if you follow the above link, you'll see that you could join the Harvard health plan, even as a family. If you had your own, your could opt out. You can't be a 3/4 or above time student without health care coverage in MA. It's the law.
It was the standard Harvard Extension ALB [0]. You can optionally choose a Field of Study, which was CompSci in my case. And then if you qualify as a special student you can take almost any course in the college or grad school. It's a very flexible program. You get out what you put in.
I think that's true for the technical portion, or more precisely someone who has recently refreshed their knowledge in college junior level algorithms and data structures. (equivalent of Harvard CS121 at minimum, ideally CS124).
I don't why Coursera gets so much attention for an online CS masters when it has been available for years at Harvard. Even Stanford relaxed the prior constraint of being in a member company for their online masters in CompSci.
Also, I got rejected twice (!) at Microsoft and didn't even get an interview at Amazon, so I can sympathize with "GoogleyAsHeck." Clearly hiring is a very noisy process with a lot of randomness. A lot of it is beyond your control no matter how much preparation is done.
It took three years. As I mentioned elsewhere, I tested out of a semester of work. Unlike undergrads, I was used to working year round, so I took January term and summer classes to reduce elapsed time.
While some remote students have been able to build quality relationships with professors, it is far easier if you are local.
I'm trying to stay anonymous and am concerned I may have already revealed too much. Let's just say it is a technical role that involves coding and leave it at that.
I was fortunate enough to be in a situation where I could pursue it full time. I started part time, really liked it, and decided to complete it full time.
I'm actually not a fan of CS50. I never took the course, but I went through the online material did the first few weeks of assignments. It is very broad and very shallow. It is also very hard and discouraging without some guided assistance. The students who take it for credit get a lot of help.
For a first CompSci course, the edX Python course is better, IMO.
I didn't have any prior degree so I couldn't go straight to masters. Not even any transfer credits. But I was able to test out (via CLEP) for the equivalent of 4 classes including the language requirement, which saved me a semester.
I had studied a lot of ML but all the technical questions were focused on traditional algorithms, so this was still a little retro. The math requirement was minimal but I had more than enough from the ML coursework.
Bamberg's Math 23a / 23c is phenomenal set of classes at Harvard. You will work like a dog but learn almost all the math you need for ML.
Harvard Extension is a bargain compared to most colleges. $1500-$2500 for most 4 credit classes. Don't tell those smart college students that you are getting the same class for less than half the price. I tried the online thing (Coursera, edX, Udemy, etc.) but was never as motivated as competing against real students in a real class with deadlines that have consequences. Fear of failure is a great motivator :)
> Did you do all classes online or did you go in person for some / all?
It was a mix. The online classes were more time efficient. The person classes built rewarding personal relationships with faculty and older students.
> Did you work in technology previously?
Yes, was primarily self taught. Only had access to a lousy community college in my teens. Turned me off school then.
A lot of it was stuff that is basic to someone with years of work experience:
* Start assignments as soon as they are given out. Stupid, right?
* Don't be shy about asking lots of questions. Don't be egotistical or afraid about asking for clarification.
* Build personal relationships with the professors and TAs. They are there to help. This is not an adversarial relationship. Show genuine passion for the material.
* Find real-world analogies or applications of the theory. An intuitive understanding is far more important memorization. It is also far more motivational. By seeing how a technique can be used to solve a real problem, the value becomes tangible.
* Be curious. You are there to learn, which means digging beyond the provided material. So many students are sadly focused on the grade or assignment, not on the learning.
Google must be doing something right in their candidate triage algorithms. I applied for one job and they suggested another that was a better fit, which I hadn't even considered.
I applied to a bunch of places and only Google seemed to perceive the whole package instead of myopically focusing on my recent education or last job. The whole process was much smarter than any other company in the interview cycle.
It is so much easier being a student now, mostly because of the amazing resources available online. In the bad old days, if you didn't understand a topic and had a bad professor, you were screwed. Today, a two second search will find lectures, articles, blog posts, free books, SO answers... It's amazingly better.
The tradeoff of ease is the huge variety and diversity of knowledge to learn. A CS degree in ML can have a completely different curriculum than a CS degree in algorithms or web design or OS/systems/databases. There is much more to learn.
I am finishing my bachelors in CompSci at Harvard Extension and just got hired by Google. And I've got about 20 years on you. So yeah, absolutely, this can work!
It was fun and challenging competing with top computer science students. In the long run, my organizational skills, focus, determination and world experience outweighed their raw brainpower and better memory.
This is another Ric Fulop company, notoriously the founder of A123 Systems [1]. That company also raised "a ton" of money but ultimately blew up, filing for bankruptcy. I'm skeptical of this new endeavor because of both economics and technology.