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
Engaging problemset makes sure that you will get your hands dirty with data. And that is great! Definitely worth your time.
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.
- 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
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.
About the Data Science at Scale Specialization
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