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Curriculum – MSBA

The Master of Science in Business Analytics curriculum blends technical skills, such as R and SQL, analytic frameworks for interpreting data, and business acumen for translating insights into actions.

The future of business leadership relies on data-driven decision-making. The MSBA curriculum is designed to allow students to gain the business knowledge and analytical skills necessary to develop effective strategies and inform decisions. From operations and finance to programming and modeling, to machine learning and artificial intelligence, this is an integrated, comprehensive curriculum for business intelligence.

Highlights

Data Analytics Skills

Cutting-edge data analytics skills, including Probability and Statistics, Programming, Data Management and Visualization, and Machine Learning.

Business Principles

Overview of key management principles from each facet of an enterprise, including Finance, Accounting, Marketing, and Operations.

Leadership Essentials

Business leadership essentials that build a solid foundation for effective data-based decision making, including Competitive Strategy, Leadership, and Building High Performing Teams.

Practical Experience

Practical experience through a hands-on capstone project with companies, and guest lectures from practicing experts in the field.

Courses

The integrated and comprehensive MSBA curriculum is designed for students to gain the business knowledge and analytical skills necessary to develop effective strategies and recommendations for business intelligence. Find out how each course in our 46-credit curriculum aims to help you become an effective Business Analyst.

Expand your technical skillset

BUS AN 507: Spreadsheet Modeling (2 credits)
BUS AN 510: Probability and Statistics (3 credits)
BUS AN 511: Programming Essentials (2 credits)
BUS AN 512: Data Management & SQL (2 credits)
BUS AN 579: Special Topics – Data Visualization & Storytelling (2 credits)

Learn to think analytically

BUS AN 513: Customer Analytics (3 credits)
BUS AN 514: Analytics for Firm Decisions (3 credits)
BUS AN 515: Digital Marketing (3 credits)
BUS AN 516: Operations Research Data Analytics (3 credits)
BUS AN 517: Machine Learning Methods and AI (3 credits)
BUS AN 579: Special Topics – Pricing Analytics (3 credits)

Advance your leadership, teamwork, and communication skills

BUS AN 502: Leadership and Professional Development (2 credits)
BUS AN 550: Business Analytics’ Leader Series (3 credits)
BUS AN 599: Business Analytics’ Practicum (2 credits)

Gain a strong understanding of business

BUS AN 500: Finance and Accounting (3 credits)
BUS AN 503: Competitive Strategy (2 credits)
BUS AN 504: Marketing Fundamentals (3 credits)
BUS AN 506: Operations and Supply Chain Management (3 credits)
BUS AN 600: Independent Study

