About a year and a half ago I started the Masters of Science in Data Science program at Eastern University. I chose this program because the total cost of attendance was affordable, and the program schedule is very flexible for working professionals. I had some apprehension about the program due to Eastern University not being the most prestigeous, recognizable name, but I figured at the very least I could walk away from the program with fundamentals and the ability to build some new portfolio projects.

At this time, I have taken 9/10 courses to complete the program, with only the Ethics Capstone course remaining. I thought this information could be helpful to others who are considering the program or any of these courses in the program.

DTSC-520: Fundamental of Data Science

This was my first class in the Masters program, and it’s what motivated me to continue. Dr. Longo did a great job explaining the material, and I really enjoyed the textbook for this course. So much so, I actually bought a physical copy of the book even though it’s free online. This class focused primarily on fundamentals working with numpy, pandas, and matplotlib, it was just a fun class. This class focused mainly on simple operations on the data, and the assignments gave me lots of repetitions to try to commit the material to memory. My only regret was not pairing this course up with another one that focused on Python data science packages, because I felt like I had to re-learn this material when I took Data Manipulation 3 courses later.

DTSC-660: Data and Database Management with SQL

This was one of the easiest courses for my given my professional experience with data and data warehousing, but I still thought the course was valuable. The textbook for this class was awesome, and I’ve referred to it even after the course. The coolest thing about this course for me, was learning about some of the more obscure functiionalities in Postgres like statistical functions, working with spatial data, and learning new ways to set constraint checks on tables. Overall it’s a good class for learning database foundations and working with SQL.

DTSC-550: Introduction to Statistical Modeling

This was one of the most challenging and one of the most foundational courses in the program. For me personally, I do not have a strong statistics background, so a lot of the material in this course was unfamiliar to me, and I struggled to internalize a lot of these concepts. This course required a significant amount of reading from the textbook, which helped me tremendously to understand the material in the lectures, but it was also incredibly dry and boring. This class really tested my willpower, but ultimately I learned a lot, and was glad I took it toward the beginning of the program.

DTSC-650: Data Analytics in R

This was one of my favorite classes in the program. I really enjoyed learning about data analytics and visualizations in R. For this course, I used the textbook more as a reference, and found it helpful for following along with lecture videos and also taking some of the material from the course a step further. The lectures and assignments went through real world examples with interesting datasets, which I found engaging and fun. The final project for this course was very open-ended, and I thought it allowed me to exercise my own creativity and skills I learned in the course.

DTSC-580: Data Manipulation

This was another course in the program that I enjoyed a lot. As I mentioned, I should have taken this with Foundations of Data Science because I had to remember how to work with numpy and pandas, but it didn’t really take that long to catch up again. Professor Andrews did an excellent job teaching this course. He paired the lectures up with Jupyter notebooks so you could follow along with the examples he was working through, which I found tremendously helpful. I also liked how he used interesting datasets to work with during the lectures and assignments. I haven’t used a ton of the material from this course outside of the class since then, but I think it’s really good to know what pandas is capable of, and how to use it to manipulate data.

DTSC-670: Foundations of Machine Learning

This was a pretty challenging class, mostly because the material was so different from anything I’ve learned before. This course was also taught by Professor Andrews, who did a good job providing thorough explanations in the lectures, which were also accompanied by the Jupyter notebooks. I spent quite a bit more time learning the material for this class since it was so new to me, but that seemed to really pay off because a lot of these concepts are foundational for other 600-level classes related to machine learning. I’ve also used a great deal of this material outside of the program as well.

DTSC-680: Applied Machine Learning

This was one of the most difficult classes of the program, but not for the reasons you would expect. I was super excited to take this course, but very disappointed by how it was executed. There wasn’t a lot of lecture material for each of the modules of the course, most of them had 30 minutes or less per module. The lectures were Professor Huddell reading the slides verbatim, and I didn’t dare read the textbook because it was so dense and hard to read. The assignments felt like they were difficult only because they were lacking instructions, and a number of questions on the assignments asked questions on things we never covered at all. Really disappointed in this class, hopefully it improves in the future because it would be great to practice more applications of machine learning.

DTSC-675: Mathematics for Data Science

Surpringly, this was another one of my favorite classes in the program. I took this course solely because I thought it would be interesting to understand the math that is happening behind the scenes of some of the machine learning algorithms, and it did not disappoint. I learned so much from this course. It was also kind of nice to take a break from all the computer assignments and do the work on paper. Professor Megonigal did an awesome job breaking down really complex mathematical concepts, and building the material in a way that helped you solidify things you learned earlier in the course, while incorporating new topics into the mix. Though I don’t think I will be doing any of this math by hand in the future, it was worth it to understand what is actually happening, and this course was very well curated.

DTSC-685: Natual Language Processing

This is another class I was very excited about, but disappointed by. This course was taught by Professor Huddell, which came with the same issues I expressed in DTSC-680: really short lectures, information copied straight from the book, reading off the slides, questions on the assignments that were not things we covered. This class didn’t really have any redeeming qualities either because the datasets used in the assignments were boring, like doing NLP on one page of Dracula, and there wasn’t a ton of coding in the course. If I could do it all over, I would not take this class again.

Overall, the majority of these classes were valuable and worthwhile. I have genuinely enjoyed the program, and I am already putting a lot of the material I learned from the course to good use outside of the program.