Graduate Business Analytics Course Descriptions

DS-501. Comm. for Data Science Practitioners. 0.00 Credits.

Communication for Data Science Practitioners is intended to provide support and tailored instruction specific to multilingual graduate students in the Data Science program who speak a language other than English as a first language (L1). The course is designed to provide an intensive and focused hybrid experience for students that will effectively prepare students for discipline-specific graduate coursework delivered in English. DS-501 offers direct English-language vocabulary and advanced grammar instruction, but combines ESOL course content with a deep focus on explicitly preparing students for the tasks they must complete as both graduate students and practitioners in their field. Coursework is steeped in a content & language integrated learning approach, and the course is meant to be paired with DS-520. DS-501 is a hybrid course, with both virtual and in-person course meetings. The course is designed as a 0-credit experience, does not contribute towards visa eligibility, and is delivered as a supportive add-on for multilingual learners at the graduate level. This course is graded on a pass/fail basis, but student grades will appear on their transcripts.

DS-510. Introduction to Data Science. 3.00 Credits.

Data Science is a set of fundamental principles that guide the extraction of valuable information and knowledge from data. This course provides an overview and develops student's understanding of the data science and analytics landscape in the context of business examples and other emerging fields. It also provides students with an understanding of the most common methods used in data science. Topics covered include introduction to predictive modeling, data visualization, probability distributions, Bayes' theorem, statistical inference, clustering analysis, decision analytic thinking, data and business strategy, cloud storage and big data analytics.

DS-520. Data Analysis and Decision Modeling. 3.00 Credits.

This course will provide students with an understanding of common statistical techniques and methods used to analyze data in business. Topics covered include probability, sampling, estimation, hypothesis testing, linear regression, multivariate regression, logistic regression, analysis of variance, categorical data analysis, Bootstrap, permutation tests and nonparametric statistics. Students will learn to apply statistical techniques to the processing and interpretation of data from various industries and disciplines.

DS-530. Data Management Systems. 3.00 Credits.

This course explores foundational concepts of relational databases, data warehousing, distributed data management, structured and unstructured data, NoSQL data stores and graph databases. Various database concepts are discussed including Extract-Transform-Load, cloud-based online analytical processing (OLAP), data warehouse architecture, development and planning, physical database design, data pipelines, metadata, data provenance, trust and reuse. Students will develop practical experience using SQL. Prerequisites: DS-510 AND DS-520.

DS-533. Enterprise Design Thinking. 3.00 Credits.

Students will learn a robust framework for applying design thinking techniques to key issues facing organizations across industries. Key skills developed include shared goal setting and decision-making, processes for continuous innovation, and the alignment of multi-disciplinary teams around the real needs and experiences of users and customers. Through instruction, experiential learning and an industry-recognized methodology, students will gain practice in the successful application of design thinking techniques to address common business problems.

DS-540. Statistical Programming. 3.00 Credits.

The course gives an introduction to SAS or R programming for statistical analyses and managing, analyzing and visualizing data. Topics include numeric and non-numeric values, arithmetic and assignment operations, arrays and data frames, special values, classes and coercion. Students will learn to write functions, read/write files, use exceptions, measure execution times, perform sampling and confidence analyses, plot a linear regression. Students will explore tools for statistical simulation, large data analysis and data visualization, including interactive 3D plots.

DS-542. Python in Data Science. 3.00 Credits.

The course gives an introduction to Python programming for statistical analyses and managing, analyzing and visualizing data. Topics include numeric and non-numeric values, arithmetic and assignment operations, arrays and data frames, special values, classes and coercion. Students will learn to write functions, read/write files, use exceptions, measure execution times, perform sampling and confidence analyses, plot a linear regression. Students will explore tools for statistical simulation, large data analysis and data visualization, including interactive 3D plots. Prerequisites: DS-510, DS-520.

DS-560. Biomedical Data Analytics. 3.00 Credits.

An introduction to the biology of modern genomics and some of the tools that are used to measure it. This will include basic molecular biology, the genome, DNA and RNA sequences, and the central dogma. Students will learn techniques to analyze data from sequencing experiments. The course covers data analytic techniques to understand and analyze the biomedical data available to bioscientists and the medical profession. Prerequisites: CS-241, BI-183.

DS-570. Healthcare Data Analytics. 3.00 Credits.

An introduction to the healthcare environment and the various sources of healthcare data. How to import, clean, and refine data from these sources. Students will learn the techniques to diagnose diseases, predict prognosis and evaluate treatments. The course covers data analytic techniques to understand and analyze healthcare data. Prerequisites: CS-241, BI-183.

DS-589. Topics in Management. 3.00 Credits.

Topics vary by term. Example topics may include but are not be limited to the following: advanced project management techniques; non-profit, philanthropic, and/or faith-based management; coding fundamentals for entrepreneurs, managers, and executives; and mindfulness in the workplace.

DS-590. Data Structures and Algorithms I. 3.00 Credits.

This course explores essential topics for programmers and data scientists including the design of and implementation and analysis of efficient algorithms and their performance. Essential data structures are also reviewed, as well as searching and sorting algorithms.

DS-596. Graduate Research Assistantship. 0.00 Credits.

Graduate Research Assistantship is a robust learning experience for pre-selected students, involving scholarly research under faculty supervision. These research projects involve the development of theoretical analyses and models, gathering and analysis of data, and special projects that require substantive research. The ultimate goals for this research is academic conference presentation, publication in peer-reviewed journals and research reports, and more broadly contributing to thought leadership of the Data Science Institute.

DS-597. Applied Research Experience. 0.00 Credits.

