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Kursteilnehmer-Bewertung und -Feedback für Data Manipulation at Scale: Systems and Algorithms von University of Washington

736 Bewertungen
160 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...



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.


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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76 - 100 von 156 Bewertungen für Data Manipulation at Scale: Systems and Algorithms

von Dany M

Aug 21, 2017

There are times where a user without a very fast connection will struggle to set up, the virtual machine is impossible to get for them. Between the internet and the forum the needed information is there but it makes the first assignment take 15+ hours. A little help on the assignment page on how to get going on Windows would save a lot of people some time.

Apart from this ist is quite good. The automatic grading is amazing and the videos quite nice.

von Hao-en S

Nov 14, 2015

Excellent course content and lecture, however, the homework design is not so friendly for students.

First, instructions are not clear. I spent a lot of time in figuring out the real meaning behind each problem. Second, the judge system is not so rigorous. Though trying best on homework and not playing tricks are students' responsibility, the judge system is just too weak to survive those hackings.

Thank you all for your efforts.

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 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 Kay S

Feb 06, 2016

There are some inconsistencies in the course or the arrangement of the videos, maybe due to technical problems.

For improvement I would really wish to have some substantial results, especially in the last week of the course. That is, I would prefer to discuss at least something in depth rather than everything broadly.

von Christine

Jul 09, 2016

This is a great introduction. I would give it a higher scoring but was frustrated with one of the labs have issues in the grader and even after completion of the course its not clear what was wrong: the instructions, the solution data set, the input data set. Other then that enjoyed the content.

von Jim S

Aug 10, 2017

The theory and relational algebra is a little heavy for me (I am very much a practitioner). That said, Prof Howe is *excellent* in is presentation. Very clear and easy to follow. Sometimes beats a dead horse (Map Reduce) and as a result, you definitely know what he's getting after!

von Andrew T

Dec 02, 2015

The lecturer is very, very knowledgable and seems to explain the landscape of topics both from a grand perspective and deep knowledge.

Though there are a wide variety of programming exercise,I would prefer some more in-depth assignments (as is usually the case with me and Coursera).

von Annavajjala S P A S

Mar 13, 2017

The contents of the course were good enough. The assignments, though simple required some work in terms of understanding the kind of data that you are dealing with, which is important. Although, a lot of content has been covered, it was arranged in a logical manner.

von Sreeparna M

Sep 18, 2017

The course is good. It definitely gives a broad overview of the topics. It's presented in an interesting manner and I would definitely go in-depth about these topics. Although, it would have been more helpful had there been more graded quizzes and assignments.

von Maxime B

Mar 01, 2016

The power-point used has a lot of mistakes corrected live by the speaker.

The speaker speech is sometime slow and not precise, it probably has been recorded only once.

Apart from that the content covers the subject and the assignments are relevant and fun.

von Joris D

May 21, 2017

The course gives a good introduction into handling large amounts of data, the problems it poses, and an overview of the available solutions. Towards the end of the course, it started to feel a bit less polished and more rushed, though

von Fermin Q

Nov 03, 2016

It gives good information, but frankly covers way too many tools at the end, and the explanations are good but somewhat rushed. Some parts were a little boring, as no immediate practical use seemed on the horizon.


Jan 05, 2016

Very broad and instructive course with a good level of theory, many practical examples. Good teaching.

Some nice assignments but a lake of assignement for the 4th week

I recommand this course

von Anne-Marie D

Jul 20, 2020

Well structured and nice overview of data manipulation. But the assignments should really be updated in order to use python 3.x instead of 2.7, which is not maintained anymore...

von Yu-Heng H

Nov 25, 2018

It's pretty tough in assignments especially when there are mistakes in the given description, but I do learn the basic concepts of relational algorithm and MapReduce from them.

von Wesley E

Oct 04, 2016

Definitely need some background in R or Python and the lectures are a bit old. Seem to be from around 2013 when this first came out but most of the info is still relevant.

von Sajit K

Jan 12, 2016

Its pretty decent. I liked the assignments. However there were some typos in the lecture slides and also the grader output is not very friendly.

von Alari

Dec 03, 2015

Very good course, but lectures could be more tuned onto the home assignments. A lot of independent work for me at least. Teacher is very good.

von Mandar B

Mar 29, 2017

Course gives you good overview on different important data science technologies. Hands on labs are important to get the grip on concepts.

von Dmitry G

Jan 02, 2016

Last week of the course is too much information and without any assignments it kind of doesn't make much sense and it doesn't stick.

von Tony G

May 13, 2016

covers a lot of ground quickly, but you still get a good understanding of the underlying theory or technologies

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 Chuck C

Jun 26, 2017

Great content. The questions are academic and sometimes hard to understand the desired outcome

von Damien L

Nov 16, 2017

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