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

4.3
686 Bewertungen
148 Bewertungen

Über den Kurs

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-Bewertungen

HA

Jan 11, 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.

SL

May 28, 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 144 Reviews for Data Manipulation at Scale: Systems and Algorithms

von Kenneth H M N

May 15, 2017

Overall a good course, with teachings bit into very manageable lengths of time. My biggest grievance is that your submission has to be in encoded in a particular format (utf-8) if memory serves. So you may have to resave your .txt files if you try to do all the programming on a windows laptop. This may be obvious to some, but it took me a little to figure out.

von Timothy R

Jun 22, 2017

Very good introduction to relational algebra and map reduce. Also helped scratch up on some python and SQL.

von Aayush M

Oct 28, 2015

I feel that there should be more assignments to make the course interesting. The last part just briefly explained about different database types but it also focused two lectures on Pig. There could be an assignment to make the lectures more meaningful or perhaps, a quiz. Otherwise, last week is too much information to grasp at once.

von Jan Z

Nov 21, 2016

The course was very good - especially the map-reduce part I found very well explained and inspirational. The problem sets were thought-provoking and really taught me a lot.

Two things that could be improved:

1) The problem sets are really nice (again, map-reduce is the best one), but there are quite a few errors in the description, a lot of information is dated (e.g. in ps.1 the twitter link is old), and working with the grader can be very clumsy. See Machine Learning by Andrew Ng to see how to design perfect, easy to operate and submit problem sets. Perhaps work with PyCharm creators?

2) The second to last part was a bit lacking - it was basically skimming though all different types of databases, which didn't make me feel like I really acquired any skill. Because of how little time was spent on each database type and there were so many, I don't really remember much of it now (hardly anything to be honest).

von Dongying Z

Feb 09, 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.

von 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.

von 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.

von Dwayne B

Apr 13, 2018

Good information but lectures were poorly produced and unedited and exercise instructions were blatantly incorrect several times.

von 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.

von 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.

von Arto P

Dec 07, 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.

von Theo L

Jan 04, 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.

von Daniel V

May 30, 2017

If you don't know Pythonl, don't take this course.

von Fisher

Aug 01, 2017

little touch of everything, it's good intro for non-tech, but way too shallow for a student from tech background

von 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.

von 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.

von Ingo B

Oct 10, 2015

This is a cooked up version from an earlier, more extensive course. Lecture videos now split from 10-14 minutes into lots of 4-6 minute videos. It seems, some assignments are missing, too.

von Hannah M

Nov 19, 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.

von Ryan S

Mar 28, 2016

Long, slow, rambling video. I watched most of it at 1.75x. Slides are kind of a mess and lectures are disorganized.

von Griffin S

Oct 05, 2015

Program instructions could be more specific. Make it clear exactly what format the programs output should be.

von James S

Jan 07, 2018

The material is good. If you can get past the instructor's mumbling and rapid speaking then you'll be okay.

von Martin M

Jan 05, 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.

von Tushar T

Jan 08, 2016

Assignments were just not that challenging except first one

von 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).

von Diego P

Feb 28, 2017

Many mistakes in the slides and poorly defined problems in the assignments have gone uncorrected for over a year. The content is very basic, as would be for an introductory course, but can even serve as a refresher for CS graduates.