The Applied Research Experience is a learning experience that gives Data Science Institute students the opportunity to conduct real-world consulting and research projects with businesses and organizations, that build upon the science, theory, and application of data and analysis. This non-credit course fulfills the business experience requirement for the program for those students who do not have a current work role that fulfills the requirement. For Traditional/Full-time programs. Prerequisites: DS-510 DS-520 DS-530 DS-542 DS-600 DS-620:.

DS-598. Applied Industry Experience. 0.00 Credits.

The Applied Industry Experience course is an academic component that accompanies students' industry experience in a full time role or internship. Students whose current industry role has been approved by the Academic Program Director as directly related to their program of study can register for this non-credit course each term during which they are working. Prerequisites: DS-510 DS-520 DS-530 DS-542 DS-600 DS-620.

DS-599. Research Practicum. 0.00 Credits.

The Research Practicum is a learning experience that gives the students the opportunity to conduct real-world consulting projects with businesses that build upon the science, research and application of data and analysis, extending to strategic planning and identifying relevant tactics to carry out strategies. For Professional Hybrid programs.

DS-600. Data Mining. 3.00 Credits.

Data mining refers to a set of techniques that have been designed to efficiently find important information or knowledge in large amounts of data. This course will provide students with understanding of the industry standard data mining methodologies, and with the ability of extracting information from a data set and transforming it into an understandable structure for further use. Topics covered include decision trees, classification, predictive modeling, association analysis, statistical modeling, Bayesian classification, anomaly detection and visualization. The course will be complemented with hands-on experience of using advanced data mining software to solve realistic problems based on real-world data. Prerequisites: DS-510, DS-520.

DS-605. Financial Computing and Analytics. 3.00 Credits.

This course covers the process of collecting data from a variety of sources and preparing it to allow organizations to make data-driven decisions. It builds upon the relationships within data collected electronically and applies quantitative techniques to create predictive spreadsheet models for financial decision making. Prerequisites: DS-510, DS-520.

DS-610. Big Data Analytics. 3.00 Credits.

Big Data (Structured, semi-structured, & unstructured) refers to large datasets that are challenging to store, search, share, visualize, and analyze. Gathering and analyzing these large data sets are quickly becoming a key basis of competition. This course explores several key technologies used in acquiring, organizing, storing, and analyzing big data. Topics covered include Hadoop, unstructured data concepts (key-value), Map Reduce technology, related tools that provide SQL-like access to unstructured data: Pig and Hive, NoSQL storage solutions like HBase, Cassandra, and Oracle NoSQL and analytics for big data. A part of the course is devoted to public Cloud as a resource for big data analytics. The objective of the course is for students to gain the ability to employ the latest tools, technologies and techniques required to analyze, debug, iterate and optimize the analysis to infer actionable insights from Big Data. Prerequisites: DS-510, DS-520, DS-530.

DS-620. Data Visualization. 3.00 Credits.

Visualization concerns the graphical depiction of data and information in order to communicate its contents and reveal patterns inherent in the data. It is sometimes referred to as visual data mining, or visual analytics. Data visualization has become a rapidly evolving science. This course explores the underlying theory and practical concepts in creating visual representations of large amounts of data. Topics covered include data representation, information visualization, real-time visualization, visualization toolkits including Tableau and their applications to diverse data rich contexts. At the end of the course, the student will be able to present meaningful information in the most compelling and consumable fashion. Prerequisites: DS-510, DS-520.

DS-621. Business Analytics With Power BI. 3.00 Credits.

This course will focus on building dynamic dashboard and applications in order to understand and interpret the data by using PowerBI. Course will also focus on visualization and business intelligence techniques to interpret the data as step towards Machine Learning. Prerequisites: DS-510 DS-520. Prerequisites: DS-510, DS-520.

DS-630. Machine Learning. 3.00 Credits.

Machine learning is the field of study that gives computers the ability to learn from experience without being explicitly programmed. This course covers the theory and practical algorithms for machine learning from a variety of perspectives. Topics include decision tree learning, parametric and non-parametric learning, Support Vector Machines, statistical learning methods, unsupervised learning, reinforcement learning and the Bootstrap method. Students will have an opportunity to experiment with machine learning techniques and apply them to solve a selected problem in the context of a term project. The course will also draw from numerous case studies and applications, so that students learn how to apply learning algorithms to build machine intelligence. Prerequisites: DS-510, DS-520, DS-530, DS-542.

DS-631. Deep Learning Algorithms. 3.00 Credits.

Machine learning is the science (and art) of programming computers so they learn from data. It is the field of study that gives computers the ability to learn from experience without being explicitly programmed. This course covers the theory and practical algorithms for neural networks and deep learning. Major topics neural networks, convolutional neural networks, recurrent neural networks, reinforcement learning, and implementation of deep learning in TensorFlow. Students will have an opportunity to experiment with advanced machine learning techniques (especially using Python) and apply them to solve selected problems in the context of a term project. Prerequisites: DS-630.

DS-637. Luster Analysis With Machine Learning. 3.00 Credits.

In this course, students will utilize machine learning techniques to generate business intelligence through the discovery of patterns and relationships in data. In particular, students will apply cluster analysis, or clustering this method of unsupervised learning and technique for statistical data analysis groups objects based on characteristics, such as high intra-cluster or low inter-cluster similarities. Pre-requisites: DS-542 and DS-630 Prerequisites: DS-542 DS-630.

DS-640. Predictive Analytic & Financial Modeling. 3.00 Credits.

Predictive analytics is an area of data mining that deals with extracting information from data and using it to predict trends and behavior patterns. This course will provide predictive analytics foundational theory and methodologies as well as teach students how to build predictive models for practical financial and business applications and verify model effectiveness. Topics covered are linear modeling and regression, nonlinear modeling, time series analysis and forecasting, segmentation and tree models, support vector machine, clustering, neural networks and association rules. Prerequisites: DS-510, DS-520.

DS-642. Advance Python in Data Science. 3.00 Credits.

