Decision Sciences

DS 110 Calculus with Business Applications (Units: 3)

Prerequisite: First-Year Math Advising Module. For students who wish to take business calculus in one semester.

Basic quantitative reasoning and employment of fundamental mathematical principles to solve business problems. Elements of calculus, mathematics of finance, and decision-making.

Course Attributes:

• 2: MATH/Quantitative Reason
• B4: Math/QR

DS 199 Decision Sciences Make-Up (Unit: 1)

Prerequisite: Permission of the instructor.

Additional study to make-up for partial equivalents in Decision Sciences courses. May be repeated for a total of 2 units.

DS 212 Business Statistics (Units: 3)

Prerequisite: DS 110* or MATH 108* or MATH 110* or MATH 226*.

Statistical methods essential in solving business problems including probability distributions, estimation and tests of hypotheses, and regression analysis.
(This course is offered as DS 212 and ECON 212. Students may not repeat the course under an alternate prefix.)

DS 310 Critical Data Analysis for Business (Units: 3)

Prerequisites: GE Areas 1A/A2, 1B/A3, and 2/B4; or permission of the instructor.

Explores data analysis tools and provides opportunities to investigate business topics from a social justice perspective. Critical analysis of visualizations to aid in communication and decision making. Discussion of data analyses undertaken on business topics relating to the 3 Ps of sustainability (people, planet, profit).

DS 311 Technologies in Data Analytics (Units: 3)

Prerequisites: DS 212/ECON 212 or MATH 124; ISYS 263; or Statistics majors with MATH 440.

Data processing and visual analytics are emerging fields concerned with extracting, cleaning, analyzing, and presenting complex high-dimensional data. Survey of state-of-the-art data processing and visualization techniques with the most updated technologies from the industry. Emphasis on practical challenges involving complex real-world data and include several hands-on group projects using different software packages. Hands-on use of software such as SQL, Tableau, R, and Python to uncover insights, communicate critical findings, and create data-driven solutions. (Plus-minus letter grade only)

DS 312 Data Analysis for Business (Units: 3)

Prerequisites: DS 212/ECON 212 or MATH 124, ISYS 263 or passing the waiver exam or equivalents; or permission of the instructor.

Interpretation and presentation of data with business applications using spreadsheets and statistical software packages, including appropriate use of AI-enabled features for analysis support and communication. Multiple regression, sampling techniques, design and analysis of surveys, analysis of variance, experimental design, and contingency tables. (Plus-minus letter grade only)

DS 314 Project Management Tools (Units: 3)

Prerequisites: DS 212/ECON 212 or MATH 124 or equivalent or permission of the instructor.

Develop the analytical tools and critical thinking framework to be able to define, plan, execute, and deliver projects in a professional organization, consistent with the standards of the Project Management Institute (PMI), the largest professional organization for project management. Master techniques to determine project schedules, resources, and budgetary needs, including calculating the likelihood of on-time completion and using cost-effective measures to mitigate project risk. Determine recommendations and communicate these appropriately to stakeholders. Develop effective AI prompting techniques. (Plus-minus letter grade only).

DS 408 Simulation Modeling for Business (Units: 3)

Prerequisites: DS 212/ECON 212 or MATH 124; ISYS 263 or passing the waiver exam; or Bachelor of Science in Statistics majors with MATH 440; or equivalents; or permission of the instructor.

Development of computer-based simulation modeling skills for analyzing complex business problems. Learn to translate real-world processes into simulation models using professional software, with emphasis on model formulation, logical flow design, verification and validation, and analysis of stochastic inputs and outputs. Methods include Monte Carlo and discrete-event simulation. Applications focus on evaluating system performance, comparing alternatives, and supporting managerial decision-making under uncertainty. Introduction to selected applications of artificial intelligence in simulation. (Plus-minus letter grade only)

DS 411 Decision Modeling for Business (Units: 3)

Prerequisites: DS 110 and DS 212/ECON 212*; ISYS 263* or passing the waiver exam in basic computer proficiency and information systems; all with grades of C- or better.

Basic concepts of spreadsheet modeling and risk analysis with applications to practical business decision-making. Topics include cost and demand modeling, risk analysis, revenue (yield) management, and implementation of decision models using spreadsheets. (This class cannot be taken after DS 601 and is not applicable to the DS major or minor.)

DS 412 Operations Management (Units: 3)

Prerequisites: DS 110* or MATH 108* or MATH 110* or MATH 226*; DS 212/ECON 212* or MATH 124*; ISYS 263 or basic spreadsheet skills are recommended; or Bachelor of Science in Statistics majors with MATH 440*.

Management of manufacturing and service operations. Use of computer-based models. Forecasting, capacity planning, linear programming, inventory management, quality management, and project management.

DS 412SI Supplemental Instruction: Operations Management (Unit: 1)

Prerequisite: Concurrent enrollment in DS 412.

