ACM SIGKDD Executive Committee has set up the ACM SIGKDD Curriculum Committee to design a sample curriculum for data mining that gives recommendations for educating the next generation of students in data mining. Based on feedback from researchers, educators, and students, we are convinced that it is an important task to have a carefully designed, conceptually strong, technically rich, and balanced curriculum for this discipline. A comprehensive and balanced curriculum will ensure that the education in data mining sets a solid foundation for the healthy growth of the field, and it will promote systematic training of students in computer science, information sciences, and other related fields, and it will provide guidance for the training of the next generation of data mining researchers, developers and technology users.
Here is the
SIGKDD Curriculum Proposal for Data Mining Education, (PDF), developed by
Soumen Chakrabarti, Martin Ester, Usama Fayyad, Johannes Gehrke,
Jiawei Han, Shinichi Morishita, Gregory Piatetsky-Shapiro, and Wei Wang.