Alright, so at this stage I've finished the first two classes:
Intro to Stats and Probability
Applied Stats and Experimental Design
I think these were a pretty logical place to start for the program and as I mentioned previously it'd been a while since I'd taken stats classes, so I was glad to have these on the course schedule. I think some other programs might assume you have more stats knowledge up front and not start from the bottom up.
I don't think there's too much to say about the content of the courses. Basic probability theory, common distributions, moments, MGFs, hypothesis testing, linear regression, GLMs, robust methods, bootstrapping/jacknifing. We did very little programming in the first course, but all of our homeworks were exclusively in R for the 2nd. I really love rmarkdown now, which is a package I knew nothing about.
Overall I have mixed feelings. It's funny being an adult (does 26 count as an adult?) and going back to school vs being a college student. Neither class had any tests, everything was just homework based. As a college student I would've loved that. Knowing that I'm going to try to build a career on this degree in ~2 years though... There were a lot of times throughout the quarters where I felt that if you'd asked me a question on what we learned 3 weeks ago I'd have to go to a book/online to get you the answer. Not having to study old material and only having class once per week really reduced how much information I retained. Obviously that can be remedied by me having good study habits regardless of what's asked of me, working on that
Another thing I mentioned in an earlier post was concern with programming skill. Thus far it hasn't been a problem, but we did pretty simple R. Our professor took the approach of, "I'm not going to teach you about packages because I want you to learn the basic way to do things." Additionally, the course isn't designed to teach you R, just what you need to know to solve the HW problems. I was a bit bummed that we didn't spend more time learning R, but a classmate posted this
R for Data Science online book and I've been working through that. I don't know that we're going to get into much Python in the program, which is a bit concerning.
That being said, the mental approach I'm taking is that what I really need to get out of this program is the fundamental stats knowledge, knowledge of how to approach a data science problem, and then an understanding of SOME tools that I can work it. No program will teach you all of SAS, R, Python, etc (I assume lol). It's just daunting thinking of applying to jobs at the end and being able to only work in R.
Looking ahead class-wise, I think the next class is the big one, Statistical Machine Learning. I'm really psyched for it and am going to try to develop a habit of studying beyond what's asked of me for homework. One of my concerns with the program is that after ML I dive into the "data" track. Visualization, databases, software design. I'm worried that during that year I'm going to forget all of my stats from this year.
Looking ahead in general, I think I'm very aware that I need to be doing things on the side. Next year for example, I might need some personal projects to keep my stats sharp. Additionally, I don't particularly know that I'm going to have a project portfolio to show potential employers at the end of all of this (I'll have at least the 1 capstone project), and my current resume is lacking in data analytics. I'm planning on starting some sort of blog or at least a github page and developing a project portfolio. I figure after the ML class I can jump into some Kaggle competitions or something. I've also got an idea for a baseball analytics project I'd like to do. Hoping to get that spun up in the next month or so. I'm really struggling with finding enough free time to tackle these personal initiatives though.
I'm feeling pretty good, overall. This is the stuff that makes me tick and while the 5 hour classes can be brutal I also find myself really engaged and excited. My worry at this point is if I'm going to know enough and have enough experience at the end of the program to get a real data science job. That being said, I'm only 2 classes in and am probably making a mountain out of a molehill.
Happy to answer any questions, and still planning on writing some sort of 'how did I get here?' post.