This course explores essential advanced Python topics for programmers & data scientists including working with databases using Python, writing web services, exploring unit-testing frameworks, understanding multithreading concepts in Python, performing advanced statistical analysis using Python libraries and learning industry standards for writing and organizing large Python programs. Prerequisites: DS-510, DS-520, DS-542.

DS-650. Data Ethics and Artificial Intelligence. 3.00 Credits.

The increasing use of big data and artificial intelligence in our society raises legal and ethical questions. This course explores the issues of privacy, data protection, non-discrimination, equality of opportunities and due process in the context of data-rich environments. It analyzes ethical and intellectual property issues related to data analytics with the use of artificial intelligence. Students will also learn the legal obligations in collecting, sharing and using data, as well as the impact of algorithmic profiling, industrial personalization and government. This course also provides an understanding of the important capabilities of business with the technologies that enable them and the management of artificial intelligence. Prerequisites: DS-510, DS-520. Prerequisites: DS-510, DS-520.

DS-660. Business Analytics. 3.00 Credits.

Business analytics is the process of generating and delivering the information acquired that enables and supports an improved and timely decision process. The aim of this course is to provide the student with an understanding of a broad range of decision analysis techniques and tools and facilitate the application of these methodologies to analyze real-world business problems and arrive at a rational solution. Topics covered include foundations of business analytics, descriptive analytics, predictive analytics, prescriptive analytics, and the use of computer software for statistical applications. The course work will provide case studies in Business Analytics and present real applications of business analytics. Students will work in groups to develop analytic solutions to these problems. Prerequisites: DS-510, DS-520 OR MS-500:.

DS-665. Advanced Machine Learning. 3.00 Credits.

Machine learning is the science (and art) of programming computers so they learn from data. It is the field of study that gives computers the ability to learn from experience without being explicitly programmed. This course covers the theory and practical algorithms for neural networks and deep learning. Major topics neural networks, convolutional neural networks, recurrent neural networks, reinforcement learning, and implementation of deep learning in TensorFlow. Students will have an opportunity to experiment with advanced machine learning techniques (especially using Python) and apply them to solve selected problems in the context of a term project. Prerequisites: DS-510, DS-520 AND DS-630.

DS-670. Capstone: Big Data & Data Science. 3.00 Credits.

This course is structured as a capstone research practicum where students have an opportunity to apply the knowledge acquired in data science to interdisciplinary problems from a variety of industry sectors. Students work in teams to define and carry out an analytics project from data collection, processing and modeling to designing the best method for solving the problem. The problems and datasets used in this practicum will be selected from real world industry or government settings. At the end of the class students will write a report that presents their project, the approach and techniques used to design a solution, followed by results and conclusion. Students are encouraged to present their capstone research at conferences. Prerequisites: DS-620, DS-630; Course Type(s): Capstone.

DS-671. Capstone: Business Analytics. 3.00 Credits.

This course is structured as a capstone research practicum where students have an opportunity to apply the knowledge acquired in data science to interdisciplinary problems from a variety of industry sectors. Students work in teams to define and carry out an analytics project from data collection, processing and modeling to designing the best method for solving the problem. The problems and datasets used in this practicum will be selected from real world industry or government settings. At the end of the class students will write a report that presents their project, the approach and techniques used to design a solution, followed by results and conclusion. Students are encouraged to present their capstone research at conferences. Prerequisites: DS-520, DS-620 Prerequisites: DS-520, DS-620; Course Type(s): Capstone.

DS-680. Marketing Analytics & Operation Research. 3.00 Credits.

Organizations need to interpret data about consumer choices, their browsing and buying patterns and to match supply with demand in various business settings. This course examines the best practices for using data to prescribe more effective business strategies. Topics covered include marketing resource allocation, metrics for measuring brand assets, customer lifetime value, and using data analytics to evaluate and optimize marketing campaigns. Students learn how data is used to describe, explain, and predict customer behavior, and meet customer needs. Students also learn to model future demand uncertainties, predict the outcomes of competing policy choices and take optimal operation decisions in high and low risk scenarios. Prerequisites: DS-510, DS-520.

DS-684. Data Engineering Using Cloud Computing. 3.00 Credits.

This course presents the fundamentals of cloud computing with a focus on data and analytics. Students will gain insights on how to analyze large datasets in the cloud using Microsoft Azure platform, from basic cloud tools to the big data distributed technologies like Spark, SQL and Python. With the exponential growth in data, organizations rely on the robust computing, storage, and analytical power of Azure, AWS and other cloud tools to scale, stream, predict, create visualizations and make data informed decisions. Course topics include: overview of cloud computing, cloud systems, parallel processing in the cloud, distributed storage systems, data visualization and creating dashboards. Prerequisites: DS-542.

DS-687. Artificial Intelligence Fundamentals. 3.00 Credits.

This comprehensive course provides an introduction to Artificial Intelligence concepts. At the end of this class students will be able to describe what is AI, its applications, use cases, and how it is transforming our lives. Students will be able to explain and understand how the terms like machine learning, deep learning, and neural networks work. Hands on experience will be practiced with IBM Watson platform by using computer vision techniques and develop custom image classification models and deploy them to the Cloud. The class will also tackle the UpToDate topics of ethical concerns surrounding AI. Prerequisites: DS-510, DS-520.

DS-688. Natural Language Processing With Ai. 3.00 Credits.

This course explores the fundamental concepts of NLP and its role in current and emerging technologies. Students will gain a thorough understanding of modern neural network algorithms for the processing of linguistic information. By mastering cutting-edge approaches, they will gain the skills to move from word representation and syntactic processing to designing and implementing complex deep learning models and other language understanding tasks. Prerequisites: DS-510, DS-520, DS-530, DS-542.

