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.
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大规模数据科学 专项课程华盛顿大学课程信息
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第 1 门课程（共 4 门）
完成时间大约为20 小时
英语（English）
字幕：阿拉伯语（Arabic）, 法语（French）, （欧洲人讲的）葡萄牙语, 意大利语, 越南语, 德语（German）, 俄语（Russian）, 英语（English）, 西班牙语（Spanish）
您将获得的技能
- Relational Algebra
- Python Programming
- Mapreduce
- SQL
可灵活调整截止日期
根据您的日程表重置截止日期。
可分享的证书
完成后获得证书
100% 在线
立即开始，按照自己的计划学习。
第 1 门课程（共 4 门）
完成时间大约为20 小时
英语（English）
字幕：阿拉伯语（Arabic）, 法语（French）, （欧洲人讲的）葡萄牙语, 意大利语, 越南语, 德语（German）, 俄语（Russian）, 英语（English）, 西班牙语（Spanish）
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华盛顿大学
Founded in 1861, the University of Washington is one of the oldest state-supported institutions of higher education on the West Coast and is one of the preeminent research universities in the world.
授课大纲 - 您将从这门课程中学到什么
完成时间为 6 小时
Data Science Context and Concepts
Understand the terminology and recurring principles associated with data science, and understand the structure of data science projects and emerging methodologies to approach them. Why does this emerging field exist? How does it relate to other fields? How does this course distinguish itself? What do data science projects look like, and how should they be approached? What are some examples of data science projects?
完成时间为 6 小时
22 个视频 （总计 125 分钟）, 4 个阅读材料, 1 个测验
完成时间为 5 小时
Relational Databases and the Relational Algebra
Relational Databases are the workhouse of large-scale data management. Although originally motivated by problems in enterprise operations, they have proven remarkably capable for analytics as well. But most importantly, the principles underlying relational databases are universal in managing, manipulating, and analyzing data at scale. Even as the landscape of large-scale data systems has expanded dramatically in the last decade, relational models and languages have remained a unifying concept. For working with large-scale data, there is no more important programming model to learn.
完成时间为 5 小时
24 个视频 （总计 122 分钟）
完成时间为 5 小时
MapReduce and Parallel Dataflow Programming
The MapReduce programming model (as distinct from its implementations) was proposed as a simplifying abstraction for parallel manipulation of massive datasets, and remains an important concept to know when using and evaluating modern big data platforms.
完成时间为 5 小时
26 个视频 （总计 122 分钟）
完成时间为 3 小时
NoSQL: Systems and Concepts
NoSQL systems are purely about scale rather than analytics, and are arguably less relevant for the practicing data scientist. However, they occupy an important place in many practical big data platform architectures, and data scientists need to understand their limitations and strengths to use them effectively.
完成时间为 3 小时
36 个视频 （总计 166 分钟）
完成时间为 2 小时
Graph Analytics
Graph-structured data are increasingly common in data science contexts due to their ubiquity in modeling the communication between entities: people (social networks), computers (Internet communication), cities and countries (transportation networks), or corporations (financial transactions). Learn the common algorithms for extracting information from graph data and how to scale them up.
完成时间为 2 小时
21 个视频 （总计 91 分钟）
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来自大规模数据处理：系统与算法的热门评论
由 MS 提供Jan 4, 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\n\nI recommand this course
由 HA 提供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.
由 WL 提供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.
由 DK 提供Jan 23, 2016
Good! I like the final (optional) project on running on a large dataset through EC2. The lectures aren't as polished and compact as they could be but certainly a very valuable course.
关于 大规模数据科学 专项课程
Learn scalable data management, evaluate big data technologies, and design effective visualizations.
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