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Fundamentals of Quantitative Modeling に戻る

ペンシルベニア大学(University of Pennsylvania) による Fundamentals of Quantitative Modeling の受講者のレビューおよびフィードバック

4.6
8,053件の評価

コースについて

How can you put data to work for you? Specifically, how can numbers in a spreadsheet tell us about present and past business activities, and how can we use them to forecast the future? The answer is in building quantitative models, and this course is designed to help you understand the fundamentals of this critical, foundational, business skill. Through a series of short lectures, demonstrations, and assignments, you’ll learn the key ideas and process of quantitative modeling so that you can begin to create your own models for your own business or enterprise. By the end of this course, you will have seen a variety of practical commonly used quantitative models as well as the building blocks that will allow you to start structuring your own models. These building blocks will be put to use in the other courses in this Specialization....

人気のレビュー

AP

2019年6月15日

Very clear and articulate explanation of the concepts. He doesn't skip a step in the sequencing ideas, drawing comparisons and differences, and illustrating both visually and story-telling. Excellent.

NC

2019年7月30日

Very nice course for beginner, the mathematic level is not high (around french baccalaureat) so available to everyone. I enjoyed a lot this course that show how simple math can be used in real life.

フィルター:

Fundamentals of Quantitative Modeling: 1376 - 1400 / 1,538 レビュー

by Charles b

2016年7月11日

GREAT MATERIAL

by Nisheeth N

2019年12月25日

Great Course!

by Sajjad H S

2020年6月24日

Good course.

by Divyam A

2020年4月12日

Basic Course

by Hongbo Q

2018年7月12日

有一点简单,很基础的课程

by Shivani J

2016年12月4日

great course

by nicholas m

2016年10月11日

Great course

by rao s

2019年5月28日

really good

by Alex B

2016年5月25日

Good review

by Narek

2016年3月20日

Good course

by Daniel P d R E

2020年7月16日

Too simple

by Quantum P

2019年11月3日

Too simple

by Sagar A

2018年4月26日

too simple

by Ishan B

2018年9月11日

excellent

by BAI Y

2020年4月28日

Not bad

by Luis E H A

2017年3月9日

Great

by Sylvia S

2020年9月18日

good

by Shrenik V Z

2018年1月8日

Good

by Nikita R

2021年10月29日

NA

by pravar n

2022年7月18日

.

by mahee r

2017年8月27日

V

by Yangzhi G

2017年7月24日

g

by John C

2018年4月30日

I liked Professor Waterman; he is clear, gives examples, and doesn't just drone over the slides like my statistics professor did in college. However, the course itself felt a little too simplified. For example, when I arrived at the topic of multiple regression, concepts like collinearity and omitted variable bias, which are crucial to understand the fitness of your model, were not mentioned. This was a bit concerning because most business operations, I would assume, have multiple variables in play and would seem more practical to have a more in-depth focus on models reflecting that characteristic.

by Erik B

2016年6月2日

The materials in this course were great, but some of the math was not properly explained enough for the individuals to be able to see how the formulas were derived - especially some of the basic calculus and the regression materials. I believe it would have only added 5-10 more minutes in one or two modules to do so since there were so few examples given (This could be covered in subsequent courses within the specialization - I am not sure yet as I will be taking course #2 in the specialization starting next week). Otherwise, this course was a great overview of the types of models used.

by Ken O

2017年12月20日

Content

This is essentially a statistics course couched in business terms, with a smattering of finance. The term quantitative modelling' is just how 'stats' has been 'rebranded' in the modern era. That is not a criticism from my point of view, but worth mentioning.

Difficulty level

Ultra-challenging for non-mathematician 'analysts'. The material is also structured sub-optimally. More cohesion would aid understanding. But the course is often rivetting and informative in ways that other groundings in stats fail to be, in my experience.

Conclusion

Difficult, but well worth the effort.