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
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About this Course
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Try Coursera for BusinessSkills you will gain
- Relational Algebra
- Python Programming
- Mapreduce
- SQL
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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
Graph Analytics
Reviews
- 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
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...
covers a lot of ground quickly, but you still get a good understanding of the underlying theory or technologies
Engaging problemset makes sure that you will get your hands dirty with data. And that is great! Definitely worth your time.
Very good introduction to relational algebra and map reduce. Also helped scratch up on some python and SQL.
About the Data Science at Scale Specialization

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