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

4.4
estrelas
421 classificações
80 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

BS

Jan 18, 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.

AF

Mar 12, 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 80 Avaliações para o Fitting Statistical Models to Data with Python

por RODRIGO E P M

Aug 24, 2020

An excellent introductory course to the world of statistical modeling.

por Nicholas D

Jan 23, 2020

Excellent course, really enjoyed the section on Bayesian statistics.

por nipunjeet s g

May 25, 2019

Very informative and the example

applications are extremely detailed

por Prabakaran C

Mar 17, 2020

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

por Hrishi P

Jun 11, 2020

Great practical applications of statistics with Python!

por DIBYA P S

Jun 21, 2020

good conceptual development , helped lot in learning

por Harish S

Jan 27, 2019

Content of course was good. Some issue with quiz.

por Appi

Sep 24, 2019

Very good instructors and very good workload!

por Debabrata A K S

Feb 19, 2020

Very nice course. Well explained kudos.

por Sumit M

Mar 30, 2020

Very Very Good For learning Statistics

por Emory F

Apr 14, 2020

The classes and mentors are amazing.

por Jose H C

Sep 02, 2019

It was good - Thanks.!

por João G T B

Sep 23, 2020

Very good statistics!

por Aniket D S

Apr 18, 2020

Detailed and Precise.

por EDILSON S S O J

Jun 18, 2019

Spectacular Course!

por Kevin K

Jan 02, 2020

Good Intro course

por Sebastian R R

Sep 23, 2020

Excelente curso.

por Gopichand M

Mar 24, 2020

Excellent!

por A.Srinivasa R

Jun 06, 2020

excellent

por Lou B V

Sep 17, 2020

Great!

por Dr. S R

Aug 18, 2020

nice

por Minas-Marios V

May 06, 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).

por Yasin A

Apr 17, 2020

It is a good introductory course for statistics. The programming assignments were not challenging enough to cement what you have learned. The concepts in week 3 and week 4 were challenging and their approach was not good. I feel like I wasted my time. The focus should have been on multilevel model fitting rather than covering bayesian statistics. Week 4 only added more confusion. However, as an introduction course, they did a good job of presenting the concepts in the prior courses of the specialization.

por Niwanshu M

Jun 15, 2020

The videos were really lengthy, above 15 minutes videos are hard to understand for me. Although the overall specialization is really good and gives me very confidence. I would recommend everyone who wants to be a data scientist in future.Thanks Brenda and Brady T West and of course Julie Deeke and other students.

por ILYA N

Oct 05, 2019

The course is alright. They give a high-level overview of linear and logistic regression, and dip a little into Bayesian statistics.

Note that they use the StatsModel package in their practice assignments. So I was a bit disappointed I didn't get to practice sklearn, which is about x10 as popular in the field.