Georgia Tech Data Analysis for Continuing Improvement

I have officially completed 5 semesters, and 7 out of 11 classes my Georgia Tech Online Masters in Analytics (GTOMSA), and 2 classes closer to becoming a double jacket!

For anyone unfamiliar with the GTOMSA program, it's an interdisciplinary master's program between the Scheller College of Business, the College of Computing, and the College of Engineering, designed to give students the experience to identify analytics problems, become comfortable with data, and understand mathematical modeling. It's completely online and created to be flexible for all time zones and work schedules – lectures are prerecorded, office hours are recorded, open discussion forums, and flexible timing on homework and exams. You can learn more about the program here. I'll eventually blog about my experience with the full program, but this is not that blog.

This semester I took two classes Simulation and Data Analysis for Continuous Improvement.

Simulation

Simulation is taught by Dr. Goldsman – he's sort of a GTOMSA legend. He hands out extra credit like candy and really dislikes Justin Bieber. He's the most enthusiastic professor I've had so far in the GTOMSA program, which makes learning a lot more enjoyable, and he's the only professor I've seen who edits special effects into the lectures!

In addition to the great professor, the class material is really interesting and (mostly) useful. The class consisted of weekly multiple choice homeworks, 3 exams, and two simulation projects.

For my first project, I simulated a dice rolling game in Python. Basically, two people take turns rolling a die and take coins out of a pot or put coins in depending on the number they roll until they run out of coins. Each set of player turns was a cycle. I simulated the base game and two extensions with different starting coin amounts for each player to determine the expected number of cycles and distribution of cycles for each variation. It was a fun way to regain my confidence with Python, and to build my own simulation. If you're interested in reading my jupyter notebook or paper write up, let me know!

The second project was to better understand a random variable independence test of runs up and down by finding the exact distributions of number of runs up and down for small ns, and then approximate the distribution for a large n.Spoiler alert: the random package in python's random number generator does create independent variables. Again, if you're interested in seeing my python notebook or reading the paper, let me know!

Exact Distributions of Number of Runs Up and Down for Small n

This class was very statistics heavy. I was worried that this class would not be applicable to the analytics work that I do since I've rarely heard of people doing simulations at work, but the class was well worth it to better understand statistics. With a 90% confidence interval, I have a 32%-44% better understanding of statistics and simulations.

5 Stars! Two thumbs up! I highly recommend this class to anyone in the program. It's interesting, challenging, fun, and a relatively stress-free class.

Data Analysis for Continuous Improvement

This is a track elective for the Business Track. Recorded lectures were by Lee Campe, but the class instructor was James Wilburn. Despite being only 2 classes different than other tracks, the business track has the reputation for being the "easy track", I now understand why! I spent <2 hours a week on this class. There were weekly homeworks, one project, and0 exams. Our textbook was Moneyball by Michael Lewis. Despite the low effort course, I feel like I learned a lot about how to talk about continuous improvement.

I have already implemented learnings from this class at work. I used the DMAIC (Define, Measure, Analyze, Improve and Control) problem-solving approach that drives Lean Six Sigma to convince my stakeholders to define, measure, and analyze a process problem before jumping to improvements that they had planned. By doing those steps first, we were able to come up with better approaches to improve the process. And, by being able to articulate the Six Sigma approach, I was able to persuade my stakeholders to wait to act until after measuring, which I may not have been able to do without learnings from this course!

Our homeworks each week and our project were based on collecting data in real life and improving the processes. Because it was a pandemic, I didn't go anywhere, so my projects were all collecting data about myself and improving my personal life including:

How fast I can knit

If my beads were usable for knitting

My daily smoothie making process

Cleaning my closet

Reducing Cellphone screen time  (please don't judge – this was between jobs so I literally had no work to do)

Is this class worth the tuition? Probably not. Am I glad I took the class? Absolutely! I read Moneyball (great book – I highly recommend it), I had fun thinking about and collecting data in my real life, I learned how to approach continuous improvement conversations, I got really good at operational definitions, and because I got an A on my final project, I now have a yellow belt in Lean Six Sigma!

3 stars! 2 thumbs up! Fun, easy class. If you're looking for a challenging coding-based class, this is not for you.

That's it! Again, I hope to follow up this blog with more reviews of previous classes, and a review of the program as a whole.

Go Jackets! Sting 'em! And THWg!

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Source: https://www.linkedin.com/pulse/georgia-tech-online-ms-analytics-simulation-continuous-meyer