- UX design
- bioinformatics
- statistics for data science
- mathematical analysis
- algebra or category theory
But of course, you don't need to stick to those categories, I'd love to learn about anything new!
and then part 2: https://www.coursera.org/learn/nand2tetris2
[Both courses are free]
These are fantastic courses, by far the best MOOCs I have ever taken. I went into them knowing nothing about computer architecture, and by the end of the first course I was able to design a fully-working digital computer in Logisim.
While other courses consist of lectures + text content, with Nand2Tetris the course is practical. The authors have developed a complete software system to allow you to complete the course:
* A simplified hardware programming language to design the ALU, CPU, clock, RAM, etc..
* A hardware simulator and debugger to allow you to test the hardware that you develop
* An assembler for the assembly programs you write for the computer
* A compiler for the higher-level programs you write for the computer
I'm probably banging-on about this course more than I reasonably should, but that's just because I enjoyed the course so much!
How useful would that course be for someone who knows about computer architecture, has coded in assembly, understands how compilers work, etc...?
Alternatively, would this course be useful/accessible for complete beginners, e.g. mid/high schoolers?
You can also filter by subjects i.e Computer Science, Data Science. Humanities, Mathematics, etc.
Disclaimer: I am the founder.
I'm asking because there seems to be an extreme bias towards beginners courses, or content that is rather limited in breadth and depth compared to what a university might teach during a full masters degree.
ie, there's about 50 security intro courses (with lots of overlap of course), one "advanced" course that's been delayed for long and isn't all that advanced (Crypto II from Stanford), but nothing that even comes close to the various full-semester courses covering particular niches that I took in university (for example, we did one full semester course on each of: symmetric crypto, asymmetric crypto, side channels, "special topics" (random stuff), a cryptoanalysis lab, and 3 more niche things - and those are just the pure crypto courses, and even/especially within that area I feel I've barely scratched the surface).
These university courses cover not only more topics than Coursera covers (overall; there are many things even in this niche that Coursera has that we weren't taught, which is neat), but within each we went into considerable depth. In particular we tended to approach them from a rigorous mathematical perspective (number theory, linear algebra, statistics, proofs, etc). My worry here is that Coursera might be more geared towards people that don't need to learn the topics well enough to be actually able to use them professionally, let alone academically. ie, more like edutainment than education (no offense intended. I wasn't sure if I should include that sentence cause it might sound harsh, but I think it illustrates what I'm getting at).
We also didn't have courses that are blatant advertisements (#18568).
I don't want to put Coursera down (quite the opposite), I am genuinely interested in your answer - Is it just me not seeing everything available? Is the field I'm (slightly) knowledgeable about an outlier? Or am I missing the point of Coursera (maybe it's more focused on training industry professionals than academics than universities?) Or is it correct, and if so, is it intentional or unintentional? Is there a single field of study where Coursera could replace a university partly/largely/mostly/entirely? Will there be?
Of course, even the definition of better here is so ill-defined it probably is of no practical significance.
If you don’t mind my asking, did your school give you access to coursera to earn credit while the campus is shut down? Or is it just something interesting and fun for students who might be inclined to learn something new while they’re stuck at home? Either way, props to your school! And enjoy whatever classes you decide to take!
[1] https://www.coursera.org/learn/the-science-of-well-being
[2] https://hn.algolia.com/?dateRange=all&page=0&prefix=false&qu...
As mentioned above, I think the credit is due to Coursera more than my university; either way, at least they've let me know that something like this is possible.
It's just for fun; most of our courses are now taught over Zoom or similar services, assignments are handled digitally and if it wasn't for the low-quality webcams, you'd almost forget something is out of the ordinary.
EdX also has something similar.
It's designed to be a foundation course for subsequent social science classes, but I personally found the exposure to models from different fields of study to be quite insightful.
If you're interested, there's also a book by the professor on the same topic: https://www.goodreads.com/en/book/show/39088592-the-model-th...
Scott is one of the best teachers on the topic, and makes complex models simple and intuitive to understand.
It is a long course, but well worth it. Cannot recommend it enough.
