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.
This course is part of the Data Science at Scale Specialization
About this Course
Skills you will gain
- Relational Algebra
- Python Programming
Syllabus - What you will learn from this course
Data Science Context and Concepts
Relational Databases and the Relational Algebra
MapReduce and Parallel Dataflow Programming
NoSQL: Systems and Concepts
- 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
A great way to start, and become familiar with the nature, requirements & analytics of today's data.
Comprehensive and clear explanation of theory and interlinks of the up-to-date tools, languages, tendencies. Kudos and thanks to Bill Howe.
- 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
Great course that strikes a balance between teaching general principles and concepts, and providing hands-on technical skills and practice.
The lessons are well designed and clearly conveyed.
About the Data Science at Scale Specialization
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