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Вернуться к Fundamentals of Quantitative Modeling

Отзывы учащихся о курсе Fundamentals of Quantitative Modeling от партнера Пенсильванский университет

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Оценки: 8,047

О курсе

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....

Лучшие рецензии

AP

15 июня 2019 г.

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

30 нояб. 2020 г.

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.

thanks

Фильтр по:

1376–1400 из 1,537 отзывов о курсе Fundamentals of Quantitative Modeling

автор: Charles b

11 июля 2016 г.

GREAT MATERIAL

автор: Nisheeth N

25 дек. 2019 г.

Great Course!

автор: Sajjad H S

24 июня 2020 г.

Good course.

автор: Divyam A

12 апр. 2020 г.

Basic Course

автор: Hongbo Q

12 июля 2018 г.

有一点简单,很基础的课程

автор: Shivani J

4 дек. 2016 г.

great course

автор: nicholas m

11 окт. 2016 г.

Great course

автор: rao s

28 мая 2019 г.

really good

автор: Alex B

25 мая 2016 г.

Good review

автор: Narek

20 мар. 2016 г.

Good course

автор: Daniel P d R E

16 июля 2020 г.

Too simple

автор: Quantum P

3 нояб. 2019 г.

Too simple

автор: Sagar A

26 апр. 2018 г.

too simple

автор: Ishan B

11 сент. 2018 г.

excellent

автор: BAI Y

28 апр. 2020 г.

Not bad

автор: Luis E H A

9 мар. 2017 г.

Great

автор: Sylvia S

18 сент. 2020 г.

good

автор: Shrenik V Z

8 янв. 2018 г.

Good

автор: Nikita R

29 окт. 2021 г.

NA

автор: pravar n

18 июля 2022 г.

.

автор: mahee r

27 авг. 2017 г.

V

автор: Yangzhi G

24 июля 2017 г.

g

автор: John C

30 апр. 2018 г.

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.

автор: Erik B

2 июня 2016 г.

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.

автор: Ken O

20 дек. 2017 г.

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.