SKIP TO COURSE REQUIREMENTS
Two tracks are available. Track 1 is for Non-College of Management students. Track 2 is for College of Management students.
Business Analytics Minor– Courses and Requirements:
- Track 1: Non-College of Management Majors
- Stat 201 Statistics
- Data 211 Data Science and Data Visualization
- Track 2: College of Management Majors
- MIS 100 Fundamentals in IT in Organizations
- Stat 201: Statistics
- MIS 310 Principles of MIS
Minor Required Courses for BOTH tracks:
- MIS 335 Management and Use of Databases
- MIS 380 Business Intelligence and Analytics
- MIS 480 Predictive Analytics
Minor Electives for BOTH tracks – Choose One:
- STAT 301 Analysis of Variance and Multivariate Analysis
- STAT 311 Regression Analysis
- STAT 480 Statistical Consulting
- DATA 499 Data Science Capstone
At least 16 credits from among the Required Courses and Electives must be completed at Metropolitan State. See also the COM policies page for requirements that are common to all programs.
Requirements (24 credits)
This course covers the basic principles and methods of statistics. It emphasizes techniques and applications in real-world problem solving and decision making. Topics include frequency distributions, measures of location and variation, probability, sampling, design of experiments, sampling distributions, interval estimation, hypothesis testing, correlation and regression.
Full course description for Statistics I
Choose one of the two courses below
This course is designed to define the role of information systems in organizations, and in particular the roles of IS staff and end-users in developing and maintaining computer systems. The managerial aspects and implications of databases, telecommunications, hardware, software and e-commerce are included. Special attention is given to management information systems theories in the organizational setting including: infrastructure, transaction processing, operational reporting, decision support systems and executive information systems. Also included are all phases of the systems development life cycle (SDLC) as well as alternative development methodologies. The course prototypically includes analysis of real world business cases and post-implementation audit report of a recently completed management information system. All students taking this class must have completed as a prerequisite the MIS 100 Fundamentals of Information Technology in Organizations course or its approved…
Full course description for Principles of Management Information Systems
An introduction to methods and techniques commonly used in data science. This course will use object-oriented computer programming related to the processing, summarization and visualization of data, which will prepare students to use data in their field of study and to effectively communicate quantitative findings. Topics will include basics in computer programming, data visualization, data wrangling, data reshaping, ethical issues with the use of data, and data analysis using an object-oriented programming language. Students will complete a data science project.
Full course description for Data Science and Visualization
Competence in management and use of organizational and external databases is a skill needed by all business people and critical to management information systems effectiveness, especially in the new era of "big data". This course teaches the development and accessing of internal and external information resources. Topics include: ensuring the availability of appropriate data; interrelating and applying data to typical business problems; normalized database design; protecting and managing information resources; scalability; and compatibility issues.
Full course description for Management and Use of Databases
Business Intelligence is the user-centered process of exploring data, data relationships and trends - thus helping to improve overall decision making for enterprises. This course addresses the iterative processes of accessing data (ideally stored in the enterprise data warehouse) and analyzing data in order to derive insights and communicate findings. Moreover, the course also addresses the use of software tools for analysis and visualization of data, especially report design along with the use of dashboards.
Full course description for Business Intelligence and Analytics
This course builds upon prior coursework related to analytical thinking and competence in business intelligence and analytics approaches. The course serves to advance and refine expertise on theories, approaches, tools and techniques related to prediction and forecasting in business. Students will gain practical experience in analyzing a variety of business analytics cases and scenarios using industry-standard tools and platforms. The course prepares learners to help organizations make more effective business decisions based on the gathering and analysis of data. The design and delivery of the course enables an engaged learning environment.
Full course description for Predictive Analytics
This course covers introductory and intermediate ideas of the analysis of variance (ANOVA) method of statistical analysis. The course builds on the ideas of hypothesis testing learned in STAT201 (Statistics I). The focus is on learning new statistical skills and concepts for real-world applications. Students will use statistical software to do the analyses. Topics include one-factor ANOVA models, two-factor ANOVA models, repeated-measures designs, random and mixed effects, principle component analysis, linear discriminant analysis and cluster analysis.
Full course description for Analysis of Variance and Multivariate Analysis
This course covers fundamental to intermediate regression analysis. The course builds on the ideas of hypothesis testing learned in STAT201 (Statistics I). The focus is on learning new statistical skills and concepts for real-world applications. Students will use statistical software to do the analyses. Topics include simple and bivariate linear regression, residual analysis, multiple linear model building, logistic regression, the general linear model, analysis of covariance, and analysis of time series data.
Full course description for Regression Analysis
This advanced workshop will give students exposure to the statistical and non-statistical issues that arise in statistical problem solving, and provide an experiential background in statistical consulting. Students will develop the knowledge, skills, and professional rapport necessary to interact with clients, including the skills necessary for communicating technical statistical content
Full course description for Statistical Consulting
This course provides a culminating experience in formulating and resolving data science and business analytics questions, regardless of domain or nature of scientific inquiry. Students work in teams on a comprehensive project to apply data science concepts and principles. Students will complete a real-world project with data collection, data cleaning, data visualization, data modeling and analysis, and presentations of findings.
Full course description for Data Science Capstone