MS in Innovation & Entrepreneurship, Technology Concentration

Addresses the specifics of new product and service development and fostering innovation and technology to increase performance. Topics include generating and screening initial ideas; assessing user needs and interests; forecasting results; launching, and improving products and programs; and overall bringing innovation from ideation to commercialization.  [ 4 cr. ]

Examines various approaches to developing high tech innovation based economies as a route to self sufficiency and growth. Factors studied include both structural reforms in the political, legal and economic areas, and government sponsored initiatives in higher education, basic research, private venture capital, grants to support new product development by promising ventures, and the creation of science and technology parks and incubators. Students independently research, write, and present studies of the strategies of various countries. This will be augmented by case studies, reading, and guest speakers on strategies being employed in such countries as Taiwan, Thailand, and Brazil.  [ 4 cr. ]

Graduate Prerequisites: MET AD741 - Includes opportunity assessment and feasibility analysis, concept development, budgeting and financial operations, financial and human resource management, legal and organizational issues analysis, and comprehension and determination of the roles of boards and external advisors, all leading into the planning and operating of new ventures.  [ 4 cr. ]

Examines legal issues that affect high technology firms. Topics include copyright, reverse engineering, trade secrets, patents, international legal differences, the Uniform Commercial Code, and product liability. Cases drawn from high tech industries are used to emphasize current and future developments.   [ 4 cr. ]

Prerequisite: AD100 Pre-Analytics Laboratory This course presents fundamental knowledge and skills for applying business analytics to managerial decision-making in corporate environments. Topics include descriptive analytics (techniques for categorizing, characterizing, consolidating, and classifying data for conversion into useful information for the purposes of understanding and analyzing business performance), predictive analytics (techniques for detection of hidden patterns in large quantities of data to segment and group data into coherent sets in order to predict behavior and trends), prescriptive analytics (techniques for identification of best alternatives for maximizing or minimizing business objectives). Students will learn how to use data effectively to drive rapid, precise, and profitable analytics-based decisions. The framework of using interlinked data inputs, analytics models, and decision-support tools will be applied within a proprietary business analytics shell and demonstrated with examples from different functional areas of the enterprise. R, SQL, and Power BI software are used in this course.  [ 4 cr. ]

Prerequisite: AD571 Business Analytics Foundations. - Python is a modern, high-level programming language. One of the most popular programming languages, its use has steadily increased across a large number of industries. This course introduces students to the Python environment and teaches a solid foundation in the basic syntax and structure. Structured Query Language (SQL) is the most common language globally for interacting with relational databases. Employers have indicated that knowledge of SQL is one of the most important skills for new graduates entering the workforce. Even with advances in database technologies and languages for handling heterogeneous data types, SQL remains the core skill for interacting with data. This course introduces both languages to equip students pursuing an analytics education with the skills necessary to succeed in the analytics and data visualization field. The outcome of this course will be a focused survey of Python and SQL topics designed to equip analytics professionals rather than a deep focus on technical programming topics.  [ 4 cr. ]

Provides analysis of the economics of innovation and the means by which firms secure the necessary capital to begin or expand operations. The students learn different procedures for raising venture capital through investment institutions and individuals.  [ 4 cr. ]

Cryptocurrencies and the underlying distributed ledger technology (blockchain), have exploded into public consciousness over the last few years, with many industry practitioners arguing that the blockchain technology has the potential to disrupt business and financial services in the way the Internet disrupted off-line commerce. This course covers digital currencies, blockchains, and related topics in the FinTech area using the analytical tools provided by economics, investments and corporate finance.  [ 4 cr. ]

This course presents financial algorithms used in applications of computer science in financial decision analysis, risk management, data mining and market analysis, and other modern business processes. The course covers theoretical background on probabilistic methods used for financial decision making and their application in number of fields such as financial modeling, venture capital decision making, operational risk measurement and investment science. Number of financial applications and algorithms are being presented for portfolio risk analysis, modeling real options, venture capital decision making, etc. The course concludes with algorithms for financial risk assessment and presents the security concepts and challenges of financial information systems.  [ 4 cr. ]

This course expands upon the foundations of finance theory with interdisciplinary approaches from statistical physics and machine learning. The course equips the students with the Python tools to tackle a broad range of problems in quantitative financial analysis and combines the study of relevant financial concepts with computational implementations. Students will learn to use packages like Numpy, Pandas, Statsmodels and Scikit, which are commonly used in research and in the industry. Prerequisites: MET AD 685 or PY 355 or equivalent or consent by the instructor.  [ 4 cr. ]

