Learn By Example: Statistics and Data Science in R

I needed to refresh my theoretical knowledge about statistics from scratch and learn "R" from the beginning on. From all the online courses I browsed this one seemed the best fit for my needs. And it definitely delivered what it promised and seem to be one of the few if not currently the only one that teaches R and Statistics together for beginners to both with real-life examples. I loved how they used examples from the financial market. What could be improved: use one example through all stages, from descriptive to regression analysis. This would help to apply the content of the course to your own example. It would also help to integrate better the parts of the two different lecturers, which seem relatively separate so far. The course could also be styled in a more engaging way adding images instead of mostly only text and vary the background jingle that gets really boring after 82 lectures. E.g. you could use one new jingle per section to give the student an acoustic feeling of advancing, similar to jingles of levels in old computer games. Overall good and necessary work! The criticism is only about details. Thank you very much, Loonycorn!

Enjoyed this a lot, as a complete newcomer to R the pace was about right. I'd have welcomed some exercises to try to test what I'd learned as well as fuller explanations of some concepts/functions. Otherwise, really interesting and I think will be useful.

Looks like I can practice what I need and learn some key concepts. Exercises in R should be more practical. Statisticals concepts were very good.

Covers the very basics of using R to perform statistics and explains some simple statistical concepts. The course seems quite mathematically sound as far as it goes. You can follow along with the lectures, which are well put-together from a graphical standpoint, but the course would benefit from student exercises, especially in the statistical inferences case studies. It's one thing to see it done and follow along, it's another to try it out independently. Probably the best overall section of the course is linear regression. Toward the end the course content seems to thin and peter out. The sections on data visualization is cursory at best and leaves a lot of basic questions unanswered (e.g. how do I get my histogram to represent sales rather than counts?). The pace is somewhat slow, but with the course animations it isn't hard to follow along at double speed (although if you're not used to Indian English you might need to work your way up). You have to be realistic; a course like this isn't a replacement for a university level course in statistics (although it would be a good refresher for someone who's had one). Thoughtful students will have questions that simply can't be answered at this level of instruction (e.g., "What would be a good value for R-squared in my linear model?" A: it depends.) So a course like this can't really prepare you to actually work as a data scientist, this one does give you a taste for how it is done.