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Good collection, but as someone who has been slowly learning data science over the past few years, I think it needs far fewer lectures and waaaay more projects.

Good collection, but as someone who has been slowly learning data science over the past few years, I think it needs far fewer lectures and waaaay more projects. The biggest difficulty I have with learning data science is not how the algorithm or tools work, but the problem setup. Where is the data? How do I clean it? What insights can I draw from this? Which algorithms to use? What can I do with the algorithm assuming it works? Most MOOC projects decide all this for you by giving you a set of tasks to do in order and skeleton code to work off of. Your job is simply to implement a small part of whatever algorithm you learned that week and press run. This way lacks creative development, exploration, trial and error, and critical thinking skills necessary when you go out in the real world. Also, I think there should be more emphasis on publishing, even if your attempts are inaccurate. Push out a jupyter notebook to github of how you tested out a rudimentary monte carlo simulation on stock data. Or write a blog post with your attempt at determining how much silicon valley home prices will drop if 10K more family units magically existed in SF. Or try to code a random forest algorithm from scratch in a language of your choice. You don't have to be right, but publishing forces you to at least take a critical look at your work and think about the material deeply. MOOCs, at least from my experience, just encourage you to move on to the next topic the moment your code works, without diving too deeply.

shanusmagnus 10y ago
Agree. I'll add that while it's valuable to compile a giant list of topic areas, and references and resources for those topic areas, working through all the stuff on this list would take years to do in a non-trivial way. What I really prize are curricula that err in the other direction; something like: here are the handful of foundational topic areas you _have_ to know about, and the pieces of those areas that will give you the absolute minimum. But taken collectively, you will then be able to make a beginning; and be able to engage with other, more advanced, resources, as the need arises, and as the sophistication of your projects require. It's hard (at least for me) to know which subset of knowledge is required to make a beginning. That's where I need help.
- neilsharma 10y ago
  That's true -- there's definitely a bit of knowledge needed to get started, but most of that can probably be taught in 2-3 classes. I think the first problem in learning data science is coming up with a "Foundations" curriculum: - Learn a relevant programming language (R, Python) + tools (ipython, anaconda, etc) - Basic linear algebra (nothing more complex than multiplying matrices) and calculus (what are derivatives and integrals) - Intro to statistics (just to know the vocabulary -- covariance, correlation, standard error, etc.) - Rough overview of Machine Learning / AI as a whole and where its used in the world today After that comes the second problem: "What do I do next?" - coming up with several interesting but manageably small projects - getting data for these projects - access to quality advanced resources that can be consumed as needed while working on the project. A 3-month long MOOC on Neural Networks is an impractical resource. A well-written blog post (with code) or youtube video is far better. MOOCs + textbooks seem to do better for the first problem if you can sift through all the noise, but fail at the second.

shanusmagnus 10y ago

Agree. I'll add that while it's valuable to compile a giant list of topic areas, and references and resources for those topic areas, working through all the stuff on this list would take years to do in a non-trivial way. What I really prize are curricula that err in the other direction; something like: here are the handful of foundational topic areas you _have_ to know about, and the pieces of those areas that will give you the absolute minimum. But taken collectively, you will then be able to make a beginning; and be able to engage with other, more advanced, resources, as the need arises, and as the sophistication of your projects require. It's hard (at least for me) to know which subset of knowledge is required to make a beginning. That's where I need help.

neilsharma 10y ago
That's true -- there's definitely a bit of knowledge needed to get started, but most of that can probably be taught in 2-3 classes. I think the first problem in learning data science is coming up with a "Foundations" curriculum: - Learn a relevant programming language (R, Python) + tools (ipython, anaconda, etc) - Basic linear algebra (nothing more complex than multiplying matrices) and calculus (what are derivatives and integrals) - Intro to statistics (just to know the vocabulary -- covariance, correlation, standard error, etc.) - Rough overview of Machine Learning / AI as a whole and where its used in the world today After that comes the second problem: "What do I do next?" - coming up with several interesting but manageably small projects - getting data for these projects - access to quality advanced resources that can be consumed as needed while working on the project. A 3-month long MOOC on Neural Networks is an impractical resource. A well-written blog post (with code) or youtube video is far better. MOOCs + textbooks seem to do better for the first problem if you can sift through all the noise, but fail at the second.