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.
Об этом курсе
- 5 stars57,23 %
- 4 stars25,39 %
- 3 stars9,07 %
- 2 stars4,73 %
- 1 star3,55 %
Лучшие отзывы о курсе DATA MANIPULATION AT SCALE: SYSTEMS AND ALGORITHMS
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.
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.
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.
covers a lot of ground quickly, but you still get a good understanding of the underlying theory or technologies
Специализация Наука о больших данных: общие сведения
Часто задаваемые вопросы
Когда я получу доступ к лекциям и заданиям?
Что я получу, оформив подписку на специализацию?
Можно ли получить финансовую помощь?
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