Course Title Details
Business Analytics’ Leader Series The Leader Series features industry speakers and guest lectures on a variety of business analytics topics. Alumni and other business and data analytics leaders share perspectives on former projects and careers in the field.
Competitive Strategy This course focuses on students’ ability to think as practicing executives, provide practice in analyzing, evaluating, and modifying organizations’ strategies while considering the changing conditions in the economic, technological, political/legal and social environments, and operate as a generalist in a corporate setting. Broadly, the course will focus on the context within which the decision maker operates, the pressures of performance, and both the personal and professional limitations of the individual executive as he or she tries to manage effectively.
Data Management & SQL This course will introduce students to SQL and the basic elements of data management and visualization.
Probability and Statistics This course reviews the uses of statistical tools to present, analyze, and interpret data and emphasizes applications of statistical tools and their uses for organizational decision-making, not the theoretical bases of statistical tools. Students will develop data and analysis skills to apply in other courses, work experiences, and life experiences.
Programming Essentials This course provides essentials necessary for business analytics professional in terms of software and programing skills. Students will learn how to use software such as R, Python and others to 1) Manage and prepare data and 2) Implement models to analyze data.
Spreadsheet Modeling This courses focuses on the basics of spreadsheet modeling. Spreadsheets and simple models implemented in spreadsheets are the initial step in modeling data. The course covers typical spreadsheet modeling problems and equips students with the tool necessary to start business analytics projects.
Course Title Details
Business Analytics’ Leader Series The Leader Series features industry speakers and guest lectures on a variety of business analytics topics. Alumni and other business and data analytics leaders share perspectives on former projects and careers in the field.
Customer Analytics This course focuses on the firm’s interaction with its customers and how data can be used to improve these interactions. Targeting and personalization are the core concepts of modern customer-centric marketing. This course will provide students with the tools and methods that will allow leveraging data to help shape customer relationships.
Finance and Accounting This course provides a broad introduction to the use of financial information to make decisions within an organization. The course will cover topics that are most useful to managers who are (or will be) in business analytics roles.
Marketing Fundamentals This course provides a broad introduction to marketing decisions within an organization. Marketing is an organizational philosophy and a set of guiding principles for interfacing with customers, competitors, collaborators, and the environment. The course will cover topics that are most useful to managers who are (or will be) in business analytics roles.
Operations Research Data Analytics This course focuses on business analytics problems and techniques from an operations research perspective. Many firms in a variety of industries use these techniques and these techniques are applicable to the many functional areas of business, such as operations, marketing, accounting, finance, etc.
Course Title Details
Business Analytics’ Leader Series The Leader Series features industry speakers and guest lectures on a variety of business analytics topics. Alumni and other business and data analytics leaders share perspectives on former projects and careers in the field.
Digital Marketing The course is designed to help students understand the digital marketing landscape using quantitative methods. The goal of the course is to introduce some of the core concepts of digital marketing, and to use a quantitative approach to develop optimal marketing strategies. This course equips students with a solid analytical foundation to evaluate digital opportunities, marketing strategies, and online business models.
Machine Learning Methods and AI This course will introduce a theoretical and practical understanding of core artificial intelligence and machine learning concepts and techniques; and provide hands-on experience in applying these techniques to practical real-word business problems. The course will cover general concepts and techniques including inductive learning, knowledge representation, reinforcement learning, recommendation systems, artificial neural networks, and natural language processing.
Operations and Supply Chain Management This course provides a broad introduction to Operations Management (OM), the design and management of the processes that transform inputs into finished goods or services. The objective of this course is to provide students with a solid foundation in the models and principles that are necessary to generate improvement ideas.
Special Topics in Business Analytics – Data Visualization and Storytelling A critical skill has emerged in this era of data: the ability to make uncover key insights in data, and convincingly present findings to an audience. Managers and executives use data to understand their environment, set and track progress against goals, and communicate with a wide variety of stakeholders, from employees to customers to shareholders. To that end, this course covers the important principles of data visualization and data storytelling.
Course Title Details
Analytics for Firm Decisions This course focuses on how firms can use data analytics to optimize their marketing mix decisions or the 4 Ps – Product Design, Pricing, Promotion and Advertising, and Placement. In the process, students will also gain expertise in methodologies for developing statistical models for descriptive, causal, and predictive models for large-scale data.
Business Analytics’ Practicum Provides the capstone learning experience. Provides students the opportunity to complement their in-class learning experience with practical experience through a team-based industry analytics project for a company.
Leadership and Professional Development This year-long* hybrid course focuses on building and working in highly effective teams and the importance of leadership in the role of a business analytics professional. It covers the various approaches that can be pursued to accelerate development and the proven behaviors that effective team members and leaders exhibit. With contemporary models as the organizing framework, the course reviews practical tools with guided self-reflection to assess students’ skills and develops a plan for students’ development.
*credits applied in spring quarter.
Special Topics in Business Analytics – Pricing Analytics This course blends marketing analytic frameworks, marketing strategy & microeconomic theory, and data to formulate actionable pricing strategies. Students will learn how to coordinate pricing decisions with the rest of the marketing value proposition. Numerous pricing structures are developed in the course, along with their microeconomic foundations. Students will learn the underlying theory for each pricing structure, along with the practical considerations for implementation.