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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Université de 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.
- 5 stars57,25 %
- 4 stars25,46 %
- 3 stars9,10 %
- 2 stars4,61 %
- 1 star3,56 %
Meilleurs avis pour DATA MANIPULATION AT SCALE: SYSTEMS AND ALGORITHMS
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.
Well structured and nice overview of data manipulation. But the assignments should really be updated in order to use python 3.x instead of 2.7, which is not maintained anymore...
The course is very coherent and comprehensive. It covers only important aspects of the fields. Also, the exercises are very well prepared.
Last week of the course is too much information and without any assignments it kind of doesn't make much sense and it doesn't stick.
À propos du Spécialisation Science des données à grande échelle
Learn scalable data management, evaluate big data technologies, and design effective visualizations.
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