Prerequisite: MET AD 571. - The course offers an overview of the key current and emerging enterprise risk analytical approaches used by corporations and governmental institutions and is focused on understanding and implementing the enterprise risk management framework on how to leverage the opportunities around a firm to increase firm value. The major risk categories of the enterprise risk management such as financial risk, strategic risk, and operational risk will be discussed and risk analytics approaches for each of these risks will be covered. Students will learn how to use interlinked data inputs, analytics models, business statistics, optimization techniques, simulation, and decision-support tools. An integrated enterprise risk analytics approach will be demonstrated with examples from different functional areas of the enterprise. Python, R, SQL, and Power BI software are used in this course.  [ 4 cr. ]

Finance is a highly competitive and dynamic industry that demands quantitative oriented professionals. This course will equip students with the empirical techniques which are used in the analysis of financial markets with a strong focus on financial applications using actual data.

The goal of this course is to provide students with a number of econometric techniques which are used in the analysis of financial markets based on asset pricing and corporate finance models. In particular, the emphasis will be on classical linear regression models, time series analysis, and limited dependent variable models applied to the following topics: predictability of asset returns; event study analysis; econometric tests of the CAPM and multifactor models; volatility modelling, etc.   [ 4 cr. ]

Prerequisites: AD571. - Explore web analytics, text mining, web mining, and practical application domains. The web analytics part of the course studies the metrics of websites, their content, user behavior, and reporting. The Google Analytics tool is used for the collection of website data and doing the analysis. The text mining module covers the analysis of text including content extraction, string matching, clustering, classification, and recommendation systems. The web mining module presents how web crawlers process and index the content of websites, how search works, and how results are ranked. Application areas mining the social web and game metrics will be extensively investigated. R, SQL, and Power BI software are used in this course.  [ 4 cr. ]

Prerequisite: MET AD571 Generative AI is transforming industries by automating tasks, generating content, and assisting in decision-making. This hands-on course explores how business professionals, analysts, and managers can apply Generative AI to solve real-world business challenges. The course emphasizes practical AI applications, covering topics like prompt engineering, AI-driven workflow automation, AI-powered data analytics, and responsible AI practices. Students will gain experience using Jupyter Notebooks, VS Code, SQL, and GitHub to integrate AI into their workflows.  [ 4 cr. ]

Prerequisites: AD571 Enterprises, organizations, and individuals are creating, collecting, and using a massive amount of structured and unstructured data with the goal of converting the information into knowledge, improving the quality and the efficiency of their decision-making process, and better positioning themselves in the highly competitive marketplace. Data mining is the process of finding, extracting, visualizing, and reporting useful information and insights from both small and large datasets with the help of sophisticated data analysis methods. It is part of business analytics, which refers to the process of leveraging different forms of analytical techniques to achieve desired business outcomes through requiring business relevancy, actionable insight, performance management, and value management. The students in this course will study the fundamental principles and techniques of data mining. They will learn how to apply advanced models and software applications for data mining. Finally, students will learn how to examine the overall business process of an organization or a project with the goal of understanding (i) the business context where hidden internal and external value is to be identified and captured, and (ii) exactly what the selected data mining method does. Python, R, SQL, and Power BI software are used in this course.  [ 4 cr. ]

Prerequisites: MET CS 544, or MET CS 555 or equivalent knowledge, or instructor's consent. - The Web Mining and Graph Analytics course covers the areas of web mining, machine learning fundamentals, text mining, clustering, and graph analytics. This includes learning fundamentals of machine learning algorithms, how to evaluate algorithm performance, feature engineering, content extraction, sentiment analysis, distance metrics, fundamentals of clustering algorithms, how to evaluate clustering performance, and fundamentals of graph analysis algorithms, link analysis and community detection based on graphs. Laboratory Course.  [ 4 cr. ]

Prerequisites: Six completed program courses. An extensive research project culminating in a written paper and oral defense. Research is conducted under intensive faculty supervision. Requires department approval and thesis supervisor from full-time faculty.  [ Var cr. ]

Prerequisites: MET AD 800. The second course of an extensive research project culminating in a written paper and oral defense. Research is conducted under intensive faculty supervision. Requires department approval and thesis supervisor from full-time faculty.  [ Var cr. ]