Last but not least, Scott E. Page is a great educator. Glad to hear there is a book from him now -- his papers were a good read as well during/after the course.
https://www.coursera.org/learn/learning-how-to-learn
The primary instructor, Dr. Barbara Oakley, wrote the book, _A Mind for Numbers: How to Excel at Math and Science (Even If You Flunked Algebra)_ that isn't just about learning math.
The Philosophers Toolkit: How to be the most rational person in any room by The Great Courses https://www.audible.com/pd/The-Philosophers-Toolkit-How-to-B...
It teaches you mental models on how to think and find a solution to a problem. It explains the concepts behind each model quite well.
Topics include how to determine a valid argument, an iron clad argument, using heuristics to solve problems, among other things.
My only gripe with the course I linked is that it is an audio version of what seems to be a video version on the Great Courses website. You might want to check that out too.
[0] https://www.coursera.org/learn/experimentation [1] https://learnche.org/ [2] https://github.com/kgdunn
Overall, this course is extremely good mostly because Ng covers the essential theoretical topics and gives some practical advice. Also, the topics are explained really well and you do not need to look up additional material. Also, I really appreciate that he took the time to derive those equations while others just drop the results.
I miss professor Pascal. I hope he will create a second course!
https://www.coursera.org/learn/psychological-first-aid
Having even passing familiarity with a way to think about helping people in crisis is extremely useful when you're in the moment.
I think the pacing is great even for people who are not yet into DSP; every lecture teaches fundamental concepts that build on top of each other, and many insightful examples are given (listening to waveforms, looking at spectrograms). I'm now in week 5 (I just watch the lectures at my own pace, e.g. so far I don't need the programming part of the course), and I've already learned a lot.
https://www.coursera.org/learn/model-thinking
We live in a complex world with diverse people, firms, and governments whose behaviors aggregate to produce novel, unexpected phenomena. We see political uprisings, market crashes, and a never ending array of social trends. How do we make sense of it? Models. Evidence shows that people who think with models consistently outperform those who don't. And, moreover people who think with lots of models outperform people who use only one. Why do models make us better thinkers? Models help us to better organize information - to make sense of that fire hose or hairball of data (choose your metaphor) available on the Internet. Models improve our abilities to make accurate forecasts. They help us make better decisions and adopt more effective strategies. They even can improve our ability to design institutions and procedures. In this class, I present a starter kit of models: I start with models of tipping points. I move on to cover models explain the wisdom of crowds, models that show why some countries are rich and some are poor, and models that help unpack the strategic decisions of firm and politicians.
The models covered in this class provide a foundation for future social science classes, whether they be in economics, political science, business, or sociology. Mastering this material will give you a huge leg up in advanced courses. They also help you in life. Here's how the course will work. For each model, I present a short, easily digestible overview lecture. Then, I'll dig deeper. I'll go into the technical details of the model. Those technical lectures won't require calculus but be prepared for some algebra. For all the lectures, I'll offer some questions and we'll have quizzes and even a final exam. If you decide to do the deep dive, and take all the quizzes and the exam, you'll receive a Course Certificate. If you just decide to follow along for the introductory lectures to gain some exposure that's fine too. It's all free. And it's all here to help make you a better thinker!
I went through this not long after it was first offered following the 2012 elections, and it introduced me to the amazing world of security and human factors. There's more to secure systems design than just smart engineering. You have to give a lot of attention to people and priorities, and elections are a great place to see that in action.
It's a great introduction to fundamental concepts. After you finish, I'd recommend reading this book he co-authored, which goes into more detail and covers more advanced concepts: https://toc.cryptobook.us/book.pdf
For instance, it hasn't been widely advertised, but you can essentially take Steven Pinker's 2020 course at Harvard on Rationale: https://stevenpinker.com/classes/rationality-gened-1066
There's upwards of 20 hours of video on this course alone. You don't get that kind of depth from most Coursera MOOCs. Further, the syllabus helps narrow down a vast subject to a few months of effort and there is no better design for learning it.
Critical reasoning skills are essential! Why not learn from one of the great thinkers on the subject?
This course has the potential of ascending to the upper echelon of MOOCs. I really hope that the content doesn't get taken down. It doesn't seem downloadable..
Atleast coursera courses are easy to rip for later use...
