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学生对 宾夕法尼亚大学 提供的 Modeling Risk and Realities 的评价和反馈

2,004 个评分
289 条评论


Useful quantitative models help you to make informed decisions both in situations in which the factors affecting your decision are clear, as well as in situations in which some important factors are not clear at all. In this course, you can learn how to create quantitative models to reflect complex realities, and how to include in your model elements of risk and uncertainty. You’ll also learn the methods for creating predictive models for identifying optimal choices; and how those choices change in response to changes in the model’s assumptions. You’ll also learn the basics of the measurement and management of risk. By the end of this course, you’ll be able to build your own models with your own data, so that you can begin making data-informed decisions. You’ll also be prepared for the next course in the Specialization....


Apr 12, 2018

covers good amount of material and exactly what is in the outline, presented with enough detail to follow. Good walk-through of the spreadsheets helps understanding, easy to follow along and practice.

Dec 18, 2016

Material was very well presented. Week 3 was challenging, but taking time to print out the slides, work through them rigorously proved very helpful. I found all sections very, very informative.


226 - Modeling Risk and Realities 的 250 个评论(共 282 个)

创建者 Fereshteh A

Apr 22, 2019

I was hoping for more complex modeling techniques.

创建者 Mohammed A K

Dec 12, 2016

Very Knowledgeable content and easy to understand.

创建者 Emilio v D

Sep 20, 2018

Week 3 was challenging, otherwise great program.


Feb 27, 2020

It could be better explained by professors.

创建者 RAJ K A

Oct 11, 2016

Nice course. But's it's a bit too simple.

创建者 Thu P

Feb 23, 2018

Much detailed! Very easy to follow.

创建者 Alejandro A B

Jun 26, 2016

Muy buen curso, excelente material

创建者 Tarun S R

Apr 21, 2019

Teacher are good in this season.

创建者 Thiago H R M

Aug 11, 2016

Good, but gives the very basics.

创建者 Elisa G

Feb 5, 2019































创建者 Carlo D R

Nov 17, 2019

In few pills big contents!

创建者 Zhao y

Jun 25, 2017

Pretty Useful

创建者 Harshit B

May 26, 2017

Nice course!

创建者 Zihui W

Aug 21, 2016

Too easy....

创建者 andy t

Oct 24, 2016


创建者 Joan S

Feb 16, 2020


创建者 Luis E H A

Mar 19, 2017


创建者 Alan I B

Jun 23, 2020


创建者 Valentin Z

Oct 2, 2017

There are good and bad things to say about this part of the "specialization". The good ones (in general thanks to Sergey) are that sometimes during this course you learn something new and useful. But not much... One can hardly say that you will get some sort of "specialization" through these 5 courses. It's highly inconsistent. I'm sorry to say that but it's not the Harvard level of education (at least I hope that the Harvard level is much higher). This part is still far from the wonderful introduction by Professor Waterman. And the 3rd week of this part is a total fiasco: we all know how to read, and mechanical reading from the screen by the teacher doesn't give anything for knowledge. I also didn't understand the part about 'time constrains' as a pretext to describe only normal and uniform distributions (we can read anywhere about these two probability distributions... but where is the value-added product of the teachers?). I would say that selecting the same (very long) range in Excel with a mouse cursor seven times in one lesson is total waste of time that could be spent more effectively. At last, the final quizes are sort of childish: the questions are written in in a very bad style (apparently to complicate understanding) but the real answers are mostly simple arithmetic...

创建者 abaluodufa

Feb 6, 2019

This course deserves a 5 star rating because of Prof. Savin but because of Prof Veeraraghavan, I gave it a 3.

Prof Savin's attention to detail and ability to explain complex concepts in a simple way is a gift indeed. He taught week 1, 2 and 4.

Week 3 with Prof Veeraraghavan however, was a different ballgame. Week 3 was an overload of theoretical statistical probability concepts with little or no explanation whatsoever. No explanation on reading chi square tables, no explanation on the calculation of pdf and cdf etc. I was lost when it was time for week 3 quizzes and I gained zero knowledge from week 3.I recommend that Prof. Savin creates a new video for week 3 as I am sure he will teach the courses therein much much better.

创建者 Mies Z

May 17, 2018

The excels presented in Week 3 and 4 were lacking in substance, they didn't really help provide any useful information in real world scenarios. One of the questions in the quiz actually asked for the mode from one of the videos, first of all the mode? That's quite a basic question but on top that you literally have to go back through the videos to find the correct answer. It was quite silly. The first 2 weeks in the course were good, which is why I'm giving it 3 stars. I expect more from Wharton.

创建者 Deleted A

May 10, 2016

This course is much more informative than the previous two in my opinion. I liked the sample spreadsheets provided. I still however feel the price is a bit steep compared to other similar courses you can find online. The quizzes aren't that challenging and it may be nicer to expand the assignments to practice more.

创建者 Anna D

Feb 27, 2017

Mostly useful practical advice and examples on how to use Excel to model risks and realities. Week 3 was not very good as it was a quick overview of basic statistics which is incomprehensible if you have no previous knowledge and useless if you do.

创建者 Daniel B

Aug 6, 2019

This course had a mix of high and lows, professor Savins pieces felt more connected to real life examples to help illustrate the concepts whereas Professor Veeraraghavan felt more like a math class and very disconnected from real world examples.

创建者 Marek N

Nov 4, 2016

Good practical examples of the concepts. In general, seems to me that the courses of the specializations do not fit together well. At times, basic things mentioned elsewhere are repeated, some others are not explained sufficiently.