Course description
Mining Massive Datasets
The course is based on the text Mining of Massive Datasets by Jure Leskovec, Anand Rajaraman, and Jeff Ullman, who by coincidence are also the instructors for the course.
The book is published by Cambridge Univ. Press, but by arrangement with the publisher. The material in this on-line course closely matches the content of the Stanford course CS246.
The major topics covered include: MapReduce systems and algorithms, Locality-sensitive hashing, Algorithms for data streams, PageRank and Web-link analysis, Frequent itemset analysis, Clustering, Computational advertising, Recommendation systems, Social-network graphs, Dimensionality reduction, and Machine-learning algorithms.
Upcoming start dates
Who should attend?
Prerequisites
The course is intended for graduate students and advanced undergraduates in Computer Science. At a minimum, you should have had courses in Data structures, Algorithms, Database systems, Linear algebra, Multivariable calculus, and Statistics.
Course delivery details
This course is offered through Stanford University, a partner institute of EdX.
5-10 hours per week
Costs
- Verified Track -$149
- Audit Track - Free
Certification / Credits
What you'll learn
- MapReduce systems and algorithms
- Locality-sensitive hashing
- Algorithms for data streams
- PageRank and Web-link analysis
- Frequent itemset analysis
- Clustering
- Computational advertising
- Recommendation systems
- Social-network graphs
- Dimensionality reduction
- Machine-learning algorithms
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