DS-690. Data Science and Health. 3.00 Credits.

Students will be introduced to the types of data commonly used in public health, biomedical and clinical settings. Students will acquire the knowledge and skills to use these data for understanding and improving the quality of health outcomes. Through lectures and class data analysis projects, students will explore, analyze and create graphical visualization of data from a variety of healthcare sources. Students will also be exposed to selective topics on real time analytics, clinical informatics, and machine learning for biomedical applications. Prerequisites: DS-510, DS-520.

DS-698. Exploring Industry & Technology Overseas. 3.00 Credits.

This travel course is tailored specifically for students in Data Science, Business Analytics, or MBA Business Analytics. Through instruction, industry visits, and cultural excursions students will gain a comprehensive knowledge of data-driven decision-making processes and business analytics practices within Germany and Belgium. Course Type(s): International (Travel).

DS-700. Independent Study in Data Science. 3.00 Credits.

In this course, students will work with a faculty member to explore a topic in depth or conduct independent research. Requirements for completion include submission of a research report. Course Type(s): Independent Study.

DS-702. Practicum in Data Science. 3.00 Credits.

Practicum is a learning experience that gives the students the opportunity to conduct real-world consulting projects with businesses that build upon the science, research and application of data and analysis, extending to strategic planning and identifying relevant tactics to carry out strategies. Prerequisites: DS-630, DS-631.

DS-703. Practicum in Statistics. 3.00 Credits.

Practicum is a teaching experience for doctoral students that gives the students the opportunity to conduct real-world consulting projects with businesses that build upon the large datasets, by working on statistical correlations while practicing teaching. Prerequisites: DS-520, DS-600.

DS-770. Topics in Data Science. 3.00 Credits.

Students will explore emerging, innovative, alternative and/or advanced subject matter in the field of data science. Topics vary by term.

DS-780. Practicum in Teaching Data Science. 3.00 Credits.

Recognizing that teaching data science at the college level requires more than just subject matter expertise, students in this course will devise, implement, assess, revise and reevaluate undergraduate and/or graduate data science lessons. Pre-service professors will develop and present student-centered lessons that engage classroom or virtual learners interactively and collaboratively by utilizing appropriate teaching and learning techniques and technologies. Classroom coaching and constructive feedback from mentors and peers will help students improve their teaching. Current instructors in data science and/or related disciplines are encouraged to enroll for professional development purposes.

DS-790. Practicum in Teaching Statistics. 3.00 Credits.

Recognizing that teaching statistical analysis and probability at the college level requires more than just subject matter expertise, students in this course will devise, implement, assess, revise and reevaluate undergraduate and/or graduate statistics lessons. Pre-service professors will develop and present student-centered lessons that engage classroom or virtual learners interactively and collaboratively by utilizing appropriate teaching and learning techniques and technologies. Classroom coaching and constructive feedback from mentors and peers will help students improve their teaching. Current instructors in statistics and/or related disciplines are encouraged to enroll for professional development purposes.

DS-800. Forecasting Methods Business Decisions. 3.00 Credits.

This course will prepare leaders for different forecasting methods and analytical tool to get them prepared for the business decisions. Forecasting methods will be evaluated according to the conditions such as under uncertainty, under risk and so on. Prerequisites: DS-801.

DS-801. Advanced Data Structures & Algorithms. 3.00 Credits.

This course explores core data structures and algorithms used in everyday applications, the trade-offs involved with choosing each data structure, along with traversal, retrieval, and update algorithms. It will be covered linked lists, stacks, queues, binary trees, and hash tables. Prerequisites: DS-630.

DS-802. Natural Language Processing. 3.00 Credits.

Students will explore the fundamental concepts of NLP and its role in current and emerging technologies. Students will develop a comprehensive working knowledge of modern neural network algorithms in order to process of linguistic information. By mastering cutting-edge approaches, students will gain the skills to advance from word representation and syntactic processing to designing and implementing complex deep learning models and other language understanding tasks. Prerequisites: DS-510 AND DS-520.

DS-803. Optimization Computational Lin. Algebra. 3.00 Credits.

In this course, students will learn about the theory and practical aspects of many fundamental tools from matrix computations, numerical linear algebra and optimization. In addition to classical applications, most examples will particularly focus on modern large-scale machine learning problems. Implementations will be done using MATLAB/Python. Prerequisites: DS-510 AND DS-520.

DS-804. Advanced Optimization. 3.00 Credits.

The course covers mathematical programming and combinatorial optimization from the perspective of convex optimization, which is a central tool for solving large-scale problems. The course is dedicated to the theory of convex optimization and its direct applications. Besides, it focuses on advanced techniques in combinatorial optimization. Prerequisites: DS-803.

DS-805. Research Seminar in Forecasting. 3.00 Credits.

In a research seminar format, students and faculty develop research proposals, analyses, and reporting in the domain of Forecasting. Topics of special interest vary from term to term. Prerequisites: DS-510, DS-520.

DS-806. Research Seminar in Unstructured Data. 3.00 Credits.

In a research seminar format, students will work with faculty to develop research proposals, perform analyses, and create reports, culminating in presentations. Topics will emphasize Unstructured Data analysis, and may vary by term. Prerequisites: DS-510, DS-520.

DS-871. Development and Initiation. 4.00 Credits.

This course is the first in a series of four courses designed to guide students through the process of conducting a data science research project and writing a dissertation. In this course, students will focus on laying the foundation for their research by developing Chapters 1 and 2 of their dissertation. They will learn about the essential elements of a research proposal, including problem formulation, dataset research (if needed), literature review, research questions, and hypotheses. Additionally, students will begin collecting and analyzing data related to their research topic. Emphasis will be placed on individual student work with their Mentor and Dissertation Committee members. Prerequisites: DS-801, DS-802, DS-803, DS-804, DS-805, DS-806.

