Master of Science in Business Analytics Curriculum

Statistical Thinking for Business Decision-Making (3 Credit Hours)

Decision-making situations involve varying levels of uncertainty. This course introduces methods for modeling and informing decision-making under uncertainty. Topics include probability theory, probability distributions, confidence intervals, hypothesis testing, analysis of variance, and linear regression with an emphasis on practical applications in business contexts and spreadsheet modeling.

Career Management (1.5 Credit Hours)

You have made an important decision to pursue an MS in Business Analytics (MSBA) to position yourself for a career in the field. Overall, the marketplace is becoming increasingly competitive, and while the MSBA will offer a significant differentiator, your ultimate success in obtaining a job that aligns with your career aspirations is heavily dependent on the critical steps you take toward career planning and preparation. Understanding career paths in Business Analytics and developing lifelong career management skills are the objectives of this class.

Python Programming for Business Analytics (3.0 Credit Hours)

Due to its power, simplicity, and open-source availability, the Python programming language has become one of the leading data analytics languages across a wide variety of organizations and industries. This course focuses on developing a strong foundation in Python programming for business analytics applications. Students will learn essential programming concepts and techniques including variables, data structures, loops, functions, and automation. Additionally, they will explore the use of Python libraries for data analysis, manipulation, and visualization.

Fall Semester

Starting in late August, the Fall semester consists of a combination of semester-long courses and two mini modules that examine the relationship among data, business analytics, and organizational performance in greater depth.

Data Management for Business Analytics (3 Credit Hours)

As a result of our interconnected digital ecosystem, businesses are inundated with vast streams and quantities of data. As data volumes grow and cloud services become the backbone of data infrastructure, there’s a pressing need for professionals who understand the nuances of managing in this new data landscape. This hands-on course delves into contemporary techniques and tools for effective data management in today’s digital ecosystem. Topics include SQL, relational databases, database design, cloud services, APIs, data integration, and big data systems, providing students with practical skills for managing and analyzing large volumes of data.

Machine Learning for Business Analytics (3.0 Credit Hours)

Machine learning is at the forefront of modern technology and powers many applications and systems that we interact with daily, such as recommendation systems, virtual assistants, and autonomous vehicles, to name a few. This course introduces predictive modeling techniques with a focus on the implementing machine learning algorithms for business analytics applications. Topics include linear and logistic regression, classification trees, clustering, random forest, gradient boosting, KNN, collaborative filtering, recommendation engines, neural networks, and explainable machine learning, equipping students with essential skills for leveraging data-driven insights.

Data-Driven Storytelling for Impact (3.0 Credit Hours)

Becoming a successful analytics practitioner who can drive impact within an organization requires the ability to effectively communicate with all levels of the organization. This course focuses on leveraging data visualization and storytelling techniques to effectively communicate analytical insights and drive impact within organizations. Students will learn best practices for data storytelling and visualization, enabling them to present complex analyses in a compelling and influential manner.

Decision Modeling (3.0 Credit Hours)

Decision Modeling focuses on the application of disciplined, well-structured, and logical problem-solving processes to support decision-making. Such processes include correctly defining and framing the business problem/opportunity, applying appropriate analytical tools and methodologies to model the situation, and utilizing the models to gain keen insights to support decisions that make a positive impact to organizations and society. Topics addressed include advanced spreadsheet modeling, optimization, decision theory, and Monte Carlo simulation, providing students with practical skills for solving complex business problems.

Introduction to Business Analytics Practicum: Assessing Performance and Managing Projects (1.5 Credit Hours)

Beyond being proficient in business analytics methodologies, success as a business analytics professional requires an understanding of how business analytics supports the broader mission of the organization and its strategy. This course focuses on the important relationships between data, business analytics, and organizational performance. Topics include project management, teamwork dynamics, and aligning analytics initiatives with organizational goals, preparing students for real-world business analytics projects.

Business Analytics Practicum I: Mess to Model (1.5 Credit Hours)

This is the first of a three-course sequence to provide a team-based experience with a real-world project using analytics. In this course, student teams collaborate with corporate partners to address a real-world business problem and define the project scope and objectives. Working with a faculty mentor, students identify data requirements and analytical techniques necessary to address the identified business challenges.

Spring Semester

Beginning in early January, this semester is divided into two separate modules, during which students continue to develop models for forecasting, optimization, and yielding actionable intelligence and explore multi-industry applications of data analysis. Students also finish the three-course team-based practicum and present their findings.

Analytics in Society (1.5 Credit Hours)

The increased focus on data, data collection, and analytics requires managers to form a deep understanding of the policy and ethical implications of their use of analytics for making decisions. This course explores the ethical, legal, and policy implications of using analytics in decision-making. Topics include ethics in big data, privacy concerns, security issues, and regulatory compliance, enabling students to navigate ethical challenges and promote responsible use of analytics and AI in organizations.

Applications of Machine Learning (1.5 Credit Hours)

This course introduces the student to several of the most commonly used machine learning methods with an emphasis on applying the methods to solve real-world problems.

Operations and Supply Chain Analytics (1.5 Credit Hours)

It is widely accepted that the environment confronting organizations today is becoming increasingly competitive as the length of product life cycles continue to decrease, the marketplace becomes increasingly global, and the pace of technological change accelerates. As a result, most, if not all, organizations are seeking ways to continuously improve their business processes, to produce and deliver their products/services more efficiently, and to meet their customer needs more effectively in their pursuit of attaining a sustainable competitive advantage. This course examines the application of business analytics methodologies to improve operations and business processes and design effective supply chains. Topics include discrete event simulation for process analysis and improvement, inventory management, supply chain network design and optimization, enabling students to enhance efficiency and meet customer needs effectively.

Marketing Analytics (3 Credit Hours)

Marketing analytics allows businesses to make data-driven decisions by providing insights into customer behavior, preferences, and trends. By analyzing data from various sources such as website traffic, social media engagement, and sales transactions, organizations can identify effective marketing strategies and allocate resources more efficiently. This course explores the collection, analysis, and interpretation of marketing data to drive data-driven marketing strategies. Topics include market segmentation, perceptual mapping, A/B testing, web analytics, and online advertising, with a focus on leveraging digital data to inform marketing decisions.

Financial Analytics and Risk Management (3 Credit Hours)

This course covers financial modeling and how to measure and manage risk through descriptive statistics, probability theory, optimization, regression analysis, decision trees, and simulation analysis. Topics covered include time value of money, bond and stock valuation, portfolio theory, financial statement analysis, capital budgeting analysis, and real option valuation, providing students with essential skills for identifying and managing financial risks effectively.

Business Analytics Practicum II: Model to Insight (1.5 Credit Hours)

This is the second of a three-course sequence to provide team-based experience with a real-world project using analytics. Building on the previous practicum course, the focus shifts to modeling and analysis to derive insights from data. Student teams work closely with their corporate partner and faculty mentors to apply analytical techniques and develop solutions that address the identified business problems effectively.

Business Analytics Practicum III: Insight to Impact (1.5 Credit Hours)

In this final course in the practicum sequence, student teams focus on communicating and implementing insights derived from their analyses. Working with corporate partners and faculty mentors, students develop strategies for implementing recommendations and driving positive impact within organizations.

Note: Program and curriculum are subject to minor changes.