GTOMSA Course Review

I am almost done with the Georgia Tech Online Masters in Analytics program! If you want to see my thoughts on the program holistically, check out my blog here: Curious about the Georgia Tech Online MS in Analytics?

I wanted to use this blog to document my thoughts about each class that I’ve taken so far. I hope this is helpful for anyone considering the program, or anyone early in the program to decide what classes they want to take.

As I have mentioned before, these are my opinions so take them with a grain of salt. Some of my favorite classes have been my friends least favorite classes and vice versa! You will likely have different opinions if you have different interest or background than I do. Also, these classes have probably changed; the GTOMSA program takes feedback very seriously and are continuously iterating to make the program better. For aggregated difficulty, workload in hours/week, rating, and other reviews see OMSCentral or omsa.ga (the courses tab).

In this blog, I will review the following courses:

• CSE 6040 Computing for Data Analytics
• ISYE 6501 Intro to Analytics Modeling
• MGT 6203 Data Analytics for Business
• MGT 8803 Business Fundamentals for Analytics
• CSE 6242 Data & Visual Analytics
• ISYE 6644 Simulation (Linked)
• MGT 8823 Data Analytics and Continuous Improvement (Linked)
• ISYE 6414 Regression Analysis

Fall 2019 - CSE 6040 Computing for Data Analytics

OMSCentral: Difficulty 3.21/5; Workload 9.75 hours/week; Rating 4.44/5

Computing for Data Analytics is a great course. Each week, you learn something new about modeling, data collection, data preprocessing, data pipelines, or data visualization, and you build many of them from scratch (ie no packages) in python. Some data topics we covered were pandas, SQL, sparse matrices, and compression. Some models that we covered were regression, classification and k-means clustering.

The homeworks were very challenging and 50% of the grade.? The other 50% was from 3 exams.

I'll be honest, this class was hard. I knew basically 0 python going into it, but I had "coding proficiency" in java in college, so I understood coding concepts. I found a friend of a friend in the course, and we met up weekly at a Starbucks for about 4 hours to go through where we each got stuck on the homeworks. I'm not sure I would have gotten an A in the class without her! We ended up taking most classes together, and helping each other a lot!

Spring 2020 - ISYE 6501 Intro to Analytics Modeling; MGT 6203 Data Analytics for Business

ISYE 6501 Intro to Analytics Modeling

OMSCentral: Difficulty 2.88/5; Workload 10.07 hours/week; Rating 4.25/5

ISYE 6501 Intro to Analytics Modeling is similar to CSE 6040 Computing for Data Analytics in that you learn how to implement a bunch of models.? The key differences are this class is taught mostly in R (not python), and it teaches you how to implement packages for each model (instead of coding from scratch).? In this class we learned classification (clustering and SVM), data preparation, basic time series models, regression, tree-based models, variable selection, basic simulation, and how to deal with missing data.?

Also similar to CSE 6040, this class was hard. I knew basically 0 R going into it. This time I had two friends in the class, and we met weekly for about 4 hours to go through where we were stuck in the homeworks. I learned so much from them!

MGT 6203 Data Analytics for Business

OMSCentral: Difficulty 1.9/5; Workload 4.51 hours/week; Rating 2.3/5

MGT 6203 Data Analytics for Business, or as I like to call it, "how to apply linear regression in R to many areas of a business".? This class was useful, a great primer for ISYE 6414 Regression Analysis, but it was really, really easy - short lectures, multiple choice homeworks, and easy exams.? I've heard they've made it slightly harder since I've taken the class, but it's still an easy course.

We learned linear regression, indicator variables (i.e. categorical variables in regression), interaction terms (i.e. two variables multiplied together), transformations of variables, logistic regression, and how to use regression to analyze experiments.? Then we learned about finance, marketing, and operations management, and how to apply regression in each scenario.

