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Relatively a good refresher course, although coming from Duke U I expected better. There are too many tweaks (lack of scaffolding) that need to be made from a pedagogical standpoint from weeks 3 and 4. Probably need to make it a 5-week course and not try to cram everything into week 4 just to finish the course. Also, the time limit to take the final test was way too short and needed to be set for at least an hour (problems 2 4 11 12 take at least 20 minutes between setting up probability doing calculations and checks). Also, too many glitches with answers coming up in computer code (Over & Over) which basically buries the learner and forces them to guess if they have no clue what the code means, including the anxiety and disorientation it causes during the final. Many times taking quizzes I felt like the problems were made more for a computer programming course than a refresher course. meant to re-sharpen math skills for data science.

As it is now, the course is a much better resource for reviewing the material (which was fine for me as it was what I was trying to do) than for learning it first time. It would be much better if it had more of the same, which is why I am giving it 4 stars instead of 5. In my opinion, it is too brief; I hope to see a part 2 expanding on the material provided here. Many of the topics mentioned, and they really were mentioned more than really taught, should have been talked about in more detail. I've completed the whole course in about 4-6 hours over 2.5 days. It is a good attempt, but it is hardly a sufficient preparation for the field of Data Science; students looking to take the course should be aware of this.

TLDR: A nice and brief overview of many important concepts (sadly, missing linear algebra) which lay the mathematical foundation for getting into Data Science. Needs to be expanded upon.

First I would like to thank the instructors, Paul Bendich and Daniel Egger for doing such a wonderful job of creating a power-packed course. I really loved the course. I'm a post graduation pursuer in field of data analytics and I was looking to Brush Up my math Skills as you know data science is a multidisciplinary subject with heavy emphasis on math and stat. The course was neatly done right down from technical aspects to content, the video companion sure provided a lot of help. I would like to mention Paul because his enthusiasm and energy was contagious even though it's a video. The only Con I felt was that Bayesian theorem could've been excellent if it had 2 or 3 more different problems that was solved; Granted, that might make the content a little bit longer than a 4-week course but I felt Sets could be little bit trimmed and Bayesian Theorem little more enhanced.