Chevron Left
Back to Practical Predictive Analytics: Models and Methods

Learner Reviews & Feedback for Practical Predictive Analytics: Models and Methods by University of Washington

303 ratings
56 reviews

About the Course

Statistical experiment design and analytics are at the heart of data science. In this course you will design statistical experiments and analyze the results using modern methods. You will also explore the common pitfalls in interpreting statistical arguments, especially those associated with big data. Collectively, this course will help you internalize a core set of practical and effective machine learning methods and concepts, and apply them to solve some real world problems. Learning Goals: After completing this course, you will be able to: 1. Design effective experiments and analyze the results 2. Use resampling methods to make clear and bulletproof statistical arguments without invoking esoteric notation 3. Explain and apply a core set of classification methods of increasing complexity (rules, trees, random forests), and associated optimization methods (gradient descent and variants) 4. Explain and apply a set of unsupervised learning concepts and methods 5. Describe the common idioms of large-scale graph analytics, including structural query, traversals and recursive queries, PageRank, and community detection...

Top reviews

Dec 22, 2016

Fantastic course! Excellent conceptual teaching for people who already know the subject but need some more clarity on how to approach statistical tests and machine learning.

Feb 7, 2016

I enjoy this course. The delivery and the course topics were very interesting. I learnt a lot and peer reviewing other people assignments is a great learning opportunity .

Filter by:

26 - 50 of 54 Reviews for Practical Predictive Analytics: Models and Methods

By Harini D

Aug 31, 2016

The entire course is an overview! This course will be a revision if you already know the concepts.

By Roberto S

Jun 13, 2017

Very good approach to each method; the assignments are a good test for the topics.

By Nico G

Dec 22, 2015

Very interesting course. It would be useful to download slide used during videos.

By Antonio P L

Jan 8, 2016

Great Course but the assigment don't show the understanding of the course

By Zoltan P

Dec 23, 2015

More dynamic visualisation please, and it will be 5*.

By Ashish S

Jun 29, 2019

Was expecting more to learn on stats and R.

By Alon M

Jan 15, 2018

rather nice course. learn R before joining

By Jiancheng

Feb 25, 2016

good course material!

By Andrew T

Jan 12, 2016

The lectures in this course were very good but I would have preferred much, much more homework to practice the concepts covered in the lectures.

Also, I was somewhat disappointed when a certain issue with the course that I asked about in the forums was never addressed by the course staff. Of course, I could have been wrong about it but, but based on the response from other students I was not the only one having this problem.

By Lucas S

Mar 15, 2017

Great overview of many models and techniques, but very high level. Would have greatly benefited from links to resources to learn more about all the subjects. This course leaves students with only basic knowledge of the subject matter, which is fine considering the course timeline. But, for those who want to explore further please recommend sources of additional reading and research.

By Robert H S J

Feb 15, 2016

This course was in some ways a disappointment. Although the lectures were intriguing and clear, I felt like the assignments were essentially "Go and pick up R on your own," which was pretty frustrating.

By Faisal G

Nov 20, 2016

I felt that topics were not treated in enough depth. It was a lot of topics to cover in a 4-week course.

I learned a lot from the kaggle competition.

By Guido T

Jan 21, 2016

Interesting course and specialization. A few inaccuracies need to be corrected so it can be properly pursued at its best.

By Solvita B

Mar 17, 2016

Problems with vitual machine for R assigment. For peer review detail evaluation guidelines is need.

By Sambed A

Dec 24, 2015

It's a decent course. Not as thorough as Analytics Edge or Machine Learning (by Andrew Ng).

By Benjamin F

Feb 4, 2018

Meh, if you want to really dive in predictive analytics go to other courses.

By Harald B

Mar 17, 2016

the "practical" part is not really existent

By Praketa S

Nov 7, 2016

it gets on my nerve from 3rd Work onwards

By Sasa L

Jul 17, 2016

Content is too easy

By Jonas C

Apr 18, 2017

The lessons are sometimes completely disconected from the graded assignments. There were some graded assignements that dealt with things I have never heard about and I completed it without even looking the lessons videos. Some of the lessons are disapointing of the lack of assistance to the required software/code to be used. In such a way that the concept worked is very simple, but if you have no experience on the software or code you can have a hard time to complete the assignements with irritating details which are not explained at all in the lessons. The lessons serves more as a guide to what you should search in google and learn through other source of information. I did not expected such poor course from a paid one; I have doen free courses way better than this course. Don´t pay or this course, find some other course free or other paid course with better reviews.

By Andre J

Jun 21, 2016

I'll say the same about this class as the rest of the specialization, if you have the skills to complete this course then you don't need to take this course. If you don't have the skills to complete this course, you will not complete this course. The course instruction is at 10000 feet level and the assignments are very challenging and the course will NOT teach you the skills required to complete the assignments.

I recommend the Machine Learning Course (from Bill's colleagues) at University of Washington. That is a course where you get some real instruction and understanding of how to complete assignments (though still very challenging).

By Qianfan W

May 9, 2016

Do not like the slides and the way it is explained. Compared with other ML courses on cousera, this one makes me feel that it is more like a handbook/dictionary instead of a tutorial to teach students. If you already know it, it would help you refresh the mind. Otherwise, you might find it is just to show off how how complex and mysterious is the data science.

By Ben K

May 27, 2016

This course probably deserves 3-4 stars in a better, maintained form, but the entire specialization is not maintained, the lectures have no production values. Basically, it's a money pit that Coursera is keeping up cynically. It's a real shame because the syllabus correctly addresses a gap in most data scientists' skills.

By Sajit K

Feb 13, 2016

Unrelated and incohesive lectures. Disappointed. Lots of random topics talked about .but nothing in depth.

By Lei Z

Mar 22, 2017

The course is good. But it does not has lecture slides that is better for students to understand.