Mining Massive Datasets

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Course Features

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Duration

7 weeks

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Delivery Method

Online

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Available on

Limited Access

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Accessibility

Mobile, Desktop, Laptop

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Language

English

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Subtitles

English

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Level

Advanced

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Effort

10 hours per week

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Teaching Type

Self Paced

Course Description

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.

Course Overview

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International Faculty

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Instructor-Moderated Discussions

Skills You Will Gain

Prerequisites/Requirements

At a minimum, you should have had courses in Data structures, Algorithms, Database systems, Linear algebra, Multivariable calculus, and Statistics.

The course is intended for graduate students and advanced undergraduates in Computer Science

What You Will Learn

Algorithms for data streams

Clustering

Computational advertising

Dimensionality reduction

Frequent itemset analysis

Locality-sensitive hashing

Machine-learning algorithms

MapReduce systems and algorithms

PageRank and Web-link analysis

Recommendation systems

Social-network graphs

Course Instructors

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Anand Rajaraman

Instructor at Stanford University

Anand is a serial entrepreneur, venture capitalist, and academic, based in Silicon Valley. He founded two successful startups, Junglee (acquired by Amazon) and Kosmix (acquired by Walmart). At Amazon...
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Jeffrey D. Ullman

Professor of Engineering, Emeritus at Stanford University

Jeff Ullman is the Stanford W. Ascherman Professor of Engineering (Emeritus) in the Department of Computer Science at Stanford and CEO of Gradiance Corp. He received the B.S. degree from Columbia Uni...
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Jure Leskovec

Associate Professor of Computer Science at Stanford University

Jure is an associate professor of computer science at Stanford. His research area is mining of large social and information networks. He is the author of the Stanford Network Analysis Platform, a gen...
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