DS-872. IRB Approval and Data Collection. 4.00 Credits.

Dissertation Seminar 2 is the second part of a four course series designed to guide students through the process of conducting a data science research project and writing a dissertation. In this course, students will delve into the critical aspects of obtaining Institutional Review Board (IRB) approval for their research and initiating the data collection process. They will gain a comprehensive understanding of ethical considerations, data collection methods, and data management. Emphasis will be placed on individual student work with their Mentor and Dissertation Committee members. Prerequisites: DS-871.

DS-873. Data Analysis and Interpretation. 4.00 Credits.

Dissertation Seminar III is the third part of a four-course series designed to guide students through the process of conducting a data science research project and writing a dissertation. In this course, students will focus on the critical phases of data analysis, interpretation, and drawing meaningful conclusions from their research data. They will learn various data analysis techniques, visualization methods, and how to effectively communicate their findings. Prerequisites: DS-872.

DS-874. Finalization and Dissertation Defense. 4.00 Credits.

Dissertation Seminar IV is the final part of a four-course series designed to guide students through the process of conducting a data science research project and writing a dissertation. In this course, students will focus on finalizing their dissertation, including editing and polishing, preparing for the defense, and taking the necessary steps to successfully complete their doctoral journey. Students must maintain continuous enrollment in this course until they have successfully completed and defended their dissertation. Students must have their dissertation proposal approved by the Doctoral Committee for Research Involving Human Subjects prior to registering for this course. Prerequisites: DS-873.

GB-500. Executive Communication. 3.00 Credits.

Mastery of effective written communication is essential for success in the business world. In this course, students will learn to analyze and produce texts in a variety of formats and genres based on their particular professional goals.

GB-503. Statistics for Managers. 3.00 Credits.

This course covers concepts of probability and statistics needed by managers to analyze and interpret numerical data in uncertain environments. It includes hypothesis testing, regression and correlation analysis and analysis of variance. Concepts are discussed in a framework of real world applications.

GB-505. Internet of Things for Managers. 3.00 Credits.

Students will learn how to extract real-world data from sensors in device, integrate them to services in the cloud, and gather valuable insights to improve business operations and enable innovative industry business models, using analytics and artificial intelligence.

GB-511. Management & Human Behavior. 3.00 Credits.

This course covers planning, organizing, staffing, directing, and the management of change in a modern organization. It examines decision making and problem solving in pursuit of organizational goals. It addresses human behavior in the areas of motivation, communication, and interpersonal relations.

GB-513. Marketing Management. 3.00 Credits.

This course examines the field of marketing and the dynamics of matching goods and services with customer and consumer needs. Topics include strategic planning, marketing research, and buyer behavior of businesses and consumers. The course covers the marketing functions of product mix and branding, price determination, channels of distribution and promotion and advertising.

GB-517. Business Ethics and Sustainability. 3.00 Credits.

This course provides a framework for students to recognize ethical dilemmas and analyze the business implications in terms of consequences, autonomy, rights, virtues and equality. Extensive use is made case studies and current events using presentation, discussion and debate delivery methods.

GB-519. Real Estate Legal Environment. 3.00 Credits.

This course covers the fundamentals of legal issues in real estate finance and development from through a managerial lens. This course is a component of the MBA in Real Estate and develops skills in legal concepts in a real estate setting. Topics that are included in the course are land acquisition, finance; choice of entity; tax aspects; management (leasing, environmental); disposition of real property (sale of mortgaged property, foreclosures, wraparound mortgages, sale-leasebacks), and recent legal developments.

GB-520. Nonprofit Management. 3.00 Credits.

This course will provide an introduction to some of the special management and leadership issues facing nonprofit organizations. Ethical challenges within the nonprofit sector will also be explored.

GB-530. Corporate Finance. 3.00 Credits.

A study of the problems associated with the financial management of business organizations. Topics include the analysis of types of firms and markets, review of accounting, time value of money, valuation, and short-term financing.

GB-533. Enterprise Design Thinking. 3.00 Credits.

Students will learn a robust framework for applying design thinking techniques to key issues facing organizations across industries. Key skills developed include shared goal setting and decision-making, processes for continuous innovation, and the alignment of multi-disciplinary teams around the real needs and experiences of users and customers. Through instruction, experiential learning and an industry-recognized methodology, students will gain practice in the successful application of design thinking techniques to address common business problems.

GB-535. International Finance. 3.00 Credits.

Analysis of the international financial decisions of multinational corporations. Topics to be covered include foreign exchange rates and the structure of foreign capital markets. Particular emphasis is placed on management decisions in an international environment including cash flows, capital budgeting, valuation, and the optimal capital structure for international operations. Prerequisites: GB-530.

GB-539. Financial Management in Nonprofit Sector. 3.00 Credits.

As current or prospective leaders, managers and staff of nonprofit organizations, students will gain basic knowledge about nonprofit financial reports, the ability to read and interpret the IRS 990 form, and an overview of how philanthropy and financial management interconnect. Students will also learn best practices for applying nonprofit accounting procedures and principles accurately to maintain compliance with state and federal regulations. Learners will gain practical skills in financial management and financial sustainability strategies.

GB-541. Blockchain for Managers. 3.00 Credits.

Students will learn how to help organizations lead the way into the adoption of Blockchain, identify industry areas for Blockchain applications and apply smart contracts using open source leading Blockchain technologies.

GB-554. Strategic Marketing: Nonprofit Sector. 3.00 Credits.

Throughout the analysis of case studies and the development of comprehensive strategic marketing plans, students will identify and apply a number of principles regarding nonprofit marketing, including brand awareness, donor retention and engagement, in a manner anticipated to generate revenue growth for an organization or social enterprise.

