I've been working as a data scientist for a few years now, primarily self taught using the wealth of high quality videos and other materials online.
I recently worked as a teaching assistant on an expensive and well-regarded data science bootcamp course. I did this primarily because I was curious what classroom-based training in data science was like.
I found it much less useful than online materials. It seemed like the primary benefit was just that once you've stumped up several thousand pounds, you're motivated to actually show up and concentrate. And you also get to meet some people with a common interest.
But the quality of teaching fell well below the standard online (not surprisingly, when online you have access to lectures by superstars like Hadley Wickham, Peter Norvig and Andrew Ng, with lectures that have been carefully recorded).
And on top of that, it's not a great use of time. A half hour to commute to get to the lecture. Then the lectures being in 'real time'. Online, I find myself running video lectures at 1.5 or 2x speed for large portions of the material - the 'filler' - and then having to watch, re-watch the hard concepts several times at normal speed and pause just to think.
I found that at the end of the classroom course, I had developed an awareness of the existence of concepts and techniques, but not really an understanding of them.
I've been a government analyst for around 10 years, starting my career as an economist (after graduating with an economics degree). This meant I was working with numbers a lot, and I found that my strengths were mainly in statistics and working with complex datasets rather than e.g. macroeconomic analysis.
This meant I had started to build up examples of projects which were getting close to 'data science' (the term, I think, is vague). Custom interactive data vis in d3.js, forecasting and regression modelling etc.
I also started quite a large web scraping project in my own time, which led to me needing to develop a much stronger programming skillset, and gave me some more tangible examples of having done 'data sciency' stuff.
When data science took off in UK gvt a few years ago, that meant I was well positioned to make the switch. No-one had much experience at that point, so it wasn't too hard to get a first role. I think generally, there's such strong demand for data scientists that there's a mismatch between the job spec (which look scary), and the candidates (who aren't the data science unicorns the job ads ask for). So with a few projects under your belt, it worth having a crack at applying for stuff.
In terms of being satisfied: It helps working for government because we're trying to use data to make better decisions that help people. There's definitely too many buzz words and fluff, and it's difficult to help non-data scientists understand what's hard (all the data engineering and modelling leading up to delivery) and what's not (a flashy data vis) - so it's hard to convince non-data scientists to allocate resource to the best areas. The flip side is that it's really clear there is a great opportunity to change how we do analysis for the better. So overall it's pretty good.
I've taught professionally for some time. I agree that the quality of many in-person courses is worse than the best online classes. Many popular books are also junk.
It's very hard to judge the quality of teaching, because nearly all students of a non-catastrophic class will give good ratings. Few students will take the same material from more than one teacher so they can make comparisons.
> This is particularly true in the fraught area of STEM (Science, Technology, Engineering, Math), where difficult explanations often cry out for a student to replay a portion of a lecture, or simply to take a pause while comprehension works its way to consciousness
Or to do a couple of exercises until students really grasp the subject matter. Often it feels like we can grasp something in theory, only to be stumped by practical applications. One of the greatest benefits a course could have is example problems, with fully explained solutions. If the problems are fun to work through, then it makes for a good course.
What I'd like to see is a promise on the likes of:
> Do these 100 problems and you'll grock X (where X is probability, calculus, functional programming, deep learning, etc)!
And the problems be well chosen, well explained and fun to work out.
Hah, are you describing most of my intro classes as an undergraduate engineer? I paid an egregious sum of money to teach myself physics, chemistry, and calculus out of a textbook and get tested on it.
The real value of the lectures, especially in higher level classes, is the color that the professor adds based on past experiences that normally doesn't come out in a text book.
Whether that's worth four years and a couple hundred thousand dollars...
I realised that with mathematics in school and university.
Even though I usually understood everything after hearing it once I was only able to get an A on an exam after I did the calculations at least once by myself.
If I didn't do that I would get caught up on "unknown unknowns", lose time and get a C.
Same experience here. Math et al are technique oriented at that level. You simply have to practice to get good at it, no way around it. Just like comprehending a jump shot is no substitute for taking hundreds of shots a day if you want to get good at it.
This was my exact experience during my first two years in undergrad. It was especially frustrating because my grade was varying from an A to a C based on what felt like dice rolls. I normally ended up just thinking "oh I had bad luck on that one."
It took me until my junior year to actually figure out what it took to succeed in college after breezing through high school. Since the number of exams your grade is based on is small (normally 1 to 3), I feel there is still a luck component, but you can really tilt the odds in your favor through good prep work.
So I've been taking online courses on the side for the last little while, including the author's often recommended, Learning How to Learn. I guess I was spurred on because I felt like I wanted to use my spare time doing something outside of playing games during the winter.
I have to say that watching lectures on Khan Academy, Udacity, or YouTube rekindled my love of learning. Now, I look forward to a block of spare time when I can watch a video and test out my knowledge by practicing on various quizzes from university sources, or by picking up a book with harder examples. And of course, one major advantage is that if I get stuck on a term or concept, I can rewind the lecture, or search on Google for more explanation, or find yet another YouTube video with another take on it. And if I get stuck, I recognize to take one step back and take a refresher on more fundamental concepts.
