For my rating system I evaluate courses using a five star scale on the categories of workload, difficulty, enjoyment and relevance to my career as a software engineer. First up,

Computer Networks

Workload: 3/10

Difficulty: 3/10

Enjoyment: 5/10

Relevance: 8/10

Overall this was a useful course. Having not taken a networking course in undergrad learning about the network layers and physical infrastructure have expanded my understanding of the role that networking plays in a software application. It was certainly easier than any computer science course I took in undergrad as the exam questions followed directly from the lectures and the assignments only required minimal programming experience. I enjoyed most of the programming assignments as they involved performing various system administration tasks on a simulated network environment and helped cement the concepts taught in the lecture. However, the lectures modules (either text or video based) often reiterated the concepts of the articles linked at the beginning of each lecture. This class was particularly relevant to my work as my team is in the process of standing up a lab network environment. However, I am not sure how relevant this course is to most SWE roles.

Bayesian Statistics

Workload: 3/10

Difficulty: 7/10

Enjoyment: 4/10

Relevance: 4/10

While not relevant to my current work at MITRE, Bayesian statistics was an interesting class that exposed me to Bayesian simulation techniques and concepts. Like most math courses I have taken, Bayesian Stats was not a lot of work when things clicked right away. Unfortunately, I was often lost and found myself relying on third party material to understand the concepts described in lectures. This led me to make use of the near identical homework solutions from the previous year that were posted to the instructors webpage. My enjoyment of the course was diminished by the self study required, but I did appreciate the opportunity to dust off some of my mathematical tool set.