Course Description and Objectives:
Recent advances in information technology along with the phenomenal growth of the Internet have resulted in an explosion of data collected, stored, and disseminated by various organizations. Because of its massive size, it is difficult for analysts to sift through the data even though it may contain useful information. Data mining holds great promise to address this problem by providing efficient techniques to uncover useful information hidden in the large data repositories.
Awareness of the importance of data mining for business is becoming wide spread. The industry has created more and more job opportunities for people who have interdisciplinary data analytic skills. Indeed, this course intends to bridge the gap between data mining techniques and business applications. The students have the opportunities to learn both domain and technical knowledge to face the big data challenges in the industry.
The key objectives of this course are two-fold:
(1) to teach the fundamental concepts of data mining and
(2) to provide extensive hands-on experience in applying the concepts to real-world applications.
The core topics to be covered in this course include classification, clustering, association analysis, and anomaly/novelty detection. This course consists of about 13 weeks of lecture, followed by 2 weeks of project presentations by students who will be responsible for developing and/or applying data mining techniques to applications such as intrusion detection, Web usage analysis, financial data analysis, text mining, bioinformatics, systems management, Earth Science, and other scientific and engineering areas. At the end of this course, students are expected to possess the fundamental skills needed to conduct their own research in data mining or to apply data mining techniques to their own research fields.