It could've gone deeper into the topic. Although it's called Fundamentals of quantitative modeling it don't got to be to basic.

This Course is very good for beginners who have absolutely no knowledge of Modelling. For Professionals, it is not of much use.

I was expecting the course to provide insight on how to use spread sheet and its respective formulae in mathematical modelling.

Well put together, however, if you have had a reasonable high school math education, most of the content will be known to you.

It would have been much more helpful to have practice opportunities besides the exam and exampled. I wanted to try it myself!

The instructor is good. Course material is very basic. However, it is not for someone having no knowledge of statistics.

As a guy from Machine Learning field, its models and concepts are so easy and simple for me, so it's lack of challenge.

More concrete exercises would have helped to anchor some principles. This was mostly a succession of videos and a quiz

This subject is totally numerical based, There should be more focus on numerical instead of theoretical knowledge.

I felt that there were insufficient examples to help us gain a better understanding of the concepts being taught

Many of the fundamentals are not explained. For example in Week 4 it is not explained how to calculate R2 or r

Assignments should be evaluated for all users (unpaid ones as well) like it is done for other Wharton courses.

Perhaps I was expecting too much given the reputation of the institution, but this is a course for beginners.

Could come a bit deeper in more complex examples at the end of the course. Most of them were too simple.

I think this course is too easy for students with Probability and Mathematical Statistics knowledges...

General description of the modeling, need further work or pre-understandings for some of the contents.

Felt a little rudimentary until the last week. I hope the specialization picks up after this point.

Not enough about formulas or real world application. Was hoping to see examples applied in Excel.

Nice primer for modeling, but wish there were more workable problems to help hit the point home.

Too descriptive for a quantitative course. Would've preferred more problem solving exercises.

-no explanation on errors

-too theoretical and abstract with lack of examples and own practice

Could have been more advanced from the perspective of practical use-cases of data modeling.

The course is a bit too introductory for me. I'm looking for more challenging material.

most contents are explicit on ppt, it is sometimes not necessary to view the lectures.

Ejercicios que requieran de mayor complejidad práctica pueden resultar beneficiosos