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.
About this Course
Skills you will gain
- 5 stars57.23%
- 4 stars25.39%
- 3 stars9.07%
- 2 stars4.73%
- 1 star3.55%
TOP REVIEWS FROM DATA MANIPULATION AT SCALE: SYSTEMS AND ALGORITHMS
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.
- great and very useful overview of concepts important in big data that does not get bogged down in random details
- interesting and sufficiently challenging assignments
Comprehensive and clear explanation of theory and interlinks of the up-to-date tools, languages, tendencies. Kudos and thanks to Bill Howe.
The course is very coherent and comprehensive. It covers only important aspects of the fields. Also, the exercises are very well prepared.
About the Data Science at Scale Specialization
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