Elective Courses

Masters of Science in Business Analytics

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Elective Courses


All of our MS Business Analytics students take 13.5 units of elective courses. Please click on a course title to read a description of each course.

DSO 516: Probability and Data Modeling

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

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DSO 522: Applied Time Series Analysis for Forecasting

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.

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DSO 528: Data Warehousing, Business Intelligence and Data Mining

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.

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DSO 531: Digital Foundations for Business Innovation

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.

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DSO 536: Monte Carlo Simulation and Decision Models

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

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DSO 547: Designing Spreadsheet-Based Business Models

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.

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DSO 556: Business Models for Digital Platforms

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.

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DSO 560: Text Analytics and Natural Language Processing

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

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    DSO 562: Fraud Analytics

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

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    DSO 574: Using Big Data: Challenges and Opportunities

    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.

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    DSO 580: Project Management

    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.

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    DSO 593: Independent Research in Data Sciences or Operations

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

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    DSO 599: Special Topics (Overview)

    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.]

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    DSO 599: Artificial Intelligence for Business (Special Topics)

    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.

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    DSO 599: Game Data Analytics (Special Topics)

    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.

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    DSO 599: Healthcare Analytics (Special Topics)

    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.

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    DSO 599 : HR & People Analytics (Special Topics)

    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

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    DSO 599: Sports Analytics (Special Topics)

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


     

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    DSO 599: Supply Chain Analytics (Special Topics)

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

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    FBE 599: Quantitative Investing (Special Topics)

    New course offered by the Finance department covering quantitative investing

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    INF 520: Foundations of Information Security

    Threats to information systems; technical and procedural approaches to threat mitigation; secure system design and development; mechanisms for building secure security services; risk management.

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    INF 553: Foundations and Applications of Data Mining

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

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    INF 556: User Experience Design and Strategy

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

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    MKT 536: Pricing Strategies

    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.

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    MKT 566: Marketing Analytics

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

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    MKT 567: Marketing Metrics for Business Decisions

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

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