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

4.4
stars
673 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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26 - 50 of 131 Reviews for Fitting Statistical Models to Data with Python

By Akash A

•

Jul 23, 2020

This was a poorly designed course compared to other online courses. A lot of different topics were covered without going into depth of any topic. Week 3 and Week 4 topics are not valuable at all.

By OSCAR A N B

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May 31, 2023

Desde mi punto de vista, considero que la especialización esta muy bien, sobre todo porque se refuerza de manera constante los aprendizajes teoricos, y al mismo tiempo existe la posibilidad de replicar el codigo dentro de los cuadernos de jupyter, ahora, evidentemente hay muchas cosas por mejorar, inicialmente el código ya esta depredado en muchos elementos y lo ideal sería corregir esto, también hay errores absurdos, por ejemplo dentro de los notebook hay errores y en muchas ocasiones no coincide el notebook con las explicaciones previas. En este ultimo curso, hubo un reto muy grande porque se explica que son los modelos de regresion lineal y logisticos, marginales y jerarquicos, y es un tema que desde mi punto de vista fue mucho más complejo de entender que los otros, entonces sería agradable que se incluyeran más ejemplos. No obstante, recomiendo enormemente esta especialización. Para mi tiene más cosas positivas que negativas, el foro es un mecanismo para salir de dudas y observar el avance de otros, además como ya lo mencione, creo que como aprendices necesitamos buenas bases conceptuales y esto nos ayudará a mejorar en proyectos propios o a entender otros cursos que tomemos con más facilidad.

By Walt T S L

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Nov 20, 2020

Great statistical lessons, I did not realize there were more regression-type models besides Ordinary Least Squares, which expanded my learning horizon, and of course, applied using Python Jupyter Notebooks. Python Code was comprehensive and enabled easy following. It was immensely helpful as I did not know how to even begin constructing a linear model study, using independent or dependent data.

By ellie c

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Aug 15, 2020

The most difficult course in this specification! The most important takeaway point of this course is to understand why we choose any model to fit our data, and how to interpret the model. Don't jump into complex math calculation, we got python to do that for us! Dr Brady did a very good job conveying those ideas to us.

p.s the forum has great discussion posts, make sure to use the forum.

By Luis G M D L C

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May 20, 2023

I enjoyed it so much! Super didactical and easy to understand! Facilitators are amazing! Best quality videos and materials! I loved the Jupyter notebooks created by the content creators since I can use them later as examples when I work on my own research applications! Thank you, so much!

By William S

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Oct 5, 2021

I have learnt to applying coding in statistical analysis. I really enjoyed the Week 4 Bayesian Statistics because the use of coding has added new favor to this topic. It makes the study a real science but not something set in the stone (textbook).

By ARVIND S

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Apr 7, 2020

A great course on how to fit models to data. Very rich on theoretical concepts and equally great on the practical aspects of using python to fine-tune your model, viewing the same each time as you modify data. Very fine course indeed

By Bharti S

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Jan 18, 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.

By Alvaro F

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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.

By Julio M M

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Dec 13, 2022

This a fundamental course for everyone who is delving into data analytics. Important note: in order to access the full potential of the lectures, it is essential to attend all 3 courses!

By Kylie A

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Jul 12, 2021

Just like the other courses in the specialization, very well thought out and planned! Up to date, great professors . . . couldn't ask for more!

By Imre V

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Apr 14, 2019

Great review of machine learning used in statistics finished up with some overview on bayesian math.

Enjoyed very much and learnt even more.

By Camila B V

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Jun 28, 2021

Excellent, the explanations were perfect and its theorical focus was the thing why I loved this course.

By Kumar R

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Jan 12, 2021

These whole three certifications lays the foundation for learning Machine Learning a more in-depth way.

By Xinyuan G

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Jun 15, 2020

The specialization covers important practical topics. I am glad to have the opportunity to explore it.

By Alexander B

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May 28, 2020

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

By Tarit G

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Jul 4, 2020

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

By Fangwen T

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Sep 10, 2023

Very Informative course. Learnt a lot statistical model fitting skills

By RODRIGO E P M

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Aug 23, 2020

An excellent introductory course to the world of statistical modeling.

By Nicky D

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Jan 22, 2020

Excellent course, really enjoyed the section on Bayesian statistics.

By Nipunjeet S G

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May 25, 2019

Very informative and the example

applications are extremely detailed

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 Hrishi P

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Jun 11, 2020

Great practical applications of statistics with Python!