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

302 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 .

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51 - 54 of 54 Reviews for Practical Predictive Analytics: Models and Methods

By Jana E

Dec 7, 2017

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

By Marcio G

Jan 6, 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This course is quite outdated.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 Aayush M

Nov 19, 2015

Hands down the worst course on Coursera. I thought it might be beneficial to take this course but it doesn't cover anything in details. Wherever algorithms are explained, a really lousy job is done. To be specific, first and second weeks are covered badly. I am still trying to understand the material by reading external articles on the course topics. I think that first course of this specialization was pretty great and this one is disappointing.

By Marina D

Dec 29, 2015

Terrible lecture videos with many typos, absence of lecture notes, absence of course staff on a discussion forum. Maybe suitable for those who is alredy a scientist and just need to get some general sence of data science.