Minor in Data Science/Analytics. The minor in data science/analytics requires the completion of 20 semester hours of the following courses with grades of C- or better: MATH-180 Statistical Methods, MATH-201 Linear Algebra, CSCI-181 Principles of Computer Science, CSCI-301 Data Mining, and CSCI-401 Machine Learning.
Double-counting restriction for interdisciplinary minors: only 4 semester hours of this minor may be double-counted toward the student's major.
CSCI-301 Data Mining
This course provides an introduction to the concepts in the automatic extraction of implicit, previously unknown and potentially useful information from large data that are generated in commerce, science and other areas. Topics include preprocessing of the data, application of the fundamental algorithms on the prepared data and interpretation of the patterns discovered by the algorithms. The fundamental algorithms for supervised learning, including classification and numerical prediction, and unsupervised earning, which includes association rules and clustering, are introduced. Prerequisites: MATH-180 Statistical Methods and CSCI-181 Principles of Computer Science. 4 SH.
CSCI-401 Machine Learning
This course provides an introduction to the systematic study of algorithms and systems that improve their knowledge or performance with experience. A statistical approach that emphasizes concepts and the implementation of the methods is presented to make sense of large and complex data. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines and clustering. Prerequisites: MATH-180 Statistical Methods, MATH-201 Linear Algebra and CSCI-181 Principles of Computer Science. 4 SH.