Student-centered discussion and problem-solving designed to promote understanding of key concepts and enhance student success in DS 412. May be repeated for a total of 3 units. Activity. (ABC/NC grading, CR/NC allowed)

DS 601 Applied Management Science and Intelligent Decision Making (Units: 3)

Prerequisites: DS 110 or MATH 108 or MATH 110; either DS 212/ECON 212 or MATH 124; ISYS 263 or passing the waiver exam; or equivalents; or permission of the instructor.

Develop a foundation in data-driven decision making through optimization modeling in a spreadsheet environment, including model formulation, solution interpretation, and sensitivity analysis. Applications are drawn primarily from finance, marketing, and operations. Use AI-assisted tools to help structure decision problems, generate model components, and interpret solution output while developing skills to critically evaluate AI-generated results. Final course projects require the collection, modeling, and analysis of real-world data. (Plus-minus letter grade only)

DS 604 Business Forecasting (Units: 3)

Prerequisites: DS 212/ECON 212 or MATH 124; ISYS 263 or passing the waiver exam; or Bachelor of Science in Statistics majors with MATH 440; or equivalents; or permission of the instructor.

Business forecasting methods for managerial decision-making using statistical and artificial intelligence techniques. Concepts of time series analysis, regression modeling, and predictive analytics for sales, revenue, demand, and financial planning. Discussion of classical forecasting approaches and machine learning methods for improving accuracy and automation. (Plus-minus letter grade only)

DS 612 Data Mining with Business Applications (Units: 3)

Prerequisites: DS 212/ECON 212 or equivalent; ISYS 263 or passing the waiver exam in basic computer proficiency and information systems. DS 311 and/or DS 312 recommended.

Concepts of modeling and understanding of complex datasets based on advanced statistical methods. Discussion of various supervised and unsupervised machine learning techniques. Instruction in the use of statistical software such as R, SAS, Stata, Python, etc. (Plus-minus letter grade only)

DS 624 Quality Management (Units: 3)

Prerequisites: DS 212/ECON 212 or MATH 124; ISYS 263 or passing the waiver exam; or Bachelor of Science in Statistics majors with MATH 440; or equivalents; or permission of the instructor.

Introduction to quality management principles used to drive operational excellence across industries. Explore frameworks including Total Quality Management, Lean, Six Sigma, and the DMAIC (Define, Measure, Analyze, Improve, Control) methodology as structured approaches to continuous improvement. Emphasis on process design, performance measurement, root cause analysis, risk-based thinking, and corrective and preventive action (CAPA), aligning quality systems with organizational strategy and customer value. Discussion of the role of AI-enabled tools in supporting process analysis and decision-making, and vice versa. Course content aligns with the key competencies for Lean Six Sigma Green Belt certification. (Plus-minus letter grade only)

DS 655 Supply Chain Analytics & Logistics (Units: 3)

Prerequisite: DS 212/ECON 212 or MATH 124 or equivalent or permission of the instructor.

Management of supply chain and logistics systems using optimization and data-driven decision models. Topics include supply network design, transportation modeling, inventory management, and aggregate planning. Develop spreadsheet-based optimization models. Introduction to Python and AI-assisted tools for structured scenario analysis and decision support. Emphasis on managerial interpretation of quantitative results and evaluation of trade-offs among cost, service performance, resilience, and sustainability in global supply chains. Supports preparation for the APICS (now ASCM) certification framework. (Plus-minus letter grade only)

DS 660GW Business Analytics: Communication and Professional Practice - GWAR (Units: 3)

Prerequisites: GE Area 1A/A2; DS 412; two upper-division quantitative courses (excluding FIN 350 and ISYS 363), one of which may be taken concurrently.

Capstone course in Decision Sciences emphasizing professional communication and writing in Business Analytics. Examine how to engage stakeholders across the project lifecycle to elicit requirements, frame problems, and define meaningful metrics, translating organizational needs into quantitative models. Focus on problem formulation, performance measurement, and computer-based analytical tools in areas such as supply chain, operations management, quality, ethics, and sustainability. Develop professional workplace competencies and explore the responsible and effective use of AI in analytics-driven, data-informed decision environments. (ABC/NC grading only)

Course Attributes:

• Graduation Writing Assessment

DS 699 Independent Study (Units: 1-3)

Prerequisite: Permission of the instructor, adviser, and department chair. Open only to upper-division students who have demonstrated the ability to do independent work.

Intensive problem analysis under the direction of a decision sciences faculty member.

DS 776 Data Analysis for Managers (Units: 3)

Prerequisite: Restricted to graduate Business students; graduate students in other programs permitted with the permission of the Faculty Director of Graduate Programs.

Spreadsheet-based statistical tools to support decision-making in operations, finance, and marketing. Graphical and descriptive tools for data analysis, correlation, regression, estimation, probability distributions, and hypothesis testing. (Plus-minus letter grade only) [Formerly BUS 776]

DS 786 Operations Analysis (Units: 3)

Prerequisites: Restricted to graduate Business students; DS 776; graduate students in other programs permitted with the permission of the Faculty Director of Graduate Programs.

