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Learner Reviews & Feedback for Data Manipulation at Scale: Systems and Algorithms by University of Washington

4.3
stars
746 ratings
161 reviews

About the Course

Data analysis has replaced data acquisition as the bottleneck to evidence-based decision making --- we are drowning in it. Extracting knowledge from large, heterogeneous, and noisy datasets requires not only powerful computing resources, but the programming abstractions to use them effectively. The abstractions that emerged in the last decade blend ideas from parallel databases, distributed systems, and programming languages to create a new class of scalable data analytics platforms that form the foundation for data science at realistic scales. In this course, you will learn the landscape of relevant systems, the principles on which they rely, their tradeoffs, and how to evaluate their utility against your requirements. You will learn how practical systems were derived from the frontier of research in computer science and what systems are coming on the horizon. Cloud computing, SQL and NoSQL databases, MapReduce and the ecosystem it spawned, Spark and its contemporaries, and specialized systems for graphs and arrays will be covered. You will also learn the history and context of data science, the skills, challenges, and methodologies the term implies, and how to structure a data science project. At the end of this course, you will be able to: Learning Goals: 1. Describe common patterns, challenges, and approaches associated with data science projects, and what makes them different from projects in related fields. 2. Identify and use the programming models associated with scalable data manipulation, including relational algebra, mapreduce, and other data flow models. 3. Use database technology adapted for large-scale analytics, including the concepts driving parallel databases, parallel query processing, and in-database analytics 4. Evaluate key-value stores and NoSQL systems, describe their tradeoffs with comparable systems, the details of important examples in the space, and future trends. 5. “Think” in MapReduce to effectively write algorithms for systems including Hadoop and Spark. You will understand their limitations, design details, their relationship to databases, and their associated ecosystem of algorithms, extensions, and languages. write programs in Spark 6. Describe the landscape of specialized Big Data systems for graphs, arrays, and streams...

Top reviews

HA
Jan 10, 2016

Great course that strikes a balance between teaching general principles and concepts, and providing hands-on technical skills and practice.\n\nThe lessons are well designed and clearly conveyed.

WL
May 27, 2016

I like the breadth of coverage of this class. Each of the exercise is a gem in that I get to learn something new also. I would highly recommend this even to experience practitioner also.

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101 - 125 of 157 Reviews for Data Manipulation at Scale: Systems and Algorithms

By Damien L

Nov 16, 2017

Excellent course. I just sad about the absence of any assignment or even quiz in Week 4..

By SIU C M

Sep 29, 2015

It is a comprehensive course for learning quite up-to-date technology and concept.

By Abhijit S

Oct 21, 2015

Good Course for beginner in Data Scientist field. I recommend this course

By Gregory C

Nov 25, 2017

Very good class - the assignments were pretty uninteresting, though.

By Dan C

Jun 9, 2016

I enjoyed this course and found it challenging. Good job!

By Jiancheng

Dec 6, 2015

Great assignment and course design! Not easy for me.

By Jeffrey L

Jan 9, 2016

Very good course! Interesting problem sets.

By Gregory T

Nov 29, 2015

Interesting intro to some powerful ideas

By XIA B

Feb 12, 2017

recommend to improve assignment details

By Krzysztof L

Jul 27, 2016

Good introduction to Big Data systems.

By Sophia J

Oct 27, 2015

it is very useful but easy enough

By Dario P C

Mar 25, 2016

Very usefull course. Great!

By Vijaya S K K

Mar 28, 2017

Excellent course!

By Mariano S B

Nov 19, 2016

Good

By Theo L

Jan 4, 2016

This course has appealing assignments and covers interesting topics. The course, however, has two fatal flaws. First, the lectures are a bit disjointed. While there is much to learn in the lectures, the lecturers style is a bit halting and scattered (it would have been much better presented if the lecturer had a script to read off of.) As is, the lectures are mediocre, which is unfortunate since the lecturer is clearly knowledgeable about the topics presented.

Second, the assignments suffer from a lack of good error messaging and no support in the forums (aside from what you will find from other students, which can be very helpful at times.) The assignments themselves are a great approach to learning concepts (and you get to work with real data, like the Twitter data), but without good error messaging when you submit a script you pretty much end guessing where you are taking a wrong turn.

