学生对 宾夕法尼亚大学 提供的 消费者分析 的评价和反馈

1) Raghu Iyengar spoke about Regression and Regression models, tables and calculations. It would have been good if there were few slides around how Regression calculations are done. Just the formulae is mentioned, but calculations of pearson's coefficient, sloe of the line is not explained making it confusing for learners to understand.

2) No mathematical calculation examples explained around RFM and WTP

I really enjoyed lectures by Prof. Iyengar and prof Bradlow. They were very helpful in understanding the basics and the applications. I also like Dr. Peter Fader's approach of teaching a concept though a story. I hope Dr. Berman can innovate and come up with more interesting ways to hold the audience and teach the concepts as currently sitting through his lectures is quite a task.

Classes were interesting, although a bit light. I would also have liked to have more explanations on the quizzes to make sure the material was fully understood. And reading references would have added to the experience. It felt a bit like the class was kept basic in order to make the corresponding "specialization" package more attractive. Slightly disappointing.

Predictive Analysis (week 2) and Application / Case Studies (week 4), were the best part of the course, and I genuinely learned things that I will try to implement in my small family business. However, Weeks 1 and 3 were highly disappointing (to put it mildly). Maybe I was expecting something more concrete or advanced, but the material provided was too basic.

The part on predictive analytics and the case studies are actually very good. The other two ... not so (and I stop here). Overall the course is too easy, unnecessarily spread over 5 weeks: e.g. week one and two are material for 2 days. Whilst successfully demonstrating the benefit of CA, the course never enters the nitty gritty of how to.

The course information is a bit outdated, and many slides are missing from the first couple of weeks. Other than that, it's a good course to get a general idea of what to research on the subject (it doesn't get down to the nitty-gritty details or how to calculate most of it, but the course states it doesn't, so it wasn't a surprise).

Nice intro to the concept of analytics, but compared to the other three courses (People, Accouting, Operations) - this one chould have been more detailed.

The course should ideally be expanded to a more holistic "Marketing Analytics", with concepts on pricing & promotional analytics as well as measuring ROI on marketing investments.

They didn't teach badly per se, but much of the information was WAY too basic compared to what I hoped to learn. The Marketing Analytics course from University of Virginia teaches a set of skills to use for marketing/customer analytics, whereas this course only briefly describes what could be done in marketing/customer analytics.

Great qualitative overview but thats where the good parts end. I wish they actually teach you how to do the math part. It would be a perfect course if we can work on case studies and calculate customer life time value and RFM. This is definitely great course if you have no idea of customer analytics or you are in executive level.

First 2,5 weeks are incredibly boring and really hard to listen to. I almost dropped out because I hated the thought that I needed to listen to these lectures. Thankfully starting week 3,5 the situation improved significantly and the contents became relevant. Really liked the last week lectures and the way the quiz was designed.

The information is good, the professors are good, but the innovation of the methods and the point of view i think that is not on a high and actualizated level and the conclusions are so normals. I hope the next formation blocks will turn on another point of view on me. In all cases, thanks for all, i'm so happy for this.

In general terms I think the course is very useful to learn and understand basic concepts around customer analytics field, with the latest platforms, studies, etc within this field.

However, I was expecting to get a little bi deeper into the concepts, with more complex analysts regardin what firms is doing nowadays.

The most interesting part was the examples at the end. I think the course would have been better if the last week's material was at the beginning. Also, more assignments would be appreciated. I find the information interesting and resources helpful, but I would like practice and some guidance on its application.

The course provided exposure to some of the new topics and techniques in Customer Analytics. Most of the video lectures were exciting but too short for any "major" in-depth learning.

Course can be improved by adding some rigorous assignments and an improved list of additional reading materials.

Kudos to the team!

As a beginner in the area, this churse was a good choice. I think I got a pretty good overview of the issue, unfortunately only on a theoretical level. I would rather more practical examples that students have to analyze themselves, try to decision which models want to use and than discuss their decision.

The course brought to me lot of insights on data, data exploration, predictive analytics and prescriptive analytics. The broad definition of the terms have been understood and few example have helped to know the subject. However, even more in-depth knowledge and study material should have been provided.

Definitely a lot of useful information. I had a little difficulty with some of the tests. I think I would have done better had there been some exercises to help bridge the concepts to application. Also, some points were tested, and it wasn't clear that those points were so necessary to know.

A large gap between the presented oversimplistic modeling approaches and the presented applications. I would expect more "meat" in between. The applications part was nice, the regression and predictive part a little too basic, and the prescriptive part not really worth the time.

The course is informative and the same time quite challenging.

However the main reason behind giving a three stars would be:

1) No quiz at the end of each lesson

2) No hands on experience

3) You need to grasp the concepts at one go or else you will end up failing in the test

The contents are quite theoretical. It could be improved by adding more practical examples.

I don't know if it is just me, but the sound for this course is too soft that I could not hear clearly through built-in speaker. Nevertheless, it works just fine with a headphone.

I did not find the structure of the course useful. Also even though there were a lot of examples (some of which were quite lengthy) I wish they went a bit deeper into the math behind the models.

Quizzes seemed to test your memory more than understanding of the material.

Comenta sobre uso de dados mas não traz exemplos de data-sets, como analisar informações coletadas ou mesmo como coletar os dados. Modelos apresentados são básicos.

Uma ótima introdução para aqueles que não entendem do assunto, porém superficial para cientistas de dados.

The flow of the course is adequate, but some questions of the quizzes were not fully discussed in the lecture slides or material. In some other cases, I would appreciate if you could include the complete procedures of the models mentioned in the lecture slide deck.

Overall is good content. I like Eric most!

The quiz part is not ideal - it does not show the explanation of the questions/answers, which is somehow frustrating to know the reason behind the answers. Additionally, the subtitles of one of the courses are not correct.

The information provide by the faculties is really insightful for a person having no marketting background. One gets to learn lots of new things. However, i feel the slides and probability model in predictive analytics can be explained in a better way.