Summer 2020 - MGT 8803 - Business Fundamentals for Analytics

OMSCentral: Difficulty 2.95/5; Workload 7.72 hours/week; Rating 2.43/5

This class is known as the “mini-MBA”. It covers Accounting, Finance, Supply Chain, and Marketing, with an optional unit on Business Strategy. If you have an undergrad business degree, or have taken a college level course in all 4 areas, you can get a waiver for the class. Each unit is completely self contained, with their own homework. There is a midterm covering the first two topics (accounting and finance - 40% of the grade), and a non-cumulative final covering the other two topics (supply chain and marketing - 30% of the grade), and 3 homeworks (30% of the grade).

Finance and Accounting: These modules were by far the hardest. There was a lot of material, a lot of formulas, and a lot of memorization. But, it was extremely useful. I can now confidently read financial statements (balance sheets, income statements, statement of cashflows); calculate net present value (NPV); understand amortization; calculate internal rate of return (IRR), payback periods and weighted average cost of capital (WACC) of various scenarios; and, in turn find quarterly earnings calls much more interesting. The homeworks and exam were very challenging.

Supply Chain and Marketing: These modules were substantially easier. In supply chain we learned basic strategies like the news-vendor model, the bullwhip effect, and centralized vs decentralized supply chains. The marketing module was honestly my first exposure to marketing - I used to think of marketing as advertising, but this module exposed me to how integrated marketing can and should be at a company.? I learned about the four Ps: product, pricing, place, and promotion, and how marketing isn't just about selling an existing item, but building demand and working with product development to make sure the product fits the market. We did a laundry detergent case simulation where we could change the product and modify the price and promotion spend. I learned a lot about marketing and supply chain, and the homework and exam were easy - the best of both worlds.

Overall, I loved this class! It was a great overview of business, gave me a holistic view of how companies operate, and helped me understand quarterly earnings calls/reports. I learned a TON, which is all you can really ask for!

Fall 2020 - CSE 6242 - Data & Visual Analytics

OMSCentral: Difficulty 3.23/5; Workload 15.53 hours/week; Rating 3.01/5

Ah DVA - well known to be the hardest required course in the program, and boy did it live up to its reputation. Not only was the content was difficult, but also homework questions had typos, setting up all of the different programs and environments was a huge pain (the only time I have yelled at my boyfriend in our 6+ years of dating was when he asked me to go for a walk 4 hours into trying to get Docker to work on my machine), and the project requirements are more tedious and restrictive than they need to be.

That being said, now that it’s over (hindsight is 20/20, am I right?) it’s probably the most important class in the program. You learn/use python, python Flask, APIs, tableau, javascript/D3, SQLite, pyspark, google cloud, AWS, docker containers, and probably more. ?It was a whirlwind.

More importantly than the tools you learn is the analytics mindset and experience. Most people in the GTOMSA program are doing this for a career change, so this is their first experience of doing an analytics project.? You learn how to try and pickup new tools, how to work with and clean real datasets (as opposed to the nice, curated data you get in most courses), how to multitask, how to collaborate on an analytics project, and how to use industry tools for free that might be too expensive or too intimidating for someone to try on their own. Of all classes in the GTOMSA, this course teaches you how to be an analyst.?

The course grade consisted of four multi-week homeworks (50% of the grade) and a semester long group project. (50% of the grade)

• Homework 1: Using a public API to pull data, visualizing it, SQLite, D3 intro, and Python Flask intro (a good gauge to see if you’re ready for the class)
• Homework 2: Tableau intro, and a whole lot of D3 (D3 is HARD if you’ve never seen it before, but this is the last time you’ll have to use it - so just get through it)
• Homework 3: Pyspark using docker, data bricks, AWS, GCP, and Azure (the hardest part of this homework is all of the setup!). This homework is more to expose you to tools than challenge you from a technical perspective
• Homework 4: Python heavy - page rank algorithm, building random forest classifier, and scikit learn
• Group project: The prompt of the project is basically “use a big dataset (ie not something you can load into excel), create an interactive visualization incorporating a non-trivial algorithm, and write a paper about it”.? Check out my group project poster video here!

