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

4.4
stars
674 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.

VO

Sep 17, 2019

Good course, but the last of three was the most difficult one. I hope that it were a good introduction to the fascinating world of statistics and data science

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126 - 131 of 131 Reviews for Fitting Statistical Models to Data with Python

By Diaconescu T

•

Dec 26, 2021

Week 1&2 are done properly. Theory is well presented and coding is enough.

Week 3&4 - complete disaster, complete garbage. Throughout the entire Statistics Certification (module 1, 2 and first 2 weeks of module 3) the difficulty is increasing at a slow pace and they make sure to present the knowledge at the same pace. For 3 and 4, they do not acknowledge at all that the difficulty of the topic just skyrocketed exponentially and they just do an expositional dump of 4 videos without any real-life example. What's even worse is that they send you on different websites to better understand - which you do - and than when you come back they teach you something completely new, without any connections. I just wasted a lot of time, and just skipped through all videos, and just trial&error the quizzes, skipped all the practice and assignments as they were imposibil to learn or even attempt. Whoever designed weeks 3&4 should pull down immediately from Coursera and let someone else do a better job. I absolutely advise to stay away from this 3rd module (or at least do the first 2 weeks, if you are not interested in the certification)

By Shen T

•

Dec 20, 2023

Starting from week 3, the lecture becomes very confusing with a lot of unexplained terminologies. Very few examples were given to help understanding the concepts.

By VENIGALLA N S V J

•

Jun 6, 2020

My final specialization course certificate not received, even after completing all courses in this specialization.

By Fabian B

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Mar 21, 2022

too much material, way too little practical examples in python

By Justin H

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Mar 25, 2023

Week 3 and 4. Really painful. truly...truly...painful..

By Mona R

•

Apr 7, 2023

bad