Master of Analytics @ Georiga Tech
Update on My Opinion of The Degree
After going deeper in the degree, I’ve found that half the courses are awesome and worth it. But half the courses are somewhat low quality and honestly a waste of time.
If you’re doing it to get a masters degree, it is worth the investment. If you’re doing it to transition into data science, I think there are better, cheaper ways. Ones where you don’t have to pay to learn, but instead, get paid to learn.
This really is a matter of gaining the skills from online resources, and then finding a stepping stone job that will further your skills, usually a data analyst role of some type. If you spend a year or two in one of these stepping stone jobs, and keep learning, and build up personal projects deployed online as a portfolio, you can go further to a data science role.
Why I Choose to Get a Graduate Degree?
The reason I chose to get a Master’s really stems from choosing Chemical Engineering as my undergrad. About the end of my sophomore year I really wasn’t sure I enjoyed Chemical Engineering too much. By my junior year, I was pretty sure I didn’t, and then by my senior year, I was positive I didn’t. During that process, I debated switching my major but to what? I knew I loved programming and machine learning. Maybe majoring in computer science or math, could be a good fit, but didn’t really seem quite what I was looking for.
I knew I wanted data science and machine learning, but that’s not really an undergraduate degree. And switching your degree can be costly financially and timely. Why not just spend that same money and time on getting a graduate degree that makes you more qualified?
Why Straight After School?
I knew it was important to me and I knew if I didn’t do grad school straight after undergrad, I might never make it back. Getting back in the grove of doing homework is no easy feat and hence I wanted a smooth transition. I actually tried to start a master’s my senior year of undergrad. I had some classes that were dual undergrad/grad that could have been counted towards a masters, alas, the University of Utah wouldn’t really let me in the end.
I started work in June, and then started school in August. Those two months sure were nice!
Didn’t You Already Have The Skills?
Yes and no. While I started working as a data scientist my sophomore year, I look back and realize I didn’t know all that much. I knew more than a lot of people, but I didn’t know nearly as much as I know now. Which is good! Growth and learning is good. For the start, I was entirely self-taught in data science. I used textbooks, YouTube, and Google as my course content, and learned a lot.
I also had a few programming classes during undergrad, as well as a stats course, a data science course, and an optimization course. This was a great foundation to start on.
I think this could have been enough to do data science tasks, but that’s not what I wanted. I wanted to be a master. I wanted to know the in’s and out’s. I wanted to be a leader. And I wanted to have the highest certification to back it up. Well that would be PhD? Yeah probably. But I’m more practical focused. I enjoy theory, but I enjoy applying it more. Plus, very few scoff at a master’s.
Why Data Analytics?
I’ll share this story in a more detailed fashion on another page, but I started working for Vaporsens the second semester of my freshman year. By the middle of my second semester sophomore year, I was promoted to a data scientist. I was addicted. I loved finding nuggets of information buried in data. I could extract insight and meaning! It was a perfect combination of programming, detective skills, domain knowledge, creativity, and business.
I was hooked.
How Did I Choose A School?
I was introduced to the Online Masters of Analytics at Georgia Tech by a colleague at ExxonMobil. He was pursuing the degree and about half way done. At the time (right before my senior year), I was heavily considering a PhD. ExxonMobil, of course, wanted me to come back. They pitched doing an master’s online degree, part-time.
The best option sounded like Georgia Tech, for a few reasons:
1) Georgia tech is a well-known, prestigious, world-class engineering institution. Always a top 10 engineering school. Number 1 in industrial engineering (which really is data science in manufacturing settings if you ask me).
2) It was cheap. They advertise the entire degree in less than $10,000, which I don’t think is quite true (I think mine will cost $12,000) but close. And still cheaper than my undergrad.
3) It was online. I wanted to work full-time and do school part-time.
Hence, I chose Georgia Tech, Go Jackets!
How Was The Application?
How is The Program?
It has been really good so far. It’s really made me learn and think like a data scientist
I’ve enjoyed the courses, for the most part. And I love the structure of the lectures. Imagine being in undergrad and being able to pause, rewind, play at 2x speed! That’s been great. I’ve also found about half the classes to be very hands on and practical, which is what I enjoy.
The classes I enjoyed the most so far, you can actually take for free. They offer a micro masters that costs $1,500 but you can also do it for free (and just not get the certificate). Check it out
I explain my feelings and this opportunity in this LinkedIn post.
What were my favorite classes?
1) IYSE 6501 - Intro To Data Analytics
2) CSE 6242 - Data Visualization
3) CSE 6040 - Intro to Programming
4) IYSE 6740 - Intro to Machine Learning
5) IYSE 6644 - Simulation
6) IYSE 6414 - Regression
7) MGT 6203 - supply chain, marketing, regression
8) MGT 8803 - finance, accounting, supply chain, and marketing