Management Analytics and Decision-making Minor
The Minor in Management Analytics and Decision-Making (MAD) will immerse students in interdisciplinary courses that foster rigorous analytical and communication skills, as well as critically thinking, with regards to managing data and analytics in complex environments. Students who earn the MAD Minor will attain skills that will help them succeed in interdisciplinary environments, solve problems, and manage resources mindful of risk, uncertainty, human dimensions, and sustainability.
Data governance and ethical considerations underlie decision making and resource management. Courses rely on case studies and applied projects to exemplify the diverse challenges encountered when simultaneously seeking profitability, social justice, and environmental sustainability. Students are introduced to the fundamentals of entrepreneurial decision-making, as well as the ethics of data use, custodianship, and communication. Case studies and project materials are drawn from real management problems from the Central Valley, Sierra Nevada, and Bay Area, incorporating content from public and private stakeholders in a rapidly developing region with an economy dependent on agriculture, food processing, and services; a sparsely populated area with an economy shaped by tourism, ranching, and extensive public lands management; and a developed, highly urbanized region at the forefront of the global technology-enabled services economy, surrounded by an extensive wildland-urban interface that exemplifies sustainability challenges with global relevance.
In summary, the minor in Management Analytics and Decision-making (MAD) provides students the tools to collect, analyze, manage, visualize and communicate data in support of diverse management applications, particularly in the context of "triple bottom-line" thinking, that is, with a focus on People, Planet, and Profit.
Minimum Requirements
To be awarded a minor in Management Analytics and Decision-Making, students must successfully complete at least 20 units.
Fundamental Requirement [4 units] – Complete one of the following courses (which is not a prerequisite for other MAD courses):
- MIST 050: Introduction to Entrepreneurship Units: 4
- MIST 060: Introductory Data Analytics Units: 4
- MIST 070: Innovation Management Units: 4
Core Areas Requirement [12 units] - Complete three courses chosen from the following:
- MIST 011: Climate Justice Units: 4
- MIST 116/ENVE 116/ESS 132: Applied Climatology Units: 4
- MIST 118/ENVE 118: Climate Change: Science and Solutions Units: 4
- MIST 128/COGS 128: Cognitive Engineering Units: 4
- MIST 130: Statistical Data Analysis and Optimization in R for Decision Support Units: 4
- MIST 131: Data Governance for Analytics Projects Units: 4
- MIST 132: Geographic Information Systems Analysis in Management Units: 4
- MIST 133: Service Innovation Units: 4
- MIST 134: Methods of Data and Network Science Units: 4
- MIST 135: Technical Communication and Visualization Skills Units: 4
- MIST 136: Retailing Management Units: 4
- MIST 137: Managing Teamwork Units: 4
- MIST 138: Systematic Financial Trading and Analysis Units: 4
- MIST 164: Energy Policy / ENVE 164: Energy Policy Units: 4
- MIST 175: Information Systems for Management Units: 4 / ENGR 175: Information Systems for Management / MGMT 170: Information Systems for Management
- MIST 190: Special Topics
Elective Requirement [4 units] - Complete one course chosen from the following:
- BIOE 108: Genetic Engineering Units: 3
- COGS 103: Introduction to Neural Networks in Cognitive Science Units: 4
- COGS 104: Complex Adaptive Systems Units: 4
- COGS 105: Research Methods for Cognitive Scientists Units: 4
- COGS 182: Service Science Units: 4 /MGMT 150: Service Science
- CSE 100: Algorithm Design and Analysis Units: 4
- CSE 111: Database Systems Units: 4
- CSE 120: Software Engineering Units: 4
- CSE 126: Information Systems and Service Design Units: 4 / MGMT 126: Information Systems and Service Design
- CSE 173: Computational Cognitive Neuroscience Units: 4 /COGS 123: Computational Cognitive Neuroscience
- CSE 175: Introduction to Artificial Intelligence Units: 4 /COGS 125: Introduction to Artificial Intelligence
- CSE 176: Introduction to Machine Learning Units: 4
- ECON 110: Econometrics Units: 4
- ENGR 180: Spatial Analysis and Modeling Units: 4
- ENVE 155: Decision Analysis in Management Units: 4
- ESS 110: Hydrology and Climate Units: 4
- ESS 132: Applied Climatology Units: 3
- MATH 180: Modern Applied Statistics Units: 4
- MGMT 180: Entrepreneurship Theory and Practice Units: 4
- ME 135: Finite Element Analysis Units: 4
- ME 137: Computer Aided Engineering Units: 3
- ME 141: Control Engineering Units: 4
- ME 142: Mechatronics Units: 4
- MSE 104: Engineering Living Systems Units: 3
- MSE 119: Materials Simulations Units: 3
- POLI 175: Advanced Analysis of Political Data Units: 4
- PSY 105: Advanced Research Methods in Psychology Units: 4
- PSY 171: Psychological Tests and Measurement Units: 4
Management Analytics and Decision-Making Minor Program Learning Outcomes
Students will engage in hands-on, practical experiences with data-driven analytics, professional communication, and entrepreneurship to acquire a knowledge base to manage complex systems. As such, the courses in the minor are built around four Program Learning Outcomes (PLOs) associated with management of complex systems, analytics, and communication of quantitative results:
- Critical Thinking and Analytics for Management of Complex Systems. Students will identify and use appropriate analytical, quantitative, and data-oriented techniques and apply reasoning to evaluate case studies for strategic decision-making in a multi-disciplinary setting and in the management of complex systems. Quantitative techniques and programming languages may be extracted from a number of different disciplines.
- Communication of Quantitative Analysis, Results, and Implications. Students will communicate effectively in classroom settings and with business and community stakeholders, preparing and delivering clear, persuasive, and professional oral and written presentations. When appropriate, emphasis will be placed on using data-driven methods and technologies to enhance visual representation and communication of information.
- Leadership and Teamwork in Practice. Students will apply principles and practices of effective leadership and teamwork in classroom and project settings.
- Ethics and Sustainability. Students will apply knowledge of ethical and legal. Students will apply knowledge of ethical and legal requirements and of professional, societal and cultural contexts of coupled environments.
Transfer Students
Courses similar to some of those in our proposed Core and Electives lists, particularly related to data science, GIS, research methods, and communication are offered at many California community colleges. Transfer students may receive credit for two such courses, assessed on a case-by-case basis by the MCS Department Chair in consultation with academic advising staff. Transfer students will be required to take a required Fundamentals course and at least two Core courses at UC Merced.