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Voltar para Fitting Statistical Models to Data with Python

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

4.4
estrelas
501 classificações
91 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
17 de Jan de 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
11 de Mar de 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 — 91 de 91 Avaliações para o Fitting Statistical Models to Data with Python

por Mike W

21 de Dez de 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.

por Xiaoping L

6 de Fev de 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.

por Yaron K

26 de Jan de 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.

por aurelien l

23 de Mai de 2020

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

por Ersyida K

18 de Set de 2019

please better explanation of python videos

por Mikel A

31 de Mai de 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.

por Papadopoulos G

2 de Jan de 2021

Overall a fair course , but i felt it was a bit too fast paced and more focused on theoritical statistics with serious lack in Pyrhon practising.I mena the notebooks were a great deal but the instractions on them and the video coures were not what i expected compared to previous lectures.It was a little bit difficult to follow on with the theoritical courses - weren't explanatory enough for me. And for sure i needed more Python practsing , lecturing and of course assessments.

por Ron M C

30 de Abr de 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.

por Ahmed A

14 de Jul de 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.

por Hernan D

26 de Ago de 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.

por Bhanu P P

28 de Jun de 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

por Lorenzo G R N

9 de Jan de 2021

This course wants to do too much. Week 3 and week 4 are like a math course but without the math so it's really hard to understand what's going on.

por Klaas v S

19 de Abr de 2020

Messy, too many half-explained ideas

por Houtan G J

18 de Jul de 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!

por Akash A

23 de Jul de 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.

por VENIGALLA N S V J

6 de Jun de 2020

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