GB-555. Personal Branding. 3.00 Credits.

This course is designed to help graduate students evaluate and improve their skill sets to establish themselves as a brand. Learn the personal branding process to create a portfolio that exploits social media, blog/websites, video resumes, networking, etc.

GB-560. Data Science for Managers. 3.00 Credits.

Students will use advanced data science methods and tools, leveraging statistical sciences, machine learning technologies and industry-specific datasets, to learn how to implement unique data models that can solve challenging problems across all industries.

GB-565. Derivative Markets. 3.00 Credits.

An examination of derivative securities, market structures, and various valuation models. The course includes discussion of spot and future markets, the valuation of futures and options, investment strategies, portfolio insurance, and recent developments in futures and options markets. Prerequisites: GB-530(8454).

GB-567. Introduction to Project Management. 3.00 Credits.

Students will enumerate and utilize best practices and current process guidelines in project management within a variety of corporate contexts and industries, in order to achieve organizational objectives through budgeting, planning, marketing, financial forecasting, staffing and human relations, as well as other aspects of management science at the project and/or enterprise levels. While doing so, students will incorporate contemporary developments in global and virtual project management.

GB-570. Investment Analysis. 3.00 Credits.

An investigation of various financial instruments - including treasury securities, corporate bonds, stocks, options, and futures - as vehicles for effective investment decisions. Selected topics include: portfolio analysis, efficient markets, and analytical techniques for determining the value of specific financial instruments. Prerequisites: GB-530.

GB-576. Project Portfolio Tools & Technology. 3.00 Credits.

Students will refine their practical, theoretical and technical competencies in project management consistent with industry best practices, focusing on the intricacies of managing projects within a contemporary competitive environment in order to deliver tangible business outcomes. They will do so by utilizing project organization, stakeholder analysis, communication planning, risk and issue management, quality management, procurement, and project leadership they will also frame their project management endeavors within the broader context of business execution, which includes program and portfolio management, organizational change, strategic business planning and implementation, as well as the operation of a project management office. Prerequisites: GB-567 OR AC-567.

GB-580. Artificial Intelligence for Managers. 3.00 Credits.

This course explores the topics, technology, and skills required for the successful development and implementation of Artificial Intelligence in today's business landscape. Students will explore methodologies used in analyzing the data interpreted by AI and effectively adapting the analysis into business requirements.

GB-581. AI Leadership and Ethics. 3.00 Credits.

In this course, students will reconcile the competing concepts of democracy, legitimacy, and transparency from the vantage point of artificial intelligence, investigating how AI can contribute to disparities in resources, opportunities, and authority across the business landscape and within various organizational contexts, sometimes perpetuating historical injustices and power imbalances. Students will distinguish between eXplainable AI (XAI) and Black box AI, as they conceptualize responsible AI and empower themselves to emerge as informed, socially responsible corporate leaders with cutting edge expertise. Students will compile a portfolio to document and describe these achievements.

GB-585. Generative AI. 3.00 Credits.

In this introductory course, students will identify and investigate generative technologies, their potential applications, and any implications and social consequences associated with their implementation. Students will experiment with AI tools, modifying assignments or tasks to incorporate AI elements in their responses. Furthermore, through engagement in online discussions, Students will refine how they utilize AI tools and techniques, while enhancing critical thinking within the context of prompt engineering.

GB-589. Topics in Management. 3.00 Credits.

Topics vary by term. Example topics may include but are not be limited to the following: advanced project management techniques; non-profit, philanthropic, and/or faith-based management; coding fundamentals for entrepreneurs, managers, and executives; and mindfulness in the workplace.

GB-590. Cloud Computing for Managers. 3.00 Credits.

Students will create disruptive cloud-based solutions that can provide unique customer experiences through the use of user-centric design practices, agile methodologies and the integration of cloud-based security, data and AI capabilities.

GB-595. Hedge Fund Management. 3.00 Credits.

This course contrasts the analytical methods of traditional fundamental analysis and quantitative investing analysis by focusing on investment management, types of investment funds such as mutual funds, ETFs, hedge funds, high frequency trading, etc. Hedge funds and hedge fund investment analysis methods are going to be analyzed in detail during this course. Prerequisites: GB-511 DS-660.

GB-596. Real Estate Practicum Capstone. 3.00 Credits.

The practicum capstone course provides a project-based hands on approach for students to experience firsthand the real estate development process from the ground up. Students will be able to use the tools and frameworks provided throughout the program curriculum to this applied experiential practicum, that puts the students in the role of decision maker and leader, as well as cover a variety of real estate product types, including office, retail, warehouse, mixed residential and specialty uses.

GB-605. Advanced AI Applications in Business. 3.00 Credits.

Through the examination of case studies and real-world examples across diverse sectors, students will catalog and critique artificial intelligence (AI) applications currently impacting the workplace. Learners will then identify opportunities and challenges associated with AI integration; they will also devise and evaluate comprehensive strategic plans for managing the practical applications of AI in the workplace. Topics include the impact of AI on organizational design, human resources, decision-making, and creativity. No particular technical background with coding or statistics is required.

GB-607. AI Apps in Marketing and Finance. 3.00 Credits.

In this course, students will analyze AI-driven applications designed to improve the customer experience and client engagement. They will optimize the potential of deep learning in order to synthesize AI-powered data analytics regarding consumer behaviors, fraud prevention and marketing efforts. Students will utilize AI applications in order to navigate the complex landscape of safeguarding consumer data; to do so, they will employ both supervised and unsupervised machine learning to enhance fraud detection and consumer protection methods.

GB-608. AI Apps in the Healthcare Industry. 3.00 Credits.

In this course, students will utilize AI-driven data in machine learning within the context of healthcare management as well as other decision-making within the healthcare field. They will optimize the potential of deep learning in order to synthesize AI-powered data analytics in order to improve patient outcomes and enhance client satisfaction within the framework of organizational needs, resources and constraints.

