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學生對 密歇根大学 提供的 Fitting Statistical Models to Data with Python 的評價和反饋

4.4
583 個評分
107 條評論

課程概述

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....

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BS
2020年1月17日

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
2019年3月11日

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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51 - Fitting Statistical Models to Data with Python 的 75 個評論(共 110 個)

創建者 Emory F

2020年4月13日

The classes and mentors are amazing.

創建者 Wissam Z

2022年1月3日

very advance and helpful course

創建者 Álvaro M R

2021年3月22日

Amazing course! Really good.

創建者 Yiyi Z

2021年8月17日

This course is very good

創建者 Jose C

2019年9月2日

It was good - Thanks.!

創建者 João G T B

2020年9月23日

Very good statistics!

創建者 Aniket S

2020年4月18日

Detailed and Precise.

創建者 Enrique A

2020年11月23日

Thanks U. Michigan..

創建者 Edilson S

2019年6月17日

Spectacular Course!

創建者 Kevin K

2020年1月2日

Good Intro course

創建者 Sebastian R R

2020年9月22日

Excelente curso.

創建者 Mogaparthi G

2020年3月24日

Excellent!

創建者 A.Srinivasa R

2020年6月6日

excellent

創建者 Lou B V

2020年9月17日

Great!

創建者 Dr. S R

2020年8月18日

nice

創建者 Edward J

2021年1月12日

Another interesting course - the final one in this specialisation - but the difficulty really ramped up in Week 3 after the final peer marked assignment. I had been so impressed with the clear explanations, revision and review, and the opportunities to apply new knowledge. However, it all became very abstract - I thought Mark did a good job but perhaps Bayesian is a whole different specialisation. Overall, I really enjoyed the specialisation and I am pleased to have received a good grounding in statistics ahead of my Data Science diploma. Thank you to Brenda and Brady especially but everyone was very strong and the future is bright with some enthusiastic young talent coming through at Michigan. Edward

創建者 Yasin A

2020年4月16日

It is a good introductory course for statistics. The programming assignments were not challenging enough to cement what you have learned. The concepts in week 3 and week 4 were challenging and their approach was not good. I feel like I wasted my time. The focus should have been on multilevel model fitting rather than covering bayesian statistics. Week 4 only added more confusion. However, as an introduction course, they did a good job of presenting the concepts in the prior courses of the specialization.

創建者 Fanchen H

2021年4月4日

Overall, this course clearly conveys the general ideas about model fitting. The python labs of week 2 and 3 are helpful. However, the materials for week 3 and week 4 lectures are not as good as others in this series. I understand that the author tend to avoid confusing learners with complicated math. Unfortunately, jumping to piles of conclusions without any necessary justifications leaves learners lost.

創建者 NIWANSHU M

2020年6月15日

The videos were really lengthy, above 15 minutes videos are hard to understand for me. Although the overall specialization is really good and gives me very confidence. I would recommend everyone who wants to be a data scientist in future.Thanks Brenda and Brady T West and of course Julie Deeke and other students.

創建者 ILYA N

2019年10月5日

The course is alright. They give a high-level overview of linear and logistic regression, and dip a little into Bayesian statistics.

Note that they use the StatsModel package in their practice assignments. So I was a bit disappointed I didn't get to practice sklearn, which is about x10 as popular in the field.

創建者 DHRUV D

2020年9月10日

python codes were pretty tough to undertsand in the end but the concepts though difficult to understand the faculty did there best possible to make it understand. Python codes should have got little bit more time to be explained

創建者 mohamad z

2021年9月28日

t​his course consist of very important topics , they give you an overview of these topics and you have to dive in .

some information hard to understand and other easy .

i​ enjoyed learning this course

創建者 Fernando S

2020年10月21日

Overall, the course was a great refresher of statistical theory and application with some great Python exercises. However, some of the Python coding instruction itself could have been more detailed.

創建者 sam m

2020年6月16日

A great introduction to regression and bayesian analysis in python. I get that the content is hard, but they sum it all well. I would recommend for those who have prior knowledge of statistics.

創建者 YAĞMUR U T

2020年9月22日

The code examples may be more precise with detailed comments. Some codes are not understood, in other words codes can be refactored in a way that can be more suitable for reproducible studies.