學生對 卫斯连大学 提供的 回归建模实践 的評價和反饋

4.4
242 個評分
48 條評論

課程概述

This course focuses on one of the most important tools in your data analysis arsenal: regression analysis. Using either SAS or Python, you will begin with linear regression and then learn how to adapt when two variables do not present a clear linear relationship. You will examine multiple predictors of your outcome and be able to identify confounding variables, which can tell a more compelling story about your results. You will learn the assumptions underlying regression analysis, how to interpret regression coefficients, and how to use regression diagnostic plots and other tools to evaluate the quality of your regression model. Throughout the course, you will share with others the regression models you have developed and the stories they tell you....

熱門審閱

VM

Mar 07, 2017

Awesome course. More than regression generation, they have explained in details about how to interpret regression coefficients and results and how to make conclusions. 5 Stars

PC

Nov 28, 2016

This was a great course. I've done a few in the area of stats, regression and machine learning now and the Wesleyan ones are the most well-rounded of all of them

1 - 回归建模实践 的 25 個評論（共 44 個）

Mar 02, 2016

The course contents are unfortunately very poorly designed, and this has been an ongoing trend in this overall specialization.

The weekly lecture videos are at times quite unclear and leave more questions unanswered than they answer. I (and other learners) have found glaring gaps in the course content again and again. The examples that the lecture videos use do not nearly cover the variety of datasets being used by the learners in the course; and the least that could have been done is to at least inform learners of what to expect if they were dealing with a different research question and using an alternate dataset.

I know the defense may be that the videos are meant to be introductory / gateway to a deeper understanding of that particular topic. The course moderators / instructors did a terrible job in keeping up with answering key questions that were posted in the discussion board. Yet most of the times when I was still searching for answers, looking up similar introductory videos in Youtube were far more helpful (and I didn't have to pay to watch them).

The assignments are poorly worded because the instructions are not always very clear, which in my opinion set up a lot of learners including myself for failure. The lectures are often broader than what should be considered when finishing the weekly assignment. It wasn't even clear at times of what was exactly being expected in the assignment. The rubric that I had to use to mark my peers' work meant their analysis had gaps because the criteria for assessment is not transparent AT ALL during the submission of an assignment (they are starkly different in terms of details).

All to say that I have been quite frustrated despite my best efforts to learn from these materials and much MUCH more can be done to improve the actual content of the lectures.

Please consider adding alternate resources AS WELL where learners can go seek the right answers if the answer is not provided by the lecture materials. This can help to cut back on countless precious hours for those who are juggling a full-time job and retraining / career change like I am, not to mention the countless other scenarios of other learners out there.

I am neither inattentive nor lazy, and learning this content is very important to me, if it wasn't clear through my comments here already.

One last comment, for those of you who are all very sceptical let me tell you that I know that I don't have to be a statistician to grade the course contents, and I really hope the intructors are paying attention to these and similar comments from those enrolled in this specialization and the others who have completed the course.

May 21, 2016

Similar to other courses in this specialization, the material is very nice (although slightly easy and straightforward), but the course instructors do not moderate the discussions enough to make them a useful tool. Especially when I'm paying for the specialization, I would appreciate responses to my questions.

Apr 22, 2016

I think you will get a lot out of this course/specialisation if you don't expect to be spoon-fed. I have seen comments here stating that only one dataset is used per course, not covering all the other options available. This is because you are supposed to understand the concepts presented to you and apply them to your own set and ideas.

The same in terms of grading: you should know whether your code throws up mistakes, how to fix those and whether your conclusions make sense.

It's preparation for the real world: the onus is on you to seek alternative sources of help at time (like stackflow), to correct your mistakes, to understand whether the result makes sense. Your classmates are only there to keep you to the deadline. In real life no-one will hold your hand.

Concepts are explained very well, I am finding it easy to complete two courses simultaneously quite quickly. Great specialisation.

Oct 01, 2016

The course content is very well-designed and to be honest, it doesn't go very deep into the statistical details and i completed the whole course within just a week. Still, it lays a good groundwork for future studies. Don't expect to be a data specialist after this course, but it definitely teaches you all the basic knowledge(Multiple regression, Logistic regression, examine model fit using q-q plot, standardized residuals and multiple diagnostic plots) you can start to implement to analyze your own data. And of course, if you are interested in all these topics and want to expand your knowledge, this course prepares you to furthure your studies. Great course! And I will sure recommend it to my friends with or without a statistical back ground.

Jan 14, 2019

This course is really an eye opener. I had no idea statistics would be this much fun! The way the course is set up is just beautiful. Concepts are clearly explained, and they give you such a good insight into the world of data science. Definitely gonna recommend this to a lot of people

Jun 28, 2019

I liked the course. I would recommend it to others. Do not have any complains. However, I recommend you to consider making the grading procedure more granular, and providing an example sketch of how the exercise reports may look.

Nov 28, 2016

This was a great course. I've done a few in the area of stats, regression and machine learning now and the Wesleyan ones are the most well-rounded of all of them

Dec 11, 2015

I enjoy this course so far. I like how the course entirely depends on peer grading. It will help us to get some honest feedback on our research.

Feb 08, 2016

I would do it again, even if there was no tutor support in discussion forums it teaches the arguments very good.

Mar 02, 2017

Very clear and documented videos, ability to choose between two différents statistical software: python and sas

Mar 29, 2016

I really like this training. It's good if you want a good view of applied regression.

Nov 23, 2015

This is the only course on SAS(structured) available for free till date

Jan 20, 2016

Very nice lecture videos and explanations! Totally worth it

Jun 29, 2018

Great classes. You need all of the content to manage data.

Mar 15, 2016

Great but too much stock video footage of people smoking.

Feb 22, 2016

Awesome course. Thanks Prof. I am expecting more courses.

Apr 02, 2017

great course , a must for all science students

Nov 03, 2017

It needs hard work and a lot of practicing :)

Jan 09, 2017

Background Sound is awesome haha

Jun 07, 2017

really enjoyed it

Jun 09, 2016

Again, great job!

Nov 21, 2016

Excelente curso!

Jan 08, 2017

easy to follow

Feb 16, 2016

v

Feb 06, 2016

I truly enjoyed this course and I learned a lot. I particularly liked the structure and setup. My suggestions for improvement would be to provide more background information and further reading on the subjects covered and to maybe have a handout that summarizes the most important concepts and key messages of each week.