GB-609. AI Apps in Human Resource Management. 3.00 Credits.

In this course, students will utilize AI-driven data in machine learning within the context of human resources management and related decision-making. They will incorporate blockchain technology to help mitigate bias within this context, and will conjecture how the capabilities of machine learning as well as other existing and emerging technologies effectively impact the entire employee lifecycle and related HR functions. Students will discuss case studies that examine bias, discrimination, and injustice in the context of human resources.

GB-610. AI Apps in Sports and Entertainment;Ai Apps Sports and Entertainment. 3.00 Credits.

In this course, students will analyze and interpret algorithmic output, such as from Natural Language Processing (NLP), in order to measure social media trends, comments and sentiments across the entertainment and sports industries. Students will also utilize predictive modeling to analyze consumer behavior, including viewing habits and preferences.

GB-619. Employment Law. 3.00 Credits.

Students will review key legislation and legal cases that form the framework within the human resources management discipline. Areas covered include rights and duties of both employer and employee in the employment relationship, legislation pertaining to employment standards, employment equity, workers' compensation, health and safety acts and other related topics. Prerequisites: GB-511 OR GB-621.

GB-620. Leadership. 3.00 Credits.

Business today requires leaders who enable organizations to respond quickly and efficiently to new market opportunities, new competitors, acquisitions, shifting market demographics, new technology and changes in government regulations. Topics explored include: the basic fundamentals of leadership; various aspects of the relationship between leaders and teams, and their impact on organizations.

GB-621. Human Resources. 3.00 Credits.

This course provides an overview of the principles and philosophy of human resource management. Topics include recruiting, hiring, training, and compensating employees, creating policies and procedures to improve employee productivity, developing effective and efficient systems for management, and methods to assure legal compliance. Prerequisites: GB-511.

GB-622. Management Economics. 3.00 Credits.

This course examines the foundation concepts for how organizations allocate resources for the production, distribution, and consumption of goods and services. Economic decisions are linked to the organization, management, and strategy involved with the conduct of operations. This course focuses on how mangers can improve their understanding of the economic environment and its impact on the business firm.

GB-623. Entrepreneurship & Innovation. 3.00 Credits.

Covers skills and talents essential for a successful entrepreneur and explores the role of innovation in business ventures and strategy.

GB-624. Technology for Managers. 3.00 Credits.

This course examines the emerging role of technology and applications to support organizational business models and computer systems. It integrates data base management and planning and controlling new systems, it discusses security and other issues related to systems support for marketing, management, and financial reporting.

GB-625. International Business. 3.00 Credits.

This course provides an understanding of best practices managing business operations that cross national boundaries. It covers strategies, planning, and operations. A particular focus is the current opportunities and risks in global operations and markets. It uses projects to challenge attendees to incorporate new thought processes in decision making and problem solving in developed countries.

GB-626. Cyber Risk Management and Insurance. 3.00 Credits.

This course deals with the role of the risk manager advising on business interruption arising from failures of management information and telecommunications systems. It addresses the complexity of technology, interaction of the web and back office, and security failures. It covers the use of cyber insurance and risk transfer strategies to protect assets, people, and business operations. Course Type(s): Online Course.

GB-628. Organizational Theory. 3.00 Credits.

Organizational theory (OT) is the study of how and why organizations function and create value. The evolution of technology has increased in frequency and complexity to challenge the traditional organization by greatly changing the way employees work and the work they do. This course will examine the historical origins of OT and will explore current approaches to managing organizational processes through designed structure and culture.

GB-629. Enterprise Risk Management. 3.00 Credits.

This course covers the emerging discipline of enterprise risk management (ERM) . It starts with ERM essentials covering key components needed to manage enterprise risk and the role of a central risk function. It discusses risk identification and sharing using a high-tech electronic platform. It considers unexpected and unforeseen major crises or disaster that are virtually unpredictable. It exams new technology to visualize risk relationships and back up the view with factors that affect them and the status of activities to mitigate them.

GB-630. Strategic Risk Management. 3.00 Credits.

This course covers risks without owners in the emerging discipline of enterprise risk management (ERM) . It exams risks and opportunities that depend upon collaboration because they cross the silos of the modern bureaucracy. Discussions cover sub-culture risk, leadership risk, and life-cycle risk. In addition, the course contains risk management stories ranging from avoiding business disruptions to the future of ERM.

GB-631. Risk Management and Insurance. 3.00 Credits.

This course covers risk management from the perspective of insurable exposures that confront modern organizations. It examines decisions to retain, mitigate, or transfer exposures. Topics include property, general liability, and employer liability exposures, protecting directors and officers, and managing potential disruptions to operations. Special attention is given to the role of and expectations from brokers, broker performance, and the compensation of brokers.

GB-632. Negotiations & Conflict Resolution. 3.00 Credits.

This course presents the conceptual framework and a deep focus on business and negotiation skills and strategies, conflict resolution and relationship management to equip the student to maintain healthy business relationships. Prerequisites: GB-511.

GB-633. Executives in Residence Seminar I. 3.00 Credits.

This course brings senior executives to the classroom to exchange ideas on the goals and strategies of companies and industries. The course will identify issues related to current trends in business strategy. Candidates will work in teams to develop an understanding of critical success factors in global business strategies and create presentations. Guest executives will respond to the presentations with their own views on goals, strategies, and current business trends. This course is generally offered in the Fall.

GB-634. Executives in Residence Seminar II. 3.00 Credits.

This course brings senior executives to the classroom to exchange ideas on the goals and strategies of companies and industries. Candidates participate in the seminar and then create a presentation on the ideas and lessons learned in the interaction with executives. This course is generally offered in the Spring.