I had high hopes for this course, but it seems as though it fails on execution.

By Alexander B R

Mar 22, 2017

Overall I enjoyed this course and got a broad overview of the various technologies used in big data analysis. The course is video heavy but short on practice. There are 3 assignments the first 3 weeks, then week4 is an endless series of videos. I really enjoyed the assignments but felt there should have been more assessment/practice provided -there are no quizzes to reinforce understanding. The readings provided are mostly academic ones which aren't that clear to beginners (even to programmers like me).

In contrast, a Python data science course on another MOOC platform has 4 times as much content with practice exercises after every video, mid and final exams, weekly problem sets as well as readings.

Ultimately the course showed me what I need to learn next to get into Data Science but the first course hasn't given me confidence that the rest of the specialization will be worth the money.

By Brian D

Sep 17, 2016

The lectures are all just the right length. As a working professional, it was easy to consume the course in my varying bits of free time. For the most part, the assignments were good. There were a few places where there were mistakes in the instructions or the code downloaded from github had some errors. It was fairly clear that these mistakes have been around for quite sometime, so I wonder why nobody ever bothered to update the code in github or update the bad instructions. This is the internet, not a published textbook. That should be pretty easy to fix. It was also a little disappointing that the 4th lesson was extra long in terms of lectures and had no related assignments. The course was marked as completed when I finished the 3rd assignment so the fourth lesson was effectively optional.

By Dongying Z

Feb 9, 2019

Pros: The content of the course is great. It introduces fundamentals of big data technologies to those who are new to this field, with some hands-on practices.

Cons: The instructions of assignments are not always clear - they are corrected in the discussion forum but why not updating in the assignment page? Usage of Python 2.7 is also somewhat out of date since it's 2019.

Biggest con: The way the lecturer talks is more than annoying. Full of stop words like 'fine', 'ok', with occasionally correcting mistakes on slides or diverging to other topics - there are only a few minutes each video and how much time did the lecturer wasted on talking nonsense? It's fine if he talks like that on some 90-min-long classes but it's on Coursera. Sometimes I just skimmed the slides rather than listen to him.

By Bernhard S

Oct 29, 2015

Lots of good material, though I don't like how they've repackaged the original material from the prior longer version, which I worked through at my own pace a year ago, off session. Cramming all the material relating to NoSQL and Graph Analytics into the final week without assignment is ineffective. Instead, consider focusing the 4th week on NoSQL, and keep an assignment with it, maybe even the original Pig assignment that required and AWS account. I don't think the nominal charge Amazon will levy would hold anybody up who's serious about learning to process data at scale, it's just a few bucks.

By Stefan K

Dec 28, 2015

Somehow interesting course about Data analysis. The lectures are interesting for those who have no prior knowledge about the topics, but boring to those who have it. The assignments are quite challenging and the disadvantage is, that they are not connected to the lectures and are therefore not well explained. What I like about the assignments is, that it is practical.

By Eric B C C

May 28, 2016

Found the assignments were 'very loosely' aligned with the lecture material and had poorly formed problems in places.

Lectures were reasonably good but not quite up to the standard set with other U of W Data Science courses or other University Data Science / Machine Learning courses I have taken.

By Arto P

Dec 7, 2015

The emphasis on methods rather than specific tools makes the course more resistant to the continuous changes in technology. The stage is set well, and there are practical implementations. Still, it's disappointing to see that errors from previous rounds have not been corrected.

By Hannah M

Nov 18, 2015

It was really frustrating that the autograder and assignment instructions didn't match. This course has been around too long for that big of a mistake. The lectures were the redeeming factor. They were interesting and presented the subject matter in a concise way.

By 罗杰彬

Oct 29, 2015

the material is too simple. This course is just like a brief introduction, but not a course in college to teach student the real knowledge. I think MOOC course should be the same as a real college course. With the same difficulty and amount of material.

By Martin M

Jan 4, 2017

Good content for Data Scientists but video lessons are not sufficient to be able to complete the assignments. It required great deal of own searching and trials and errors to complete the course.