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

4.4
stars
553 ratings
101 reviews

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.\n\nthis 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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76 - 100 of 103 Reviews for Fitting Statistical Models to Data with Python

By JIONG L

Oct 15, 2020

Overall it's very good for someone who has a fair background in statistics, except for some small mistakes in slides and notebooks.

By Luis D R T

May 7, 2020

Me gusto sobre todo los modelos de nivel combinados con estadistica bayesiana ,eso fue lo mejor y de verdad invaluable del curso

By Sheng-Ta T

Jan 24, 2021

Week 3 starts to get unreasonably difficult and hard to understand. Apart from that, the course is still worthwhile to take.

By Ezequiel P

Oct 11, 2020

Great course. In my view, the lectures were too long and the assignments a bit easy. But, overall, great course.

By Antonio P

Sep 7, 2020

I think the notebook walkthroughs, while useful, could use some extra reinforcement in the statistical concepts

By Iderval d S J S

Nov 30, 2020

The course is great, but I would suggest that the subject of week 3 be divided into two weeks.

By Sunit K

May 27, 2020

Great course. It really improved my understanding of statistical modeling methodologies.

By Santanu G

Jul 22, 2021

Starting from basics of Statistical model to the depth its fine course.

By G.akhil

Mar 6, 2020

team work

By sahil f

Sep 17, 2020

None

By Sebastien d L

Jun 1, 2020

The content of this course is very thorough, but unfortunately it does not make very good use of the online asynchronous nature of a platform like Coursera. Most of the course consists of lengthy video-lectures paging through slides (and occasionally walking through notebooks). The hands-on parts seem like a second thought, and are mostly made of either reading long Jupyter notebooks, or running simple pre-coded ones to answer a short quizz. Statistical modeling is a topic that shoudl naturally lend itself really well to a "learn by doing" method, but unfortunately this course took the more traditional academic approach (nothing wrong with the later, it's just less engaging for me, especially when sitting in front of a computer).

By Carlos M V R

Sep 13, 2020

I do not feel like this course had given me great knowledge, there is a lot of theory and almost none practice of python, specially in the last two weeks. Topics are interesting and they are good as an opener to learn statistics but there is not enough python about them. I am disappointed on this specialization (specially on this course), I only finished the course because it was the one left to complete the specialization.

By Mike W

Dec 21, 2019

There is some good lecture content, but the assessments don't really give you a chance to "do stats" and demonstrate mastery of the material.

E.g., the week 3 Python assessment consists of just running Python code--you don't actually write any code--and answering the questions is as easy as, e.g., picking the parameter with the largest number.

By Xiaoping L

Feb 6, 2020

It feels like Brady is reading off the slides and squeezing in a lot of information in a 10-12 min talk. I would prefer the course slows down and would introduce a case example before jumping into models full blown. The slides look wordy. Circling out the numbers when they are mentioned in the talk would help students focus as well.

By Yaron K

Jan 26, 2019

I had never given much thought to multilevel models and their implications (for example how clustering or the interviewer effected the results). So the course was definitely interesting. However the Python notebooks that are part of the course don't give enough detail to be able to apply the theoretic material to other models.

By Amirali K

Aug 2, 2021

It needs more mathematics and theories in its content presentation to better understanding what happened in the python codes. Thank you for giving me a chance to pass this course to have an overview of statistical modeling.

By aurelien l

May 23, 2020

I was a bit disappointed by the notebooks of week3: missing some details and explanations for me.

By Ersyida K

Sep 18, 2019

please better explanation of python videos

By Mikel A

May 31, 2020

In my opinion, the course does not worth. I just complete it, as I came from the first two courses and I wanted to complete all the specialization (and I still had some days untill the deadline of the fee).

The first week is very basic. Week two, could be the most usefull if they had develope the maths behind fitting, not just a conceptual explanation. And finally weeks three and four, in my opinion, are out of the level of the course; I can't understand why to move to multivelel or Bayesian, if the basic fitting of Week2 has not been explained. In all the course, just concepts are explained, not the maths to understand in detail.

Moreover, I found too many extern lectures, apps or interviews that add little to the course.

The quiz, as in the previous course should be re-thought, I don't think are the best evaluation method. As for example, you can have wrong answer just not for running the code in Jupyter Notebook but in Spyder. Moreover, the quiz from weeks 2 and 3 about Python are ridiculous, you just have to run a code already written by the teaching stuff.

By Ron M C

Apr 30, 2020

Good job in covering the initial models, and then above average when going into the multi-level modeling, but pretty disappointed on the marginal and the bayesian. Bayesian videos started out well, but really felt superficial when it was all done. With all of the courses in this specialization, there is little to no actually learning of python, just some simple outputs -- really missed the mark in teaching us python to solve these problems.

By Ahmed A

Jul 14, 2020

I was following this specialization since course 1, unfortunately, I only found course 1 easy to understand for someone like me with good background in computer science. However, course 2 and 3 were very hard to grasp. I would suggest to start each topic with a simple visualized example to explain and demonstrate the essence before delving into the math.

By Hernan D

Aug 26, 2020

In my opinion, I think the course is not as good as the first two courses of the specialization. The explanation of the python libraries from week 3 and 4 are very poor and should be improved. However, the theoretical regression section is well explained and carried out.

By Bhanu P P

Jun 28, 2020

The course made things even more complicated. The duration of the video being more than 10 mins is only frustrating and the quiz has noting to do with the concepts. The lectures are boring and rushed. Not to the mark

By Klaas v S

Apr 19, 2020

Messy, too many half-explained ideas

By Houtan G J

Jul 18, 2020

This is the worst course I have ever wasted my time and patient on it. I don't understand how can a specialization with net materials including at most 30pages of pdf and 2 hours video get stretched for 12 weeks in lengthy boring videos mostly by young students who don't have a deep understanding of what they are talking about!! just to give you how pointless this specialization is I finished the 3rd course week 3 and 4 in 5 min by just solving quizes. this specialization explains ideas and materials which are so simple that you could grasp in 2 minute if explained by a knowledgable teacher, in hours on non sense boring repetitive shallow talks. no math explained properly, no plot explained properly. there are hours of videos that TAs going through notebooks and reading the code already written with no explanation of underlying mechanisms, which could shorten the specialization by removing them. I can't understand why this specialization is popular! maybe because you can get certificate without watching anything!