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Learner Reviews & Feedback for Fitting Statistical Models to Data with Python by University of Michigan

4.4
stars
689 ratings

About the Course

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....

Top reviews

BS

Jan 17, 2020

I am very thankful to you sir.. i have learned so much great things through this course.

this course is very helpful for my career. i would like to learn more courses from you. thank you so much.

AF

Mar 11, 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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51 - 75 of 136 Reviews for Fitting Statistical Models to Data with Python

By Prabakaran C

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Mar 17, 2020

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

By Ryan C D

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Nov 10, 2022

My 4th specialization in data science, and the best taught so far.

By Erhan K

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Jan 17, 2022

Especially the part on Bayesian Statistics are very informative.

By Gerardo C P

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Sep 26, 2024

El curso despertó mi curiosidad por los modelos multinivel.

By Hrishi P

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Jun 11, 2020

Great practical applications of statistics with Python!

By DIBYA P S

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Jun 21, 2020

good conceptual development , helped lot in learning

By Harish S

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Jan 27, 2019

Content of course was good. Some issue with quiz.

By Appi

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Sep 23, 2019

Very good instructors and very good workload!

By Debabrata A K S

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Feb 19, 2020

Very nice course. Well explained kudos.

By Sumit M

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Mar 30, 2020

Very Very Good For learning Statistics

By JamieLiu

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Sep 8, 2021

Great course ,I learned a lot from it

By SHIVAM A

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Jun 2, 2021

awesome course teaching and materials

By Emory F

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Apr 13, 2020

The classes and mentors are amazing.

By wissam m

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Jan 3, 2022

very advance and helpful course

By Jorge L C T

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May 19, 2023

Nice as an introductory course

By Álvaro M R

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Mar 22, 2021

Amazing course! Really good.

By Jiang X

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Mar 17, 2024

Very useful and practical!

By Yiyi Z

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Aug 17, 2021

This course is very good

By Jose H C

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Sep 2, 2019

It was good - Thanks.!

By João G T B

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Sep 23, 2020

Very good statistics!

By Aniket S

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Apr 18, 2020

Detailed and Precise.

By Edgar L

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May 9, 2023

great course overall

By Enrique A

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Nov 23, 2020

Thanks U. Michigan..

By EDILSON S S O J

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Jun 17, 2019

Spectacular Course!

By Kevin K

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Jan 2, 2020

Good Intro course