The College of St. Scholastica
1200 Kenwood Avenue
Duluth, MN 55811
TTY/TDD: (218) 723-6790
Brandon Olson, Ph.D.
Tower Hall, Room 3600
Fast Facts: Data Analytics Minor
Minor: 20 credits
Here are some classes you could take as part of this minor. Please note that you would not necessarily need all of these courses to fulfill a minor. This list doesn't include general education courses. Be sure to create your course plan in consultation with your advisor.
CIS 3107 - Database Modeling
Provides an understanding of fundamental concepts in the management of data, hands-on experience with a small-scale database management system, and an awareness of the application of business data base management systems. Lab exercises involve use of a relational DBMS to load, update and retrieve information from a database.
CIS 3115 - Machine Learning for Data Science
Explores how machine learning algorithms are applied to data science problems. This includes examining how data is used within the scientific method to justify hypotheses, but also how poor data can result in machines the discriminate against some populations. Students will look at a wide range of classification and regression problems from business, healthcare and the arts. Students will implement machine learning algorithms using current tools that require minimal programming and learn to analyze and visualize data and write clear descriptions of their processes and analysis of their work.
MGT 3130 - Quantitative Business Analysis
Includes forecasting, quality assurance, project management and other mathematical models for data analysis. Software is used to solve and illustrate problems and solutions.
MTH 4411 - Probability and Statistics I
A survey course in mathematical probability and statistics. It includes probability distributions and densities, mathematical expectations, functions of random variables, introduction to estimation theory and hypothesis testing and applications. Prerequisite: MTH 2222.
PSY 3331 - Statistics
Covers basic statistical concepts and methods useful in conducting research and evaluating results of studies done by others. Topics include frequency distributions and graphs, measures of central tendency and variability, transformed scores, correlations, multiple regression, hypothesis testing (t test, analysis of variance, and chi square), selection of appropriate statistics, calculation with MS Excel spreadsheets and SPSS, interpretation of the "results" sections of journal articles, and numeracy (understanding and using numbers in decision-making). Prerequisite: competence in arithmetic.