle of working... and found myself missing my passion. Honestly, this was tough for me to process, but I soldiered through it trying to be as pragmatic as possible. And I discovered a silver lining, as a part of my job, I got to deal with massive amounts of data which I would have to examine, process, clean, and manipulate in order to produce desired results.

The more I worked with the data, the more I enjoyed it. It revived an old interest I had in coding and allowed me to use my math background. So I started pursuing this growing interest with the same excitement that I had for my studies in school. I started learning python, c++, and even gained experience in parallel coding and computing (which seemed like black magic to me) all as means to more efficiently work with the data that I had. And for several years, this was enough... but all the while, I wondered if I couldn't be doing this more. Finally hit a cap of sorts within my job, and I decided to make the plunge completely into a field that I could fully pursue my new passion... working with data. One of Flatiron's mantras sums it up best for me, "Always do what you love."

## Ok... but why a bootcamp?

During all of my self-study into data science, one thought continually nagged me... How can I find a way to do this full time? Demonstrating to potential employers, that I have the necessary skills isn't always easy... especially without any credentials or documented background. Additionally, how can I most efficiently fill in knowledge gaps and build my experience into a fully-fledged set of skills? I had heard of various data science bootcamps and decided to look into them. This is how I found the Flatiron school; a 15 week program that mixes lectures with real-world problems to build and cement all the skills I need to make this leap into a new career. As necessary as it was, leaving a field that you've established yourself in and starting fresh isn't easy. Having the support and guidance of a program like this helps ease the anxiety and let's me focus on studying, learning, and preparing for the future.

So here I am, I've begun the program and am wrapping up the first week. The pace is blistering...

![Speed of average lecture](https://media.giphy.com/media/1SVTwnP1jKNuo/giphy.gif)

But I'm excited to continue learning and absorbing as much as I can, and I hope that it pays off in a job in which I can do what I love.