I think that's why I prefer it over being back in school where the quality and personal connection to the teacher varied so heavily, and the class/course just kept going regardless of your comprehension. We all know those one or two teachers growing up who were so exceptionally bad at teaching a particular subject that it's quite probable some students stopped going in a certain path because of that struggle. Just look up popular math or biology videos, and you'll find students who leave comments like, "Wow you just explained in five minutes what my teacher/prof tried teaching me for x months." I know so many smart people who don't think they could excel in variety of subjects because of bad experiences in school, treating it like a mark of permanence. And it didn't help that some teachers honestly believed if you didn't get it then, well tough luck, you're hopeless.
I still much prefer in-class instructions, largely for the communal experience of learning with others and because the curriculum tends to be more rigorous than online courses. But I find online videos, and write-ups far more effective at teaching me complex things over the average teacher/professor. And one can sometimes make up the difference in difficulty by taking up personal projects or finding difficult quizzes/tests online.
I think a lot of this is stemming from the deeper issue that kids are just tired of being charged absurd amounts of money for a cookie cutter syllabus based on a text book, and then tested on it, while the college shoves as many kids in classrooms and labs as possible and praising their "% of grads that find jobs immediately after graduating!"
As a reaction to that, yes I agree. However, we will be fooling ourselves if you think that anything compares being next to a real teacher. Someone who has intimate knowledge of the subject. Someone who truly cares about teaching and guiding. I can think of all the MIT OpenCourseWare lectures I watched where my mouth dropped to the floor and I said to myself "Gosh I really wish I could of had a teacher like that at my University" And I can only imagine being able to actually go up to their office and get personal 1 on 1 time with them, or work along side some of them in research...crazy, but getting that experience at some state college in middle america somewhere is just a rarity, a needle in a haystack.
I've been lucky enough to get that experience with a Udacity course. The first time Peter Norvig taught Design of Computer Programs, he was very active in the forums and in Google Hangouts. I got the chance to study from one of the best out there, and it didn't cost me a dime.
Virtual classes are better in some ways and inferior (to in person) in others.
I'm currently applying for a masters in computer science and debating between attending a program in person or online. One advantage of attending in person is immersing myself in a class room full of students, aligned with a similar goal in mind. An online course, on the other hand, offers me more flexibility (I'm working full time at AWS) and saves me time on commuting.
Although I'm constantly studying on my own and leveraging online courses, I'm leaning towards an in person program for a couple reasons. First, I recently moved to Seattle and the university of Washington (UW) offers a program tailored for working professionals; this is a great way to not only meet other people on a similar path, but, most importantly, receive in-person feedback from the professors (as well as other students). Feedback, as well as networking, is really difficult to replicate with an online format.
On an separate note, I have yet to find an online masters in CS that offers a thesis—all the programs I researched only offer course based work.
The only problem with MOOCs I've had is that they assume the message boards are usable (they're not) and basically non-existent study groups. The boards are insanely slow and they're no substitute for office hours or a study group. Study groups are hard to put together because most people don't have 2-5 people in real life interested in taking the course and have time to come together on a Friday night. Though I'm a little embarrassed to admit it, those two factors have caused me to "fudge" the homework a little bit because there's just no way I can tackle something like that on my own and finish it on time. I still learned the material, but there's always the guilt that I "cheated" because I just don't have the time to finish it without any external help.
I don't know about MOOCs, but I teach Programming online (we have a remote bootcamp) and I prefer the online experience 1000 times more than the physical experience. The amazing groups of people that we create, having folks from all around the world is invaluable. They feel comfortable, secure, and motivated.
My main perspective on the issue: If I want to learn a completely new topic, I like it to with a teacher standing in front of me. Otherwise, I can take an online course just to recall some topics and update my outdated information.
I forgot why. Because I like to interact and get answers for my unique questions from the teacher. Not a lot of online classes provide this interaction. Actually most of them are prerecorded videos and stuff.
Shucks. There I was, itching to inflict "new VirtualAbstractContextServiceProviderContainerFactoryProxyBean()" on my next enterprise client and hike up my fee accordingly.
I recently worked as a teaching assistant on an expensive and well-regarded data science bootcamp course. I did this primarily because I was curious what classroom-based training in data science was like.
I found it much less useful than online materials. It seemed like the primary benefit was just that once you've stumped up several thousand pounds, you're motivated to actually show up and concentrate. And you also get to meet some people with a common interest.
But the quality of teaching fell well below the standard online (not surprisingly, when online you have access to lectures by superstars like Hadley Wickham, Peter Norvig and Andrew Ng, with lectures that have been carefully recorded).
And on top of that, it's not a great use of time. A half hour to commute to get to the lecture. Then the lectures being in 'real time'. Online, I find myself running video lectures at 1.5 or 2x speed for large portions of the material - the 'filler' - and then having to watch, re-watch the hard concepts several times at normal speed and pause just to think.
I found that at the end of the classroom course, I had developed an awareness of the existence of concepts and techniques, but not really an understanding of them.