As someone pursing their MSCS in ML, which of the classes would be the most beneficial to me to take or just take first? I'm trying to get into an Applied DS/MLE-type role The courses are an advanced algorithms course that goes into more depth (trees, hashing, graphs, asymptotic complexity analysis, complex sorting, etc), algorithmic design and implementation (a lot of NP type problems and topics), and cloud architecture course (designs, business use cases, security, etc). I took a DS&A course in undergrad and I'm more than willing to self-study as well if some of these topics could be learned just as well on my own to free up class space #machinelearning #machinelearningengineer #datascience
NP problems in the algo implementation class are more important to the job, but tbh feels like advanced algos will do more for getting the job Cloud is important too, but way less important than the others
Thanks for the response, very insightful. Assuming I could fit all three at some point in the degree, do you think the advanced algos would be better to take before the algo implementation then or doesn't really matter?
Ask the faculty and former students, will depend on what and how is covered