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Comentários e feedback de alunos de Fitting Statistical Models to Data with Python da instituição Universidade de Michigan

502 classificações
91 avaliações

Sobre o curso

In this course, we will expand our exploration of statistical inference techniques by focusing on the science and art of fitting statistical models to data. We will build on the concepts presented in the Statistical Inference course (Course 2) to emphasize the importance of connecting research questions to our data analysis methods. We will also focus on various modeling objectives, including making inference about relationships between variables and generating predictions for future observations. This course will introduce and explore various statistical modeling techniques, including linear regression, logistic regression, generalized linear models, hierarchical and mixed effects (or multilevel) models, and Bayesian inference techniques. All techniques will be illustrated using a variety of real data sets, and the course will emphasize different modeling approaches for different types of data sets, depending on the study design underlying the data (referring back to Course 1, Understanding and Visualizing Data with Python). During these lab-based sessions, learners will work through tutorials focusing on specific case studies to help solidify the week’s statistical concepts, which will include further deep dives into Python libraries including Statsmodels, Pandas, and Seaborn. This course utilizes the Jupyter Notebook environment within Coursera....

Melhores avaliações

17 de Jan de 2020

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.

11 de Mar de 2019

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.

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26 — 50 de 91 Avaliações para o Fitting Statistical Models to Data with Python

por Alexander B

28 de Mai de 2020

Overall really great coure that covers a lot of material in a concise way.

por Tarit G

4 de Jul de 2020

Excellent course! Thanks to the instructors and the team made this MOOC.


23 de Ago de 2020

An excellent introductory course to the world of statistical modeling.

por Nicholas D

22 de Jan de 2020

Excellent course, really enjoyed the section on Bayesian statistics.

por nipunjeet s g

25 de Mai de 2019

Very informative and the example

applications are extremely detailed


17 de Mar de 2020

Have given me CLearcut idea about Mixed-effects and Marginal Models

por Hrishi P

11 de Jun de 2020

Great practical applications of statistics with Python!


21 de Jun de 2020

good conceptual development , helped lot in learning

por Harish S

27 de Jan de 2019

Content of course was good. Some issue with quiz.

por Appi

23 de Set de 2019

Very good instructors and very good workload!

por Debabrata A K S

19 de Fev de 2020

Very nice course. Well explained kudos.

por Sumit M

30 de Mar de 2020

Very Very Good For learning Statistics

por Emory F

13 de Abr de 2020

The classes and mentors are amazing.

por Jose H C

2 de Set de 2019

It was good - Thanks.!

por João G T B

23 de Set de 2020

Very good statistics!

por Aniket S

18 de Abr de 2020

Detailed and Precise.

por Enrique A M

23 de Nov de 2020

Thanks U. Michigan..

por Edilson S

17 de Jun de 2019

Spectacular Course!

por Kevin K

2 de Jan de 2020

Good Intro course

por Sebastian R R

22 de Set de 2020

Excelente curso.

por Gopichand M

24 de Mar de 2020


por A.Srinivasa R

6 de Jun de 2020


por Lou B V

17 de Set de 2020


por Dr. S R

18 de Ago de 2020


por Minas-Marios V

6 de Mai de 2020

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).