Principles of probability methodology. Application for providing structure to uncertainty. Develop, implement, and use probability models.

Survey of forecasting and time series methods. Models for stationary and nonstationary time series; ARIMA model identification, estimation, and forecast development. Seasonal and dynamic models.

Introduction to data-warehousing, multidimensional database, on-line analytical processing, and survey of business intelligence applications that extract useful information from data warehouses. Business applications emphasized.

Developing a strategic perspective on emerging digital innovations shaping consumer-oriented businesses. Topics include artificial intelligence, autonomous vehicles, augmented/virtual reality, post-screen usability and cybersecurity.

Application of Monte Carlo simulation to determine a range of outcomes for all possible courses of action. Application of Excel simulation.

Application of decision analysis, simulation and optimization techniques to managerial problems. Learn how to create and present useful spreadsheet models to analyze practical business models.

Managing Business models in digital platform ecosystems; designing new products and services for digital platforms; establishing digital platform leadership; assessing emerging niches in digital spaces.

Acquire, analyze, visualize and perform natural language processing (NLP) on text data. Apply Python, machine learning packages, statistical methodology and computer code to business decision-making

Fraud detection model systems; identify normal vs. outlying behavior; malicious adversaries; complex datasets; supervised and unsupervised fraud statistical models; measures of model efficacy.

How companies can implement ‘big data’ initiatives to improve business activities. How leading companies have successfully implemented ‘big data’ initiatives and why some have failed.

Applications of systems theory and concepts, matrix organizational structures, PERT/CPM project modeling, and management information systems to the management of complex and critical projects.

Independent research beyond normal course offerings. Proposal, research and written report/paper required.

Selected topics reflecting current trends and recent developments in operations management, information systems, and decision support systems. [Courses offered on an trial basis prior to conversion into permanent courses.]

This course will introduce popular AI and deep learning tools from machine learning for modern business applications. Topics include neural networks, their basic structures, learning and tuning these networks, convolutional neural networks, recurrent neural networks, generative deep learning techniques (such as GAN and autoencoders). Emphasis on business applications in areas such as finance, text, health care, and brain images.

The global game industry now generates over $100 billion in revenue annually, making it bigger than both the movie and recorded music industries. As the game industry grows and tracking improves, the field of game data analytics becomes more important. his class helps graduate students  understand and analyze the basics, challenges, and opportunities of game data analytics.

The healthcare industry is changing rapidly due to technological changes, regulatory changes, demographic shifts, and changes in consumer expectations. This class helps graduate students  understand and analyze the basics, challenges, and opportunities of healthcare analytics.

HR and People analytics covers reporting standards, metrics that turn data into predictive intelligence and ROI with advanced visualization, storytelling, and real-world case studies.

This course is designed to examine how sports performance measurements, data collection, statistical models are implemented, interpreted, and presented in the pro sports industry.

Analytics for supply chain planning. Topics include data-driven decision making, solving real-world problems, utilizing scalable technology, and current industry best practices.

Build, test and implement the types of models in use by quantitative asset managers

Introduction to the fundamentals of pricing and pricing strategy. Develop a conceptual framework and a set of analytical tools used to make sound pricing decisions.

Applications and models of marketing-related data analyses to the development of data-driven marketing strategies and making data-driven marketing decisions.

Application, development, interpretation and implementation (in Excel and Tableau) of marketing metrics using case studies, data, and practitioner talks

Data mining and machine learning algorithms for analyzing very large data sets. Emphasis on Map Reduce. Case studies.

The practice of User Experience Design and Strategy principles for the creation of unique and compelling digital products and services.