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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 st

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.

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.