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.
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.
- 5 stars57.25%
- 4 stars25.46%
- 3 stars9.10%
- 2 stars4.61%
- 1 star3.56%
Very good course, but lectures could be more tuned onto the home assignments. A lot of independent work for me at least. Teacher is very good.
covers a lot of ground quickly, but you still get a good understanding of the underlying theory or technologies
Comprehensive and clear explanation of theory and interlinks of the up-to-date tools, languages, tendencies. Kudos and thanks to Bill Howe. Highly recommended.
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.