Mode of learning : Online - Self Paced
Domain / Subject : Engineering & Technology
Function : Information Technology(IT)
Trainer name : Jure Leskovec
Starts on : 6th Jan 2015
Difficulty : Advanced
CS246 - Mining Massive Data Sets
The importance of data to business decisions, strategy and behavior has proven unparalleled in recent years. Predictive analytics, data mining and machine learning are tools giving us new methods for analyzing massive data sets. Companies place true value on individuals who understand and manipulate large data sets to provide informative outcomes.
Pivotal issues pertaining to mining massive data sets will range from how to deal with huge document databases and infinite streams of data to mining large social networks and web graphs.
Practical hands-on experience will entail the design of algorithms for analyzing very large amounts of data and to learn existing data mining and machine learning algorithms. As a useful analytic tool, case studies will provide first-hand insight into how big data problems and their solutions allow companies like Google to succeed in the market.
Students enrolling under the non degree option are required to take the course for 4.0 units.
At least one: Computer Organizations & Systems (Stanford Course CS107) OR Introduction to Databases (Stanford Course CS145) or equivalent
At least one: Intro to Probability for Computer Scientists (Stanford Course CS109) OR Theory of Probability (Stanford Course STATS116) or equivalent
Non Degree Option
Note: Enrolling in this course for credit under the Non Degree Option requires an approved application. If you do not already have an approved application on record, the application will be presented to you as part of the checkout process. If your application is denied, tuition and fees for the course will be refunded.
Students enrolled in a graduate course for credit are required to complete homework assignments, projects, and take exams as required of all students during the 10-week quarter. Information regarding textbooks and materials is usually covered in the first lecture and may also be found on the course Web site.
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