# 學生對 宾夕法尼亚大学 提供的 Fundamentals of Quantitative Modeling 的評價和反饋

4.6
7,417 個評分
1,462 條評論

## 課程概述

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....

## 熱門審閱

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.

S
2020年11月30日

for the beginer like me i have experience in banking of 8 years still for me this fundamentals are new specially quantitative modelling.Kindly provide banking related examples in here too.\n\nthanks

## 1301 - Fundamentals of Quantitative Modeling 的 1325 個評論（共 1,441 個）

2018年4月26日

too simple

2018年9月11日

excellent

2020年4月28日

2017年3月9日

Great

2020年9月18日

good

2018年1月8日

Good

2017年8月27日

V

2017年7月24日

g

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.

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.

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.

2017年8月21日

The content as dry as it is was well presented. No problems there just be sure to have a good night sleep lest you find nodding in the middle of a module.

The reason for the three starts is a knock on Coursera for not letting me submit my last quiz on the last day of my paid subscription. I finished the content at 10:00 CST but I was not allowed to submit my last quiz. I had to pay for the next course in the certificate to get the completion certificate.

2020年6月20日

The course was a good exposure to the theoretical concepts of Fundamentals of the Quanititative modelling. However, I would have found it more useful if the course had more practical applications that the I could work on myself. to help me understand the applications of the theoretical concepts better. Also, would have preferred the course to take through at least the basic of modelling these important models using some trial data set.

2020年5月26日

Much of this course requires some previous knowledge of calculus and economic thinking. I found that much of this course was a mixture between my business calculus course and intermediate micro economics course that I have taken at college. I think it is unreasonable to call this a beginner level course given that I recognize a lot of the information from intermediate college level coursework.

2016年3月9日

A good introductory course but it would have been nice to see a bit more of the math behind the models. I don't think the assessments were worth upgrading to the paid version of the course though. They were basically memorization and regurgitation directly from the lecture videos with very little, if any, thinking required. A bit disappointing from an institution like Wharton.

2016年4月3日

The lecture is clear, but it's a fundamental lesson which could not learn many things, just theories. Furthermore, I suggest that there should be solution hints for Quiz, because students will be frustrated by not knowing the answers and not knowing how to fix them. In addition, the discussion broad is a little bit cold and lack of mentors to help students.

2020年5月27日

The theory part is good but it is not explained with how the equation or graph is created, only meaning is explained which makes the course less dynamic as at the end of the course one feel that the knowledge is incomplete and have to go back and reunderstand when I will understand the logic and how those equation or graph is developed.

2017年10月27日

The course was helpful to understand key statistical concepts but I hope it would have been worked on a little bit more to make it less boring. I do understand it's rather difficult to make quantitative courses interesting but this is just my feedback to the makers of the course so they can take it into account as and if they desire.

2016年2月12日

This course was a good refresher of mathematical and statistical concepts. I found the mathematical and statistical concepts applied to modelling interesting. I never knew that what I learned in high school in the past can actually be used to come up with models to solve business problems. Some videos were a bit long though.

2020年1月26日

I thought this course was going to talk more about quantitative investment model, there's going to be more finance knowledge, but it's totally mathematical models, and I had actually learnt these mathematical models in other courses. I don't think the name of the lecture and introduction perfactly matches the contentIs.

2017年2月27日

This is a quick run-through different types of distributions. I found it useful as a recap of things I learned in high school (!) and there were some additional insights. However, I think this course might be hard if you never did any more advanced (high school) math.

2019年11月24日

Good Course but the lecturer misses a lot of details and assumes the target students understand most of the basic math such as logarithms. Without further researching those concepts, the equations are a bit difficult to understand and interpret in a practical way.

2016年10月16日

I believe this was a very good course for beginners. However, from Wharton, I would have expected more realistic examples. I would have also liked some hands-on assignments. But, otherwise.. Great, great course from a beginners point-of-view! Thank you so much!

2020年5月4日

The performance of the teacher was excellent, he is very communicative and you could watch him teach for hours, the reading material though should be more helpful, more available sources for further reading and any book chapters or hall books would be ideal

2017年11月12日

It would have been nice to have some of the back work for some of the numbers provided in the RMSE, or R squared numbers. To better understand how those were calculated... instead of just taking someone's word for it.