About this courseSkip About this course
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, you can download a free copy Here. 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.
What you'll learnSkip What you'll learn
- MapReduce systems and algorithms
- Locality-sensitive hashing
- Algorithms for data streams
- PageRank and Web-link analysis
- Frequent itemset analysis
- Computational advertising
- Recommendation systems
- Social-network graphs
- Dimensionality reduction
- Machine-learning algorithms
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Frequently asked questions
How much work is expected?
The amount of work will vary, depending on your background and the ease with which you follow mathematical and algorithmic ideas. However, 10 hours per week is a good guess.
Who can take this course?
Unfortunately, learners from one or more of the following countries or regions will not be able to register for this course: Iran, Cuba and the Crimea region of Ukraine. While edX has sought licenses from the U.S. Office of Foreign Assets Control (OFAC) to offer our courses to learners in these countries and regions, the licenses we have received are not broad enough to allow us to offer this course in all locations. edX truly regrets that U.S. sanctions prevent us from offering all of our courses to everyone, no matter where they live.