I am very thankful to you sir.. i have learned so much great things through this course.\n\nthis course is very helpful for my career. i would like to learn more courses from you. thank you so much.
The course is actually pretty good, however the mix between basic subjects (like univariate linear regression) and relatively advanced topics (marginal models) may discourage some students.
por Alexander B•
Overall really great coure that covers a lot of material in a concise way.
por Tarit G•
Excellent course! Thanks to the instructors and the team made this MOOC.
por RODRIGO E P M•
An excellent introductory course to the world of statistical modeling.
por Nicholas D•
Excellent course, really enjoyed the section on Bayesian statistics.
por nipunjeet s g•
Very informative and the example
applications are extremely detailed
por PRABAKARAN C•
Have given me CLearcut idea about Mixed-effects and Marginal Models
por Hrishi P•
Great practical applications of statistics with Python!
por DIBYA P S•
good conceptual development , helped lot in learning
por Harish S•
Content of course was good. Some issue with quiz.
Very good instructors and very good workload!
por Debabrata A K S•
Very nice course. Well explained kudos.
por Sumit M•
Very Very Good For learning Statistics
por Emory F•
The classes and mentors are amazing.
por Jose H C•
It was good - Thanks.!
por João G T B•
Very good statistics!
por Aniket S•
Detailed and Precise.
por Enrique A M•
Thanks U. Michigan..
por Edilson S•
por Kevin K•
Good Intro course
por Sebastian R R•
por Gopichand M•
por A.Srinivasa R•
por Lou B V•
por Dr. S R•
por Minas-Marios V•
This course does a nice work introducing the concepts of model fitting, especially during the first two weeks where the emphasis is on multiple linear regression and logistic regression. Professor West does a great job focusing on the theory that one needs to know before applying any modeling, and there is quite a lot of Python material at the end that the learner will have to explore mostly on his own, since the corresponding videos are somewhat lacking in depth. Week 3, on the other hand, introduces some very interesting but advanced concepts that can be quite hard to grasp, especially for learners that haven't had much experience with classic statistical model fitting. Week 4 is mostly an introduction to Bayesian Models, but nothing deep.
Overall, I was a bit disappointed with how the course was structured, and the fast pacing after Week 2 might discourage learners. I would recommend the couse however to anyone wanting to really follow up on the material covered, especially from a Statistics perspective (not Data Science-wise).