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.
- 5 stars57.18%
- 4 stars25.42%
- 3 stars9.09%
- 2 stars4.74%
- 1 star3.55%
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.
Its pretty decent. I liked the assignments. However there were some typos in the lecture slides and also the grader output is not very friendly.
Comprehensive and clear explanation of theory and interlinks of the up-to-date tools, languages, tendencies. Kudos and thanks to Bill Howe.
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.