I really enjoyed this course, but I did struggle more than I should have with the software tools. In many cases, my version of the tool (the latest) did not match the instructor's version, and I worked to translate my version to his, and that's not a good use of my time. That said, the material was interesting, and the professor did a great job presenting it. I would recommend the course, but I would recommend that you learn to get the results not just in XL Miner, but in R or some other software, as well

Very interesting course that covers a lot, which is good in that it gives exposure to different mining techniques, but bad in that I feel very far from mastering the techniques. Each mining technique could be its own course. Course could do a better job of explaining how to interpret the model outputs.

Very good course for understanding Regression, classification. Other advance predictive models like trees, random forest, neural networks are covered fast. Could have been little more lengthy sessions.

instructor is very fast in explaining concepts.

it is really a good course which helps me to understand the basic knowledge of data mining in which I learned about logistic and linear regression and also about boosting, bagging, and random forest.

Professor is a little tough to understand, so I had to read the transcript during some of the videos. However, once I got the XLMiner issues resolved, it continued to be a great class and experience.

Really like the course and learned a lot. Wish that the quizzes didn't offer as much guidance on the steps to use XL Miner. Because this is given, it's not fully testing students on the material

The tutor organised the course with clear points and highlights questions during videos. The only confusion is about web version xlminer tool.

A good introduction to various techniques of predictive modeling. To better understand, further study on the topics is necessary.

Good course, one recommendation is to use, at least, one extra tool apart from xlminer.

The Case Assignments are pretty useful

very well explained

Tricky to Pass

The course although presents great topics, it needs improvement to support students with questions about exams and assignments. I'm not gonna sugar code this review, it took me hours and hours of dedication to pass the quizzes and assignments, and did not get much help from the professors, one of the instructors has poor English skills and is very hard to understand his lectures. However, this course presents you a challenge to better yourself, believe me, if you spend the time, read the questions that other students post in the discussion forum, and follow the guidelines from the videos, you'll pass. I struggled at the end but ended up getting 97% in my final grade. One advice, keep track of the correct and wrong answers you get from the quizzes, many questions repeat themselves, take notes from the videos or ppt, is not enough just watch, be an active learner and you'll be fine.

I really want to mention two thing about this course. The first thing is the content (Algorithms) which are talked in this course are Really usefull and important mostly used algorithms to learn clearly.and another thing is the instruction was so bad in explaining the subject .He is so fast in explaing concepts and math beahind it .Even not only he is fast his native english pronosation is so bad and its really tough to understand.

I found the course content to be good and explanations to be concise but sometimes rushed through. The choice of software to use, however well aimed as it did not require manual programming, it proved a more frustrating experience than actually writing it in Python. I also hardly believe that I would be using Analytic Solver in the future.

A good course overall; however, I expected some further reading recommendations by the lecturer to help develop a deeper understanding of predictive models.

The last peer assignment was boring and too simple relative to the course knowledge level. Plus, one of the tasks was replicated from another one that I previously did.

Covers a lot of essential topics around analytics and predictive modeling. However, the delivery in the lectures lacks enthusiasm and seems very scripted. Also, the final project, despite being primarily a predictive analytics courses is simple a re-hash of the Week 1 project, and is not cumulative of all topics covered.

Many of the concepts felt "glossed over" as the subject matter was rich but it was hard to get a complete understanding of it in the videos. I found myself having to do a lot of additional supplemental research not even mentioned in the videos to accurately answer some of the quiz questions.

The Course is good, it covers lot of topics but there are certain drawbacks like the accent is not clear, have to read the transcript and other issue is the contents are very briefly explained, need to read it separately on google, if you are not from science background.

There waw a lot of information but it wasn't clear or how was related with the previous course. Moreover, most of the quizzes depend of XLMiner and difficulties were encountered by some students with the application in the course.

This is a very difficult context to learn how to use a computer program--it's outdated, since XLminer is now called Analytics Solver, and it's very hard if you are having an issue with the program and can't get any help.

Although the teaching method is wonderful. A little bit more content will go a long way. Little more clarity in video lectures is required.

EL profesor no es muy didáctico y su dicción no es fácil de seguir.

Preferentemente mayor uso de las herramientas de predicicon de excel

Using XLminer in the course created a problem for a while. Otherwise, the course is good.

XL Miner too cumbersome to handle