1. 1 Data Mining Education Across Disciplines Dr. Alan Safer and Dr. Lesley Farmer California State University Long Beach asafer@csulb.edu / lfarmer@csulb.edulfarmer@csulb.edu

2. 2 What is Data Mining? Data mining is essentially the process of uncovering meaningful new correlations, patterns and trends from large quantities of complex data using statistical and mathematical techniques.

3. 3 Looking for Differences Among Disciplines Keywords Publications Courses offered Textbooks used Software used

4. 4 Methodology Identification by professional association of accredited programs Content analysis of graduate program websites Literature review of discipline-specific database aggregators (“data min*)

5. 5 Data Mining Education Business: identifying credit card fraud, insider trading patterns, defect analyses Sciences: medicare fraud, astronomical variations, and disease risk Statistics: fuzzy logic, theory and applications Library/Information Sciences: research and best practices (e.g., health, business)

6. 6 Course Offerings DisciplineNumber of departments with data mining courses Percent of total number of departments in discipline offering data mining courses Business8317.60% Computer Science/ Engineering 18748.80% Statistics4628.00% Library/ Information Science 1530.00%

7. 7 Comparative Key Words Business: decision-making, management, and competition Computer science/engineering: technology-related and intelligence-related terms Statistics: methodological terms. Library/info science: greatest variation; most terms associated with applications (e.g., bibiometrics, health informatics, and information management) Greatest overlap existed between business and library/information science due to decision-making methodology and management issues.

8. 8 The Domains of Data Mining

9. 9 The Domains of Data Mining In Library/Information Sciences

10. 10 Major Textbooks Artificial Intelligence: A Modern Approach (3rd ed.), by Russell S., and Norvig, P. (2009), Prentice Hall. Pattern Classification (2nd ed.), by Duda, R., Hart, P., and Stork, D. (2000), Wiley- Interscience. Machine Learning, by Mitchell, T. (1997), McGraw-Hill. Introduction to Data Mining, by Tan, P. et. al. (2006), Addison Wesley.

11. 11 Titles Patterns Little agreement on textbooks except in computer science/engineering. In specialized subsets of field (e.g., biometrics), few titles available from which to choose Textbook choice depends on specific course objectives and content focus

12. 12 Major Software SAS: business and statistics Matlab and C++: computer science SPSS, SQL, Excel : library/info science Factors: student ability, DB features

13. 13

14. 14 Bottom Line about Data Mining Education Blend of theory and practice that reflects each academic discipline rather than a unified system