Skip to main content

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.

Mining Massive Datasets

There is one session available:

8,054 already enrolled! After a course session ends, it will be archived.
Starts Jul 29
Ends Dec 31
Estimated 7 weeks
5–10 hours per week
Self-paced
Progress at your own speed
Free
Optional upgrade available

About this course

Skip 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.

At a glance

  • Institution: StanfordOnline
  • Subject: Computer Science
  • Level: Advanced
  • 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.

  • Language: English
  • Video Transcript: English

What you'll learn

Skip 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

About the instructors

Frequently Asked Questions

Skip 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 residing in 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.

Interested in this course for your business or team?

Train your employees in the most in-demand topics, with edX for Business.