One of the stated aims of the course is to get you in a position where you can read the Financial Times without going cross-eyed, and it works really well to give a clarifying framework for understanding market concepts outside the course's scope.
Another I really liked was "The Modern and the Postmodern" by Michael Roth. I don't know if that course would work well "at your own pace" though, the reading and peer essay feedback was a big part of it.
It's fine in teaching you introductory (although it seems to cover more basics than a lot of other courses do, somehow) ML. But more importantly, it's a well designed course. You can see how each piece uses previous pieces and how it solves problems and edge cases not covered earlier.
Edit: It gives an important understanding on how our minds function and how we learn, which, I think, forms the basis of effective work. Knowing how to work, and being an effective learner are incredibly important qualities in life.
It's not an intense differential equations course, and I don't think you even need calculus to understand or complete the exercises. It has a lot of really great, well, explained, fun exercises like computing a gravitational slingshot, computing the spread of an epidemic, then N-body problem, and others. The exercises are solved programmatically, not with math equations.
America's Unwritten Constitution https://www.coursera.org/learn/unwritten-constitution America's Written Constitution https://www.coursera.org/learn/written-constitution
If you're American, you'll likely find both of these courses extremely interesting. What they (probably) taught you in grade/high school was very overly simplified, or just wrong. This is geared toward people who have no background in law. I don't remember there being amazing exercises to do, but there were a lot of mind blowing facts I learned about things the constitution does and doesn't cover.
I also really liked "Discrete optimization" (https://www.coursera.org/learn/discrete-optimization). At the time that I took it it also had a competitive element where you would solve optimization problems and there was a leader board comparing all the students in the current batch. That was when courses still started in batches and were free so the experience would probably no longer be the same, unfortunately.
Obligatory, Stanford CS231n: Convolutional Neural Networks for Visual Recognition [2] The assignments are excellent and will let you implement a deephish network from practically scratch, before diving into modern frameworks and applications.
[1]: https://www.coursera.org/learn/statistical-mechanics [2]: http://cs231n.stanford.edu/index.html
As a non-programmer but a decent mather, I thought it presented the materials in a way that was easy to understand. In my mid-thirties now, I feel like I could have handled this at 18 just fine--but not in a patronizing way. It was just very clear and the professor had a good sense of humor.
I just built my first time-saving Python program and it felt really satisfying. I built a few others that were cool but none actually saved me time. Very satisfying! At the end of the 3 courses (~60 hours) and some additional tinkering (~40 hours), I had the skills and that's pretty cool.
Having said that and with the caveat that these probably changed since I taken them, I recommend the following:
- Cryptography - https://www.coursera.org/learn/crypto - great introduction to the fundamentals and math behind cryptography. A lot of theory but also some practical exercises. This is my top recommended.
- Machine Learning - https://www.coursera.org/learn/machine-learning - a good introduction to the basic of machine learning; focuses on octave/matlab and does not dive into frameworks like scikitlearn or tensorflow
- Introduction to Interactive Programming with Python - https://www.coursera.org/learn/interactive-python-1 - I took a course from Rice University on Python programming through making games that was fun. As far as I can tell, this is the modern incarnation in two parts.
- Software Security - https://www.coursera.org/learn/software-security - goes into stack / overflow exploits, tools for testing, and web-based attacks
- Functional Programming Principles in Scala - https://www.coursera.org/specializations/scala - this was a good introduction to scala and functional programming - it got me thinking in a different way
- C++ for C Programmers - https://www.coursera.org/learn/c-plus-plus-a - I think this was the first coursera class I took. This course dove into the C++ STL and a lot of modern features introduced in C++11.
Anyway, I found it here: https://www.youtube.com/playlist?list=PLoRl3Ht4JOcdU872GhiYW...
The assignments are completed in Java and cover the lecture material from the week. For example, your 1st assignment is to use a union data structure to determine if a grid percolates, similar to a coffee filter percolating.
One slight drawback with the course was that it was originally published several years ago, so the forums are not well moderates much more and some of the previous quizzes are no longer available. Also Princeton does not issue certifications of completion.
Still it’s the best online course I have taken in a MOOC.
The Modern World, Part One: Global History from 1760 to 1910: https://www.coursera.org/learn/modern-world (2nd part is as great as the first one)
Also at least some courses are paid-only - initially there were plenty of free courses. For example, even if you don't want a certificate for Deep Learning specialization, just to view videos require you to enter a credit card and agree to some dim conditions probably drafted by lawyers (and expected to be read by non-lawyers).
Coursera was great initially, with well taught courses. Now it's more of a gated community, much worse IMO than it used to be. Hope there are better offerings elsewhere.
It's quite accessible and a good introduction to artifical intelligence.
- Automata Theory by J. Ullman is also really good. It used to be on Coursera but is now on EdX (https://www.edx.org/course/automata-theory)
I think public speaking is a very important skill that not enough people take the time to learn.
I quite liked the Web Development course taught by Steve Huffman (the founder of Reddit) on Udacity. It's possibly a bit dated right now.
https://www.coursera.org/specializations/cloudera-big-data-a...
1: https://chrome.google.com/webstore/detail/link-grabber/caode...
Professor Xavier Serra[1] is a highly respected veteran in the field.
It doesn't get very deep in terms of knowledge of networks, TCP/IP stack etc, it's a very lightweight course that's easy to get through, it was my gateway MOOCs years ago, instructor is great and there's great footage from the beginnings of the internet, it feels more like an interactive documentary than an online class.
Great course to learn about monetary systems, central banks and its effects on financial markets.
(Not just on Coursera, but also others. You can filter for the Coursera ones.)
The course also covers some interesting, non-standard topics. In particular, I liked the lecture on a discrete version of calculus (https://www.youtube.com/watch?v=NHa8UgWigZk) which can be used to find easy solutions to series and recurrence relations (e.g. the "discrete anti-derivative" can be used to provide quick closed-form solutions to sums of the form "n^k from n=1 to K" - an example occurs at the 5:28 mark of the linked lecture, but some background from earlier in the video will be necessary to follow along).
The lecture videos are available on Youtube (https://www.youtube.com/playlist?list=PLKc2XOQp0dMwj9zAXD5Ll...), but I would recommend working through the problems on Coursera (especially the challenge problems) as well. I would also recommend that viewers watch the videos as 1.5x speed or faster. Dr. Ghrist speaks so slowly in these videos that I found it distracting.
For those who have some knowledge of the standard intro calculus textbooks, the level of rigor and difficulty in this course is above the Stewart book that many universities use, but below the Spivak/Apostol/Courant type of book that an honors course may use.
This used to be a single course, but Coursera split it up into 5 pieces, with somewhat unhelpful names. The sequence is "Part 1 - Functions"[1], "Part 2 - Differentiation"[2], "Part 3 - Integration"[3], "Part 4 - Applications"[4], and "Part 5 - Discrete Calculus"[5]. The first four parts names are reflected in their Coursera titles, but the "Discrete Calculus" course is titled "Single Variable Calculus" instead since it contains the final exam for the overall sequence.
It's also worth mentioning that Dr. Ghrist also has other video lectures available on Youtube (https://www.youtube.com/c/ProfGhristMath) for other math courses including a sequence on multivariable calculus called "Calculus Blue."
[1] https://www.coursera.org/learn/single-variable-calculus
[2] https://www.coursera.org/learn/differentiation-calculus
[3] https://www.coursera.org/learn/integration-calculus