Production management and control with related computer applications: production and distribution planning, inventory control, and demand forecasting. Quantitative analysis. (Plus-minus letter grade only) [Formerly BUS 786]

DS 816 Forecasting for Managerial Decision Making (Units: 3)

Prerequisites: Restricted to graduate Business students; DS 786; or permission of the instructor; graduate students in other majors admitted with the permission of the Faculty Director of Graduate Programs.

Business forecasting methods for managerial decision-making using statistical, artificial intelligence, and machine learning techniques. Covers time series modeling, regression-based forecasting, model diagnostics, validation, and forecast accuracy assessment. Emphasizes model selection, parameter estimation, and integrating AI-driven approaches to improve predictive performance and automation. Evaluate trade-offs among model complexity, interpretability, and strategic business impact in sales, revenue, demand, and financial planning applications. (Plus-minus letter grade only)

DS 852 Optimization Modeling for Managers (Units: 3)

Prerequisites: Restricted to graduate Business students; DS 786; graduate students in other majors admitted with the permission of the Faculty Director of Graduate Programs.

Develop a foundation in data-driven decision making through optimization modeling in a spreadsheet environment, including model formulation, solution interpretation, and sensitivity analysis for complex business settings. Applications are drawn primarily from finance, marketing, and operations. Use AI-assisted tools to help structure decision problems, generate model components, and interpret solution output while developing skills to critically evaluate AI-generated results. Final course projects require the collection, modeling, and analysis of real-world data. (Plus-minus letter grade only)

DS 853 Applied Statistical Analysis for Business (Units: 3)

Prerequisites: Restricted to graduate Business students; DS 776; graduate Business Analytics students with concurrent enrollment in ISYS 812; or permission of the instructor; graduate students in other majors admitted with the permission of the Faculty Director of Graduate Programs.

Application of multivariate analysis methods to business data sets. Methods covered include simple and multiple regression, logistic regression modeling, and time series analysis. Spreadsheet-based analysis is used extensively throughout the semester, alongside an introduction to programming languages (e.g., Python or R) and the use of AI to explore and clean data sets, generate code for multivariate models, and interpret output coefficients. Final course projects require the collection, analysis, and modeling of real data. (Plus-minus letter grade only)

DS 855 Supply Chain Management (Units: 3)

Prerequisites: Restricted to graduate Business students; DS 786; or permission of the instructor; graduate students in other majors admitted with the permission of the Faculty Director of Graduate Programs.

Supply chain design, planning, and operation. Concepts of competitive strategy and sustainability; aggregate planning and management of the marketing/operations interface; inventory management and procurement strategy; design of supply chain networks; the role of information technology. (Plus-minus letter grade only)

DS 856 Practical Project Management (Units: 3)

Prerequisites: Restricted to graduate Business students; DS 786; or permission of the instructor; graduate students in other majors admitted with the permission of the Faculty Director of Graduate Programs.

Develop the analytical tools and critical thinking framework to be able to define, plan, execute, and deliver projects in a professional organization, consistent with the standards of the Project Management Institute (PMI), the largest professional organization for project management. Master techniques to determine project schedules, resource, and budgetary needs, including calculating the likelihood of on-time completion and using cost-effective measures to mitigate project risk. Determine recommendations and communicate these appropriately to stakeholders. Learn the concepts and techniques behind these tools and develop effective AI prompting techniques. (Plus-Minus letter grade only).

DS 861 Data Mining and Advanced Statistical Methods for Business Analysts (Units: 3)

Prerequisites: Restricted to graduate business students; DS 853* and ISYS 812*; graduate students in other programs admitted with the permission of the Faculty Director of Graduate Programs.

Focus on concepts of modeling and understanding of complex datasets based on advanced statistical methods with various supervised and unsupervised learning techniques. Includes an overview of relevant algorithms while emphasizing business applications of these tools and statistical software commonly used in practice, such as R, Python, SAS, Stata, etc. (Plus-minus letter grade)

DS 862 Machine Learning for Business Analysts (Units: 3)

Prerequisites: Restricted to graduate Business students; DS 861*; graduate students in other programs permitted with the permission of the Faculty Director of Graduate Programs.

Focus on advanced machine learning methods including supervised and unsupervised learning techniques used to extract valuable information from quantitative and text data. Includes an overview of relevant algorithms while emphasizing business applications of the tools with a focus on commonly-used statistical software, e.g., R and Python, and how to apply the techniques learned in class. (Plus-minus letter grade)

DS 899 Independent Study (Units: 1-3)

Prerequisites: Restricted to graduate Business students; permission of the instructor, adviser, and department chair; open only to graduate students who demonstrate the ability to work independently.

Intensive study of a particular problem under the direction of a business analysis faculty member. (Plus-minus letter grade only)