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學生對 华盛顿大学 提供的 大规模数据处理:系统与算法 的評價和反饋

4.3
677 個評分
143 個審閱

課程概述

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...

熱門審閱

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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1 - 大规模数据处理:系统与算法 的 25 個評論(共 140 個)

創建者 Max E

Nov 12, 2018

Assignments need to be updated, but the material is solid!

創建者 Jan M

Jun 17, 2019

The course material is ok, but the support and assignment grading is horrible - I spend several hours just battling with grader after having the results ready. Definitely wouldn't recommend this course to anyone. I subscribed for the whole Specialization and completed Course 1 and 2. Unfortunately Course 2 finishes with Peer Graded Assignment - I submitted it with a few weeks to go before my subscription expires but there was no one to grade it so once my subscription ended I didn't get the certificate despite completing the whole second course as well and I lost access to all my submissions and the Course material even though I have already paid for it.

創建者 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.

創建者 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.

創建者 Guruswamy S

May 29, 2018

Very wide and fundamentally robust introduction.

創建者 Batt J

Apr 14, 2018

Very good course for understanding the underlying logic behind emerging big data technologies

創建者 Dwayne B

Apr 13, 2018

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

創建者 Achal K

Feb 05, 2018

A very good introduction to skills needed for applying data science ideas on large scale data problems.

創建者 Anish C

Jan 17, 2018

Thanks for this course.True Parallel computing example would have made it even more awesome .

創建者 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.

創建者 Jana E

Dec 07, 2017

Quite interesting subjects, but video material is not of high quality and many mistakes are not changed in later sessions but altered via a text in the screen of a note on the next sheet.

創建者 Gregory C

Nov 25, 2017

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

創建者 Damien L

Nov 16, 2017

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

創建者 Lloney M

Nov 03, 2017

The course info makes no mention of Python as a prerequisite. Yet the first assignment demands Python knowledge and skills. Without which you can't pass the assignment. Yet the week's lecture is not about Python.

創建者 FilippoV

Sep 19, 2017

very poor!

創建者 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.

創建者 kazım s

Sep 10, 2017

If you want to head into Data Science, this is a nice course that will help you.

創建者 valery n

Sep 02, 2017

Excelente curso, contenidos muy completos; sin embargo, deberían actualizar las instrucciones de cada Assignment con las correcciones ya descritas en los foros, para algunos es díficil encontrar estas correcciones fuera del enunciado. Por lo demás, gracias por esta oportunidad, por abrir las puertas de una universidad tan importante a otros estudiantes que jamás podrían asistir a su campus.

創建者 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.

創建者 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!

創建者 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

創建者 Roland P

Jul 27, 2017

Great intro into wider aspects

創建者 Chuck C

Jun 26, 2017

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

創建者 Timothy R

Jun 22, 2017

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

創建者 Leonid G

Jun 20, 2017

Comprehensive and clear explanation of theory and interlinks of the up-to-date tools, languages, tendencies. Kudos and thanks to Bill Howe.

Highly recommended.