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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- 5 stars57,23%
- 4 stars25,39%
- 3 stars9,07%
- 2 stars4,73%
- 1 star3,55%
Principais avaliações do MANIPULAÇÃO DE DADOS EM ESCALA: SISTEMAS E ALGORITIMOS
Engaging problemset makes sure that you will get your hands dirty with data. And that is great! Definitely worth your time.
Definitely need some background in R or Python and the lectures are a bit old. Seem to be from around 2013 when this first came out but most of the info is still relevant.
It's pretty tough in assignments especially when there are mistakes in the given description, but I do learn the basic concepts of relational algorithm and MapReduce from them.
Comprehensive and clear explanation of theory and interlinks of the up-to-date tools, languages, tendencies. Kudos and thanks to Bill Howe.
Sobre Programa de cursos integrados Ciência de dados em larga escalaCiência de Dados em Larga Escala
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