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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

745 個評分
161 條評論


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...



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.


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.


51 - 大规模数据处理:系统与算法 的 75 個評論(共 157 個)



very interesting materials about RDBMS and nosql systems

創建者 Guruswamy S


Very wide and fundamentally robust introduction.

創建者 Nayan J


Coding assignments help shed the resistance :)

創建者 Shivanand R K


Excellent thoughts and concepts presented.

創建者 Artur S


Brilliant course with amazing test tasks!

創建者 Kevin R


Great exercises one can learn alot from.

創建者 Cesar O


Nice explanation of mapReduce, love it

創建者 Matthew M


excellent treatment of the material

創建者 Felipe G


great course! ... congratulations.

創建者 Roland P


Great intro into wider aspects

創建者 Dan S R


Great work, very satisfied!!

創建者 Miao J


Great course. Very helpful!

創建者 Shibaji M


This is a great course

創建者 Minh T


Great for students.

創建者 Menghe L


great for learner

創建者 Shambhu R


Very nice course!

創建者 Desiree D


Hard but awesome

創建者 Vaibhav G


Awesome content.

創建者 Sebastian O M


100% Recomendado

創建者 devang


Amazing Course!

創建者 Jeffery L T


Great course!

創建者 francisco y


Great course!

創建者 Muhammad Z H


learnt a lot

創建者 Ivan S


Nice !

創建者 Jan Z


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).