# 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
584 個評分
108 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....

## 熱門審閱

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.

## 101 - Fitting Statistical Models to Data with Python 的 111 個評論（共 111 個）

2021年8月2日

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.

2020年5月23日

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

2019年9月18日

please better explanation of python videos

2020年5月31日

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.

2020年4月30日

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.

2020年7月14日

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.

2020年8月26日

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.

2020年6月28日

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

2020年4月19日

Messy, too many half-explained ideas

2021年12月26日

Week 1&2 are done properly. Theory is well presented and coding is enough.

Week 3&4 - complete disaster, complete garbage. Throughout the entire Statistics Certification (module 1, 2 and first 2 weeks of module 3) the difficulty is increasing at a slow pace and they make sure to present the knowledge at the same pace. For 3 and 4, they do not acknowledge at all that the difficulty of the topic just skyrocketed exponentially and they just do an expositional dump of 4 videos without any real-life example. What's even worse is that they send you on different websites to better understand - which you do - and than when you come back they teach you something completely new, without any connections. I just wasted a lot of time, and just skipped through all videos, and just trial&error the quizzes, skipped all the practice and assignments as they were imposibil to learn or even attempt. Whoever designed weeks 3&4 should pull down immediately from Coursera and let someone else do a better job. I absolutely advise to stay away from this 3rd module (or at least do the first 2 weeks, if you are not interested in the certification)

2020年6月6日

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