Introduction
The recently introduced terminology of Big Data refers to data sets whose volume (amount of data collected, number of data sources), velocity (rate at which data is collected) and variety (heterogeneity of data and data sources) are so extreme that advanced data mining algorithms are needed to process and discover useful patterns in data for actionable intelligent decisions, in a reasonable amount of time.The purpose of this course is to introduce theoretical as well as practical aspects of advanced algorithms for mining massive datasets. Topics include: dimension reduction techniques, similarity algorithms, streaming, web mining, on-line mining, recommendation systems, market-basket models, and Naive Bayes & Bayesian networks.
Teacher
David Pfeffer
ADJUNCT PROFESSOR
Email: dpfeffer@stevens.edu
Education
Bachelors of Science in Computer Science
Stevens Institute of Technology
Hoboken, NJ
Honors; Minor in Law and Public Policy
Masters of Science in Computer Science
Stevens Institute of Technology
Hoboken, NJ
4.0 GPA; Graduate Certificates in Computer Systems, Databases & Service Oriented Architecture, Distributed Systems, Enterprise Computing, Quantitative Software Engineering, Service Oriented Computing
Professional Societies
Member of the IEEE Computer Society and ACM.
Courses
CS 521 TCP/IP Networking
CS 570 Introduction to Programming, Data Structures, and Algorithms
CS 465 Selected Topics in Computer Science
SSW 810 Selected Topics in Systems Centric Software Engineering