The program will engage the undergraduate students in the latest data mining research methodologies and techniques to address multi-disciplinary applications in computational science and engineering fields such as environmental studies, sociology, economics, biology, chemistry, engineering, computer science, and information design. The goal of this project is to provide a diverse set of undergraduate students with the research skill set to uniquely position themselves in the expanding field of data mining. The program will engage the undergraduate students in the latest data mining research methodologies and techniques to address multi-disciplinary applications in computational science and engineering fields such as environmental studies, sociology, economics, biology, chemistry, engineering, computer science, and information design. The goal of this project is to provide a diverse set of undergraduate students with the research skill set to uniquely position themselves in the expanding field of data mining. The extraordinary spread of computers and online data is changing forever the way that important decisions are made in many organizations.
Hospitals now analyze online medical records to decide which treatments to apply to future patients.
Banks analyze past financial records to learn to spot future fraud, while factories analyze past operations to learn to produce higher quality goods. While a first generation of data mining algorithms has already been developed and is gaining widespread commercial use, we expect future advances to dramatically increase the role of data mining throughout society. Today's demand for data mining expertise far exceeds the supply, and this imbalance will become more severe over the coming decade. Unfortunately, current degree programs in traditional disciplines fail to provide the kind of training needed for the practitioners of the future. The problem is that data mining requires a rich, interdisciplinary education combining topics such as advanced machine learning algorithms, statistical principles that provide the foundations for these algorithms, database and data warehousing methods, complexity analysis, approaches to data visualization, privacy and security issues, and specific application areas such as business, marketing, and public policy.