This class presents the fundamental probability and statistical concepts used in elementary data analysis. It will be taught at an introductory level for students with junior or senior college-level mathematical training including a working knowledge of calculus. A small amount of linear algebra and programming are useful for the class, but not required.
The mission of The Johns Hopkins University is to educate its students and cultivate their capacity for life-long learning, to foster independent and original research, and to bring the benefits of discovery to the world.
- 5 stars63.40%
- 4 stars24.31%
- 3 stars7.72%
- 2 stars2.27%
- 1 star2.27%
Good course, needs updating, since it was a first generation course. Worth taking, but make sure to review a bit of Calculus, especially integration.
I knew a lot about probability before starting this course, but I didn't know much of anything about frequentist statistics. This course helped me understand some tricky concepts.
I enjoyed the course. I wish there was perhaps a little more evaluation along the way (maybe occasional in-lecture questions), but otherwise very nice.
I like really course content but it would be better to add exlaination for quiz (especially when initial answer is wrong, now student can only see explainations when answer question right)
關於 Advanced Statistics for Data Science 專項課程
Fundamental concepts in probability, statistics and linear models are primary building blocks for data science work. Learners aspiring to become biostatisticians and data scientists will benefit from the foundational knowledge being offered in this specialization. It will enable the learner to understand the behind-the-scenes mechanism of key modeling tools in data science, like least squares and linear regression.