Advanced Data Mining Techniques
Master advanced algorithms and systems for mining massive datasets, including MapReduce, PageRank, and machine learning.
Master advanced algorithms and systems for mining massive datasets, including MapReduce, PageRank, and machine learning.
This comprehensive course, based on the textbook "Mining of Massive Datasets," covers advanced techniques for analyzing and processing large-scale data. Students learn essential algorithms and systems including MapReduce, locality-sensitive hashing, and PageRank. The curriculum encompasses data streams, frequent itemset analysis, clustering, computational advertising, recommendation systems, social network analysis, and dimensionality reduction. Taught by the textbook's authors, the course closely follows Stanford's CS246 content.
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What you'll learn
Master MapReduce systems and algorithmic implementations
Understand locality-sensitive hashing and stream processing
Apply PageRank and web-link analysis techniques
Develop skills in clustering and frequent itemset analysis
Implement recommendation systems and social network analytics
Skills you'll gain
This course includes:
PreRecorded video
Graded assignments, exams
Access on Mobile, Tablet, Desktop
Limited Access access
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Module Description
This advanced course covers comprehensive techniques for mining massive datasets, based on Stanford's CS246 curriculum. The course material spans fundamental algorithms like MapReduce and PageRank to advanced topics in machine learning and social network analysis. Students learn practical applications in computational advertising, recommendation systems, and data stream processing, while mastering essential concepts in clustering and dimensionality reduction.
Fee Structure
Instructors
2 Courses
Pioneering Computer Scientist and Educator
Jeffrey D. Ullman is the Stanford W. Ascherman Professor of Engineering (Emeritus) in the Department of Computer Science at Stanford University and CEO of Gradiance Corp. After earning his B.S. from Columbia University in 1963 and Ph.D. from Princeton in 1966, he worked at Bell Laboratories before joining Princeton's faculty in 1969. In 1979, he moved to Stanford, where he served as chair of the Computer Science Department from 1990 to 1994. Ullman's contributions to computer science span databases, compilers, automata theory, and algorithms, authoring 16 influential books that have shaped generations of computer scientists. His work with Alfred Aho, particularly on compiler design and algorithms, earned them the 2020 Turing Award, the highest honor in computer science. Ullman's numerous accolades include election to the National Academy of Engineering, the American Academy of Arts and Sciences, and prestigious awards such as the Knuth Prize and the IEEE von Neumann medal. At Stanford, he developed courses on database systems, automata theory, and compilers, while also creating innovative online learning platforms through Gradiance Corporation. His teaching and mentorship have profoundly influenced the field, with many of his students becoming distinguished computer scientists in academia and industry.
1 Course
Stanford's Network Science Pioneer and AI Innovator
Dr. Jure Leskovec, serving as Professor of Computer Science at Stanford University, has established himself as a leading authority in network science and machine learning since joining the faculty in 2009 after earning his PhD from Carnegie Mellon University. As the creator of the Stanford Network Analysis Platform (SNAP) and Chief Scientist at Pinterest, he combines academic excellence with practical industry applications, focusing on developing computational models for analyzing massive networks across social media, technological systems, and biomedicine. His groundbreaking research has earned him numerous accolades, including the ICDM Research Contributions Award, the Lagrange Prize, and selection as a Sloan Research Fellow, while his teaching portfolio includes influential courses in machine learning with graphs and mining massive datasets. Through his leadership roles at Stanford's AI Lab, Machine Learning Group, and Center for Research on Foundation Models, Dr. Leskovec continues to advance the field of network science, with his work finding applications in major technology companies like Facebook and Uber, as well as contributing to public health initiatives during the COVID-19 pandemic. His research spans graph neural networks, geometric deep learning, and recommender systems, while his recent work focuses on foundation models and their applications in scientific discovery, making him a pivotal figure in shaping the future of artificial intelligence and network analysis.
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14,959 ratings
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