I always wondered if there was a comprehensive way to understand how different fitness regimes and diets actually help or don't help. This course was amazing and helped my understand the fundamentals. A bit technical - goes into the basics of biology, but even without that information, this was a great course. The instructor is Dr. Robert S. Mazzeo, who has been studying, researching and teaching in the field of exercise science for over 40 years.
I have actually applied several of the principles in my workout regime and started to see the effects over the last few months. I highly recommend this one.
https://www.coursera.org/learn/design
It's taught by Karl Ulrich, a UPenn/Wharton professor/Vice Dean who helped design the Xootr scooter, Gushers, and many other awesome products. He teaches most of the course in his garage. Taking the course feels like you're his apprentice.
Don't let the idea of doing 'basic' calculus turn you away as it is an incredibly tough course. The reason it can be so challenging and the reason I find it so incredible is that it teaches Calculus through the lenses of Taylor Series. Very different to other Calculus courses and as someone who hated my first year university maths course it's helped me really come to appreciate the beauty of it!
Here's the link to the first course of 5:
* Multivariable calculus (a linear algebra based approach, and a very nice intro to differential forms)
* Applied dynamical systems (ongoing, started recently)
AI for Trading https://www.udacity.com/course/ai-for-trading--nd880
Includes an introduction to finance/markets, and goes into strategies, multi-factor models, and deep learning. Great projects too!
Or did you really write it twice?
(I've never used Emacs)
The course that brings me the most in terms of concepts I keep coming back to in everyday live is 'Introduction to Psychology': https://www.coursera.org/learn/introduction-psychology
I must confess I always find it quite hard to take psychology very serious, but this course does a good job at cutting to the bone of what psychology is about and refrains from making unfalsifiable statements.
EDIT: Almost forgot Astronomy: Exploring time and space: https://www.coursera.org/learn/astro. It comes with a very awesome free online book/website.
This is far and away the best MOOC I've ever taken. The class is genuinely challenging. It's a real Caltech undergraduate course, and you can't get away with copy-pasting code or just keep resubmitting until you pass the grader. The course is focused on real understanding of what's going on mathematically, not just learning to use some library API.
The Missing Semester of Your CS Education - https://missing.csail.mit.edu/
Contents: Course overview + the shell, Shell Tools and Scripting Editors (Vim), Data Wrangling, Command-line Environment, Version Control (Git), Debugging and Profiling, Metaprogramming, Security and Cryptography
As an aside, the world of genetics (and molecular biology in general) is beyond fascinating. I remember coming home after one the 5th or 6th lecture on Cell biology and thinking "wow, take your worst spaghetti code and imagine the pasta becomes sentient --- that is us".
https://www.coursera.org/learn/model-thinking
I've larned much more from this than from anything else on this site.
There are big jumps in reasoning and explanations so you'll need to rely on external material quite a bit (I've used Bayesian Reasoning by Barber) Also, exercises are presented in Matlab/Octave which makes them a pain to work with.
After completing the specialization, I've almost felt that you could just do a course that explains this course.
I probably won't have time to sit through it, but I've taken a course from this instructor in the past and he is pretty good. I'm really curious to know how he explains coding and engineering principles to "managers, designers and entrepreneurs".
https://www.coursera.org/learn/programming-languages
introduces the underpinnings of programming languages via standard ml, racket, and ruby.
Also, Dan is a good professor and is really enthusiastic about the subject.
i still need to go back and finish the ruby section. at that point in the course, i got distracted with other things.
https://www.coursera.org/learn/introduction-tensorflow
Data Scientist's Toolbox:
1. How to Win a Data Science Competition https://www.coursera.org/learn/competitive-data-science
2. Bayesian Methods for Machine Learning https://www.coursera.org/learn/bayesian-methods-in-machine-l...
https://online.stanford.edu/courses/sohs-ystatslearning-stat...
However, if you spend enough time sifting through the junk, there are some decent material.
The Honors Track of the UCSD series is really great.
https://www.coursera.org/specializations/bioinformatics
It's super hard and as a side effect you learn a ton about very interesting, amazing, and useful algorithms that you'd never even hear about in a top notch CS program.
Some people here in Europe take bioinformatics as a shorthand of "database management, pipeline construction, and scaffold building" --- I'm glad to see the course is more algorithm oriented (maybe with a bit of DS thrown in as well).