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
757 個評分

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

2016年1月10日

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.

WL

2016年5月27日

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:

26 - 大规模数据处理:系统与算法 的 50 個評論(共 165 個)

創建者 Killdary A d S

2019年7月4日

創建者 Leonid G

2017年6月20日

創建者 Mahmoud M

2016年1月18日

創建者 Jun Q

2016年8月8日

創建者 Karol O

2019年12月22日

創建者 Roberto S

2017年6月13日

創建者 Daniella B

2016年4月21日

創建者 Itai S

2015年11月14日

創建者 Achal K

2018年2月5日

創建者 Raheel H

2019年7月1日

創建者 Bingcheng L

2019年8月4日

創建者 Padam J T

2021年8月7日

創建者 Batt J

2018年4月14日

創建者 Edwin A P V

2020年12月12日

創建者 Usman

2016年12月27日

創建者 BI C

2016年1月20日

創建者 Kazım S

2017年9月10日

創建者 Daniel A

2015年11月21日

創建者 Wonjun L

2016年3月6日

創建者 Ahmed A

2017年4月14日

創建者 Asier

2015年11月20日

創建者 Bruno F S

2016年2月15日

創建者 Muhammad A I

2019年9月10日

創建者 Gokhan C

2016年5月28日

創建者 NothingElse

2015年11月5日