Chevron Left
返回到 大规模数据处理:系统与算法

学生对 华盛顿大学 提供的 大规模数据处理:系统与算法 的评价和反馈

759 个评分


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 10, 2016

Great course that strikes a balance between teaching general principles and concepts, and providing hands-on technical skills and practice.

The lessons are well designed and clearly conveyed.


May 27, 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.


1 - 大规模数据处理:系统与算法 的 25 个评论(共 165 个)

创建者 Anne-Marie T

Jan 6, 2020

创建者 Max E

Nov 12, 2018

创建者 Toby E

May 7, 2020

创建者 Anish C

Jan 17, 2018

创建者 Alon M

May 16, 2017

创建者 Jan M

Jun 17, 2019

创建者 Daniel W

Apr 26, 2017

创建者 Christopher A

Sep 29, 2015

创建者 Huynh L D

Jun 30, 2016

创建者 Valery N

Sep 2, 2017

创建者 Sofia C

Nov 14, 2016

创建者 Zahid P

Nov 14, 2015

创建者 Korbinian K

Nov 7, 2016

创建者 Jakub B

Jan 4, 2016

创建者 Francisco J

Mar 6, 2017

创建者 Robert H S J

Feb 15, 2016

创建者 Mangesh J

Sep 27, 2015

创建者 Vijai K S

Jan 19, 2016

创建者 Kairsten F

Sep 22, 2016

创建者 Maria P

Oct 28, 2015

创建者 Qianhong H

Sep 9, 2019

创建者 Kenneth P

Dec 6, 2015

创建者 Paulo S S S

Feb 6, 2016

创建者 Hernan A

Jan 11, 2016

创建者 Dimitrios K

Jan 24, 2016