provides an introduction to, strategies for, and applications of data mining, also known as knowledge discovery in databases. Data mining, "the nontrivial extraction of implicit, previously unknown, and potentially useful information from data," uses machine learning, statistical and visualization techniques to discover and present knowledge in a form which is easily comprehensible to humans. The course focuses on the analysis of data and the use of software techniques for finding patterns and regularities in sets of data. In particular, the course emphasizes data mining analysis which works from the data up and employs the best techniques developed with an orientation towards large volumes of data, making use of as much of the collected data as possible to arrive at reliable conclusions and decisions. The analysis process starts with a set of data, uses a methodology to develop an optimal representation of the structure of the data during which time knowledge is acquired. Once knowledge has been acquired in a particular field, students will extend this knowledge to larger sets of data working on the assumption that the larger data set has a structure similar to the sample data. Applications will be across the fields relevant to students careers and interests. 3 Credits.