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The College of St. Scholastica
1200 Kenwood Avenue
Duluth, MN 55811
(218) 723-6046
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Brandon Olson, Ph.D.
Department Chair
Tower Hall, Room 3600
(218) 723-6199

Data Analytics

Fast Facts: Data Analytics Minor

  • Analytics is the science of analysis applying technologies, quantitative evaluation, and evaluative and predictive models to data in support of decision making. Data analytics is applied to a domain rather than serving as a domain in of itself. As a result, the minor is applicable to any major where students benefit from developing analytical skills.
  • Builds a solid foundation of data analytics to evaluate business challenges and formulate questions leading to solutions in any field
  • Complements majors in fields such as computer information systems, business management, communication, marketing, psychology and various scientific and technical fields or any field that uses data to make decisions
  • Students are able to identify data needs to answer business questions, apply modern techniques to prepare data for analysis and communicate results effectively

Program requirements

Minor: 20 credits

Sample curriculum

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.

Course Creation Center

Expand and Collapse Coursework

Expand and Collapse 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.

Expand and Collapse 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.

Expand and Collapse MGT 3130 - Quantitative Business Analysis

Provides a foundational exploration of topics such as forecasting, quality assurance, project management and other mathematical models for data analysis. Emphasis is on applications of solutions of real world problems. Software is used to solve and illustrate problems and solutions.

Expand and Collapse 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.

Expand and Collapse 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.

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