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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

570 classificações
103 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

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.

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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1 — 25 de 106 Avaliações para o Fitting Statistical Models to Data with Python

por David Z

10 de Fev de 2019

Great lecture content, poor quiz design. Hard to apply any of the concepts that you learn.

por Kristoffer H

13 de Jan de 2019

If you don't already understand the topic don't bother with this course, the lectures are 95% hand waving and showing formulas they don't explain how to make sense of and then the quizzes are answering questions on what they didn't bother to explain.

por Michael L

20 de Jan de 2020

I was looking for an application course that would help with using Python with real world data. This was a theory course that added a small poorly explained notebook and a very brief lecture which didn't explain the code very well. If you're looking for a statistics theory course this might be fore you. If you're looking for how to use Python in the real world, I might look at other courses first.

por Flo

23 de Mar de 2020

The most impressive part is Week 2 Linear and Logistic Regression model fitting, Professor Brenda is Brilliant! She has the magic to explain complicated and abstract concept into a very easily understandable ones. Thanks her a lot! Also I was impressive on Week 4 Bayesian approaches courses. Thanks Mark Kurzeja. I think He is a very qualified teacher and prepare for this course content very careful and take it seriously. He also gives a very clear mind to understand those abstract statistic concept!

Overall, the series of Statistic with Python are impressive! You can really learn something useful and the course design is scientific. All teachers in all courses are very good!

por Viraj J

18 de Jul de 2020

Well-structured and adeptly delivered course.

A perfect introduction to regression analyses and more advanced statistical modeling procedures that are frequently used in practical scenarios to conduct in-depth data analyses and make accurate data-driven predictions. Students, independent learners and industry professionals who wish to understand the intricacies of assessing good predictive models can start off their analytical journey with this course.

por Matteo L

4 de Abr de 2020

I think the content here is great and Mr. West is a wonderful teacher. That being said I do believe the multi-level regression model topics were quite difficult to understand and it did feel like some of the content was a bit rushed in week 3. It would have been nice to go over some non-linear regression as well as I did appreciate week 4 but I am not sure these special topics were as useful as the previous topics. Weeks 3 and 4 could have been used to dilute the content a little bit to go into multi-level regression a little bit more in depth and maybe look at non-linear regression. On a side note, I though the pdf files explaining linear regression and logistic regression as extra reading were absolutely fantastic to clear things up. I am sure the course would benefit from more content like that.

por Minas-Marios V

6 de Mai de 2020

This course does a nice work introducing the concepts of model fitting, especially during the first two weeks where the emphasis is on multiple linear regression and logistic regression. Professor West does a great job focusing on the theory that one needs to know before applying any modeling, and there is quite a lot of Python material at the end that the learner will have to explore mostly on his own, since the corresponding videos are somewhat lacking in depth. Week 3, on the other hand, introduces some very interesting but advanced concepts that can be quite hard to grasp, especially for learners that haven't had much experience with classic statistical model fitting. Week 4 is mostly an introduction to Bayesian Models, but nothing deep.

Overall, I was a bit disappointed with how the course was structured, and the fast pacing after Week 2 might discourage learners. I would recommend the couse however to anyone wanting to really follow up on the material covered, especially from a Statistics perspective (not Data Science-wise).

por Jerrold

17 de Mar de 2021

Overall this course was okay at best. It DOES NOT lack depth nor are the notebooks poorly explained. Many high level mathematical concepts are covered in this course and it is not shallow at all. The python notebooks are robust, and are excellent examples of statistical coding. But it badly lacks a bridge to take the student from simple theory to high level theory, the lectures are very poorly designed and are just bad at transmitting the subject content, critical explanations of terms and mathematical processes are lacking, and I had to google many intermediate statistical concepts and explanations just to understand what was going on; this is not a course for people with no statistical and probability background

I was really disappointed with week 3 and 4 of this course and only managed to learn a few basic lessons despite being able to pass the quizzes. I would recommend that they take out course 3 of the specialization and only add it back after revising and revamping course 3.


por George P

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 Zengxiting

5 de Mai de 2020

The course inspires me to think more about how to use statistical theory in some application fields. Specially the python exercises such as multilevel regression and marginal regression is very helpful in understanding their concepts. However, in my opinion, it is better to add some more pratical Python source code or give some learning links in Github. For example, I have not understanded what is Bayesian regression even with the help of the source code given by the course until I found a source code in Github. From my own experience, a good piece of source code surpasses a long-time oral explaination.

por HUNG H L

1 de Ago de 2019

Thank you for creating this course. I have learned basic knowledge to succeed my incoming business education. I have a bachelor degree of laws and am transferring to a master of management. I used this course to learn the prior knowledge that I need about statistics. I finished this specialization and feel more confident about the numerical analysis. Thank you again Michigan Online for your great courses!

por Jafed E G

6 de Jul de 2019

I enjoy the lectures. The professor has a good speaking and teaching style which keeps me interested. Lots of concrete math examples which make it easier to understand. Very good slides which are well formulated and easy to understand

por Pierre C

6 de Ago de 2020

Really well explained, maybe a bit long on some aspects but really great overall. The best of the three courses especially considering that's the "practical one"

por Vinicius d O

18 de Set de 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

por Elliot T

1 de Jul de 2020

Awesome overview about what can we do with statictics knowlegde! Half theory, half practice with Python is a great format

por Nadine A

20 de Dez de 2019

Challenging but excellent course, especially how content was organized and examples used to explain concepts


30 de Jun de 2019

Really thorough and in-depth material about statistical models with python.

por Aayush G

29 de Mai de 2019

I must say that this is a must take course for ones who are aspiring a career in Data Science. All the concepts were laid out so beautifully and it was explained very clearly with visualisations of each real-life-examples. I enrolled in this specialisation before starting my Machine Learning so that I have all the necessary fundamentals of Statistics. Brady Sir & Brendra Ma'am are simply phenomenal, the way they explain the concepts are incredible. The concepts gets etched in one's memory.

por Tobias R

9 de Mar de 2019

The content itself is great but some notebooks were a bit unready. Otherwise great course!

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 Mark M

15 de Abr de 2020

Pretty good, but a lot more video lectures than I'd like. I don't really learn from watching, at least not while actively participating.

That said, the course is super informative and the supporting materials are relevant to what's being discussed for the week. I definitely plan to review some of the lectures to try and catch anything that I may have missed or just to reinforce the concepts that were presented.

por Walt T S L

20 de Nov de 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.

por ellie c

15 de Ago de 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.

por William S

5 de Out de 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).


7 de Abr de 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