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.
ワシントン大学（University of Washington）
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.
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- 4 stars25.46%
- 3 stars9.10%
- 2 stars4.61%
- 1 star3.56%
DATA MANIPULATION AT SCALE: SYSTEMS AND ALGORITHMS からの人気レビュー
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.
A great way to start, and become familiar with the nature, requirements & analytics of today's data.
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
Very good introduction to relational algebra and map reduce. Also helped scratch up on some python and SQL.
Learn scalable data management, evaluate big data technologies, and design effective visualizations.