Chevron Left
返回到 大规模数据处理:系统与算法

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
692 個評分
151 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.

篩選依據:

76 - 大规模数据处理:系统与算法 的 100 個評論(共 147 個)

創建者 Jeffrey L

Jan 09, 2016

Very good course! Interesting problem sets.

創建者 Dario P C

Mar 25, 2016

Very usefull course. Great!

創建者 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).

創建者 Jiancheng

Dec 06, 2015

Great assignment and course design! Not easy for me.

創建者 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.

創建者 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).

創建者 Tony G

May 13, 2016

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

創建者 Krzysztof L

Jul 27, 2016

Good introduction to Big Data systems.

創建者 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

創建者 Xuefei J

Oct 27, 2015

it is very useful but easy enough

創建者 Abhijit S

Oct 21, 2015

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

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 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.

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 Chuck C

Jun 26, 2017

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

創建者 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.

創建者 Mariano S B

Nov 19, 2016

Good

創建者 Gregory T

Nov 29, 2015

Interesting intro to some powerful ideas

創建者 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.

創建者 Dylan T

May 06, 2017

The course is interesting and well made. Compared the the other two, I found the first assignment quite difficult and required quite a bit of time to complete. Introducing SQL through relational algebra seemed relevant to me, and made the formulation of SQL queries very natural. The section about map reduce may appear difficult to process first but as the student has to go through (and beyond in one case) the examples presented in the course. In the end, I found the assignment very useful in putting thing in place. I received full grade but still have to go through week 4, maybe a small quiz in the end to test our understanding of the different concept would have been handy.

創建者 MICHEL S

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

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 xia b

Feb 12, 2017

recommend to improve assignment details

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 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.