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學生對 伊利诺伊大学香槟分校 提供的 Data Modeling and Regression Analysis in Business 的評價和反饋

4.3
49 個評分
12 條評論

課程概述

The course will begin with what is familiar to many business managers and those who have taken the first two courses in this specialization. The first set of tools will explore data description, statistical inference, and regression. We will extend these concepts to other statistical methods used for prediction when the response variable is categorical such as win-don’t win an auction. In the next segment, students will learn about tools used for identifying important features in the dataset that can either reduce the complexity or help identify important features of the data or further help explain behavior. ...

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1 - Data Modeling and Regression Analysis in Business 的 12 個評論(共 12 個)

創建者 Jie W

2019年7月6日

This is a worst class you can take.

創建者 Dejan H

2019年7月4日

Very interesting course taught but a great professor. Some material may benefit from additional background knowledge or further clarification, but overall good content and delivery. This is not a beginner course and some understanding of elementary statistics is pretty much required.

創建者 Bill C

2019年8月21日

this course is easier to understand the statistical modeling, no need to learn the deep algorithm

創建者 Jesse A

2019年8月6日

One of the most poorly designed courses I have ever taken...

創建者 Aditya S

2019年8月11日

The course was easy to understand and fun to practice at home which made it exciting and useful at the same time.

創建者 Venka P C

2019年7月16日

Excellent course and great Teacher!

創建者 Pooja B K

2020年12月24日

Well teaching

創建者 Vivek B

2019年7月10日

Excellent

創建者 Kiran J S

2020年5月19日

best

創建者 Linlu Z

2020年9月26日

I have finished all the courses and assignments, how can I get the certificate that can be shared on linkedin?

創建者 Li Y

2019年7月11日

It would be better if the course is organized more in order based on teaching and homework pace.

創建者 VIOLA B

2021年8月9日

I've been waiting weeks for the feedback on the last submission but it doesn't work. Greater solutions will come in the future.