Riesling’s Tips to Survive DVA:

• FIND A GOOD GROUP.? I repeat, FIND A GOOD GROUP! ?
• Why? You’re going to spend dozens of hours with these people, and relying on them for 50% of your grade.
• How? Before the course started, I got two friends to take the class with me. We knew about the skills needed for the project from friends who had taken it previous semesters, course reviews and slack channels.? So, we met up and created a write up about our group, what skills we excelled at, and what skills we lacked to post on slack and attract other talent in the class. We reached out to 1 person who wrote up his own bio, and had a bunch of people reach out to us so we got to pick who we wanted to join.
• My experience: We had so much fun.? 5/6 of us took Simulation together the next semester, and 3/6 of took regression the next semester.? Find a good group! It’ll pay off in future semesters!
• When picking your topic, ENSURE THERE IS A GOOD, CLEAN, USABLE, FREE DATASET TO USE
• Why? You only have like 8 weeks for the project, and you cannot change your topic.? You don’t want to spend the first half cleaning the data.? Or, even worse finding out your dataset isn’t usable for the problem you want to solve or finding out you’ve hit an API limit and it is no longer free and needing to find a new dataset
• How? Spend the time up front before submitting your proposal playing with the data.? More time upfront will save you a major headache later
• My experience: We used census data (easy), but we also wanted to add in grocery stores with a google API.? We realized the google API stopped being free, and, we had no way to join census data (listed by census tract) to google API data anyway!? In the end, we used the foursquare API and joined on GPS coordinates using a python package to convert census tracts and addresses to GPS coordinates. We survived, but it would have saved us a headache if we had considered these impediments before committing to adding grocery stores to our project
• Take this class by itself
• Game plan your total points.
• Your project is worth 50% of your grade.? Do that well.? Know the topics of the 4 homeworks ahead of time and be okay with bombing one.? I only spent 2 hours on Homework 4 and got a 15%, but still got an A in the class!? It wasn’t worth the stress and effort to get a higher grade on the homework, and it wasn’t worth the risk of not having time to finish the project.
• Don’t be afraid to time time off from work
• Doing a masters program is hard.? You’re going to spend 10-20 hours per week on this class. Take a vacation day every once in a while to catch up or veg out.

Spring 2021 - ISYE 6644 Simulation; MGT 8823 Data Analytics and Continuous Improvement

See my previous blog post: Georgia Tech Online MS in Analytics - Simulation and Data Analysis for Continuous Improvement Semester Review

Summer 2021 - ISYE 6414 Regression Analysis

OMSCentral: Difficulty 2.95/5; Workload 8.87 hours/week; Rating 3.37/5

Regression analysis has a polarized reputation. People fall into the following camps:

1. Regression was one of the best classes in the OMSA program because it is the most applicable to my everyday work. It helped me understand the mechanics of regression, how to tune and improve regression models, and most importantly, when regression is not the right model to use. And, she gives you coding examples!
2. The class was horrible. The professor was hard to understand, she makes declarations but doesn’t explain them, the multiple choice homeworks and exams are overly tricky in wording, and there isn’t enough time for the coding portion of the exam.

I think I’m in camp 1. Yes, the professor is hard to understand, but she provides transcripts of the lectures to read along to. Yes, the sometimes she doesn’t explain concepts, but the professors and the TAs are very responsive on Piazza for clarification, and there are recomended textbooks. Yes, the multiple choice questions are tricky, but I don’t think unreasonable. And yes, the coding exam can be stressful. My favorite part? She gives coding examples! These are sooo helpful for the homeworks, exams, and for future reference! My biggest complaint about this course is most of the content is covered in MGT 6203: Data Analytics for Business. This class just goes more in depth to model fit, model assumptions, and statistical inference. Additionally, the professor states statistical properties without explaining them; taking Simulation before this class was very helpful in understanding all of the concepts.

Another thing that I love about the course is content is released every 2 weeks, with due dates every 2 weeks. This gives me more flexibility and lets me either distribute the work, or plan ahead and cram everything into 1 week if I want to have more free time the other week.

The class has 4 homeworks (15% of the final grade), a midterm (40% of the final grade) and a final (45% of the final grade).

Overall, I think the pros of this class outweigh the cons. So much so, I’m going to be taking Time Series Analysis with the same professor next semester!