Business Analytics Curriculum

Home
Graduate Studies
Master of Science in Business Analytics
Elevate your career with advanced business and leadership skills.

100% Online    |   35 Credits    |    2 Years    |    $650 per Credit 

The business analytics program includes theory, foundational knowledge, and practical application courses for real-world skills. The schedule is designed to meet the needs of busy, working professionals with accelerated courses and interim breaks.

Courses are determined based on students’ academic background, experience level, and career goals, in alignment with program requirements.

Interested in taking an elective course from the program? Complete a short admission form to take individual graduate courses from the program as a non-degree-seeking student.

All courses are taught by business analytics faculty.

View Course Catalog

Fall One

  • 3 credits
  • This course focuses on both the technical and visual aspects of inspecting and presenting data. Technical topics will include importing data from various sources, establishing relationships between data tables, transforming data (including pivoting and unpivoting), filtering, sorting, and aggregation. Visuals will be designed to focus attention on what the data is saying, with a special focus on visuals that respond dynamically to user manipulations (e.g., filtering). Emphasis will be placed on the design/refinement cycle for visualizations, with peer review playing a prominent role.
  • 3 credits
  • An introduction to the theory and practice of quantitative modeling and optimization, with applications to computer simulation and business resource management. Possible topics include linear and nonlinear programming, network analysis, game theory, deterministic and probabilistic models. Prerequisites: Instructor's consent.

Spring One

  • 3 credits
  • Students will learn specialized applications of operations research to problems arising from business. These will include data envelope analysis, transportation and transshipment problems, goal programming, network models (including PERT-CPM), and capital budgeting. Other topics such as inventory models, facility location problems, etc. will be covered as time and student interest permit. Special attention will be paid to the development and analysis of models for realistic medium- to large-scale problems. Prerequisite: MATH-635
  • 3 credits
  • In this course, students will gain a solid understanding of supply chain and risk management principles, including effective ways to identify, mitigate, and measure the impact of potential supply chain disruptions, leading to informed and effective supply chain decisions. Topics in the course include supply chain design, strategies, integration, visualization, analytics, endogenous and exogenous risk, and mitigation methods.

Summer One

  • 3 credits
  • Courses covering various topics of interest in a particular discipline are offered regularly. Topics addressed are related to recent and current events, skills, knowledge, and/or attitudes and behaviors pertinent and relevant to student's professional development.
  • 3 credits
  • This course allows the student to understand and demonstrate knowledge of descriptive and inferential statistics used in research, and apply their knowledge to real-world situations and research questions. Emphasis is placed on distinguishing similarities and differences among statistical tests, and recognizing the essentiality of statistics for producing and comprehending scientific research.

Fall Two

  • 3 credits
  • This course is designed to provide a comprehensive view of the nature and practice of leadership. Among the topics explored are historical, philosophical and theoretical foundations; ethics and values; power and influence; conflict management; and effective leadership in formal organizations.
  • 3 credits
  • Forecasting is the science of predicting future events and outcomes. In this course students will learn how to effectively use both data and theory to create forecasts and how to quantify and communicate uncertainty in forecasts. Topics include random walks, Markov models, time series analysis, Bayesian methods and qualitative forecasting.
  • 3 credits
  • This course explores advanced concepts and theories related to leadership with emphasis on contemporary topics of leadership and factors that guide leader behavior. Students will examine classic and current scholarship to bridge between theory and practice. The course focuses on critical thinking about leadership.
  • 3 credits
  • The purpose of the capstone course is to provide students with a culminating, integrative curricular experience. The capstone project consolidates and exemplifies students’ knowledge across content areas within the business analytics program. Students will focus on in-depth case analyses relative to their own professional development. (The capstone project is designed to be taken toward the end of the business analytics program. It isn’t required that all other courses be completed, but students should have completed more than 50% of the coursework before taking the capstone experience.) Prerequisites: Instructor’s consent

Spring Two

  • 3 credits
  • This course focuses on the development and analysis of cost information used by management decision makers to evaluate and improve company performance. It includes product cost analysis, profitability planning, performance analysis and emerging cost strategies.
  • 3 credits
  • Data mining is the study of discovering and assessing patterns, relationships and information within large data sets. This course provides an introduction to data mining with an emphasis on predictive modeling techniques and machine learning algorithms. Examples and applications will be drawn from various disciplines.
  • 3 credits
  • This course provides an overview of corporate financial management. Students will develop the skills needed for the analysis of investment and financing decisions in a corporation and how these decisions affect firm valuation. Topics covered include working capital management, risk and return, capital structure, mergers and acquisitions, discounted cash flow analysis, the capital asset pricing model, and the cost of capital.
  • 3 credits
  • This course provides students with an introduction to the construction and analysis of least-squares models, including multiple regression, ANOVA, ANCOVA, and mixed models. Generalized linear models will also be presented, with special attention paid to logistic regression and log-linear models.
  • 3 credits
  • The purpose of the capstone course is to provide students with a culminating, integrative curricular experience. The capstone project consolidates and exemplifies students’ knowledge across content areas within the business analytics program. Students will focus on in-depth case analyses relative to their own professional development. (The capstone project is designed to be taken toward the end of the business analytics program. It isn’t required that all other courses be completed, but students should have completed more than 50% of the coursework before taking the capstone experience.)