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.<p>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?<p>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.<p>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.