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

287 ratings
53 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 23, 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 08, 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 .

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1 - 25 of 51 Reviews for Practical Predictive Analytics: Models and Methods

By Yogesh B N

Feb 20, 2019

Nice course

By Anand P

Feb 11, 2019









By Yifei G

Jan 03, 2019

I can feel Prof. Howe tried to cover as much as possible and to build a foundation for both practicing as well as further study on the topics. However, I do feel it is not patient enough to give a detailed yet easy-to-follow explanation for some of the topics, and I had to do quite some self-readings to close the gap. I think it will be helpful if the course can provide some reading materials on how some of the formulas are derived (e.g. gradient descent, logistic regression etc.) as a supplement.

By Benjamin F

Feb 04, 2018

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

By Alon M

Jan 15, 2018

rather nice course. learn R before joining

By Jana E

Dec 07, 2017

Same as before, subjects are quite interesting, but the video material is of quite low quality.

By Sergio G

Oct 30, 2017


By Roberto S

Jun 13, 2017

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

By Menghe L

Jun 12, 2017

great for learner

By Nathaniel E

Jun 08, 2017

I think the amount of course work to lectures was more appropriate than the first segment. I enjoyed the exercises and felt that they mixed the correct amount of theory and applicaiton.

By William L K

Jun 06, 2017

Excellent Lectures. Since the course is several years old the organization of some of the assignments needs updating. That's the only reason I gave it 4 instead of 5 stars.

By Jonas C

Apr 19, 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 Lei Z

Mar 22, 2017

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

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 Marcio G

Jan 07, 2017

This course is quite outdated. I didn't learn much beyond what I already knew before I started. The Spark courses from edX are way better than these. Hopefully "Big Data Analysis with Scala and Spark" from the "École Polytechnique Fédérale de Lausanne" (also from Coursera) is good (I know their Scala courses, which are taught by Martin Odersky, are quite good).

There are very few quizzes between lectures and the assignments are not very challenging.

Many of the videos, specially the ones at the end were extremely rushed over. They serve more as a review if you know the subject, otherwise I don't think most people will get much from them.

The audio isn't very good for most of the lectures, many having an very annoying chirping sound (from when you leave an old flip phone near a computer... "teh-teh-teh teh-teh-teh teh-teh-teh teh-tehhhhhh....". Gosh, I haven't heard this sound in maybe over five years...).

The Kaggle competition at the end of the course can be fun if you do the hard work, but you don't need to put much of an effort to pass. I know that the submissions I peer reviewed were quite poor, but the grading criteria that we need to follow as reviewers is quite vague and not very thorough. You also run the risk of getting a lesser grade than you deserve because your reviewer is incompetent, which is a bummer... At the moment the course has very few people taking it (the same people I peer reviewed, also reviewed me, which leads to me to believe that maybe only 3 or 4 people were taking this course during the November 2016 iteration).

By Seema P

Dec 23, 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.

By Faisal G

Nov 21, 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 Praketa S

Nov 07, 2016

it gets on my nerve from 3rd Work onwards

By Kairsten F

Oct 26, 2016

This course covers a lot of material, but unfortunately lacks depth and thorough examples in many areas. It could also use more hands-on activities. Overall, I learned quite a bit and found it was worth the time and effort.

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 Chen

Jul 20, 2016

Nive that the course covered a broad range of topics.

And good to get pushed to do some kaggle competition and peer review.

By Sasa L

Jul 17, 2016

Content is too easy

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 Shivanand R K

Jun 18, 2016

Excellent thoughts and concepts presented.


Jun 06, 2016

A quick overview of technology terms used for Machine Learning, and gentle introduction into learning through Kaggle.