GB-637. Cluster Analysis With Machine Learning. 3.00 Credits.

In this course, students will utilize machine learning techniques to generate business intelligence through the discovery of patterns and relationships in data. In particular, students will apply cluster analysis, or clustering this method of unsupervised learning and technique for statistical data analysis groups objects based on characteristics, such as high intra-cluster or low inter-cluster similarities. Pre-requisites: DS-542 and DS-630 Prerequisites: DS-542 DS-630.

GB-638. Disaster Recovery. 3.00 Credits.

In this course students will learn how to identify cyber security vulnerabilities and implement appropriate countermeasures to mitigate risks. Techniques will be taught for creating a continuity plan and methodology for building an infrastructure that supports its effective implementation. Throughout this course, skills in disaster recovery planning will be acquired through a series of interactive workshops and case studies. Students will design and develop a disaster recovery plan. Prerequisites: CY-510 OR GB-639.

GB-639. Cyber Security and Risk Management. 3.00 Credits.

In this course we will study the concepts in cyber security design and implementation for computer systems (both hardware and software). Security architecture, organization policies, standards, procedures, and security system implementation, including diagnostic testing of databases and networks. Throughout this course, practical skills will also be acquired through a series of interactive risk assessment workshops and case studies.

GB-640. Cyber Crime Invest & Digital Forensics. 3.00 Credits.

The topics covered in this course include cyber-crime investigation, digital forensics, forensic duplication and analysis, network surveillance, intrusion detection and response, incident response, anti-forensics techniques, anonymity and pseudonymity, cyber law, computer security policies and guidelines, court report writing and presentations, and case studies. The course will include lecture and demonstrations and is designed around a virtual lab environment that provides for robust and realistic hands-on experience in working with a range of information assurance topics. Students will be assigned projects to apply information security practices and technologies to solve real-world cyber security problems.

GB-641. Marketing Strategy. 3.00 Credits.

This course equips the student with advanced marketing concepts and methods to provide and sustain customer value. Emphasis is placed on the tools managers use to analyze marketing problems and make effective decisions. Discussions include case studies, analysis of marketing models, group presentations, and computer-based models to reinforce the marketing strategies. Prerequisites: GB-513 OR GB-643.

GB-643. International Marketing. 3.00 Credits.

This course covers the process of international marketing including techniques of exporting and importing, creating foreign direct investments, licensing, franchising, partnering, and other structures. Discussions focus on cultural and economic factors that shape strategies in developed and developing consumer and business markets and strategies for successful branding, pricing, and promotion.

GB-645. Marketing Research. 3.00 Credits.

This course covers the tools and techniques used to gather information in order to identify market opportunities, monitor marketing performance and evaluate market change. Special attention is given to matching the characteristics of products and services with the needs of businesses and individual buyers. Prerequisites: GB-513.

GB-646. Crisis Communications. 3.00 Credits.

The need for effective crisis communication is a valuable asset for an organization, especially now in a 24-hour news cycle and with multiple social media outlets. The focus of the course is to identify, define and prepare students to proactively and effectively respond to crisis situations.

GB-647. Global Logistics. 3.00 Credits.

Students will investigate international movements from producing through distribution to the sale of components and finished products in order to solve problems and create solutions when managing complex supply chains. Class discussions will include planning and managing systems that create efficient and timely cross-border and cross-ocean shipments.

GB-648. Social Networking & New Media. 3.00 Credits.

This course is part class and part workshop, covering social networking and other trends that are producing complex and subtle changes in business communications. Topics include blogging, YouTube, Second Life and various social networking sites and their emerging role for private businesses, their products, and markets. Attention is paid to current trends in convergence, creativity, collaboration and community as modern media replaces earlier forms of communication and attracts more active --- and interactive ---audiences. The goal of the course is for students to familiarize themselves with various social networking theories, perspectives, sites, tools, and strategies, and to critique, consult on and create social networking plans.

GB-650. Business Analytics. 3.00 Credits.

Introduction to statistical analysis using three software packages: WATSON, Excel and Tableau; probability: distributions, expectation, variance, covariance, portfolios, central limit theorem; data summaries and descriptive statistics.

GB-651. Predictive Analytics. 3.00 Credits.

Analysis of time series data with emphasis on appropriate choice of forecasting, estimation, and testing methods to solve business problems.

GB-652. Industry Analytics. 3.00 Credits.

This course covers concepts and techniques for retrieving, exploring, visualizing, and analyzing data to develop marketing strategies, and key metrics to assess goals and return on investment. Special emphasis on market segmentation, social media and website clickstream data.

GB-653. Real Estate Valuation & Market Analysis. 3.00 Credits.

This course explores the steps and data techniques used in the valuation and market analysis process. It provides an analysis of real estate trends, market activity, sales, lending, leasing, and the research process. Additional topics include land-use studies and city planning, traffic studies, population behavior and mobility, and consumer spending and trade area. Pedagogy includes live lectures, case studies, simulations, and class discussion.

GB-654. Property Mgmt Real Estate Invest Mgmt. 3.00 Credits.

This course includes coverage on the area of commercial property management, allowing students to gain a comprehensive understanding of the principles, practices and skills needed to manage commercial office and mixed-use buildings. Among the topics covered within this course are: ownership structures and investment strategies; management plans and agreements; operating procedures; fees; personnel management; risk management and insurance; ethics; and many more. In addition, current industry trends and analysis of key issues within real estate investments will be explored. Students will have the ability to combine theory with practice regarding specific relevant topics.

GB-655. Real Estate Development. 3.00 Credits.

This course is an introduction into different phases of the real estate development process. These include conceptualization, site acquisition, planning and design, construction, financing, leasing, and marketing. In addition, you will learn about leadership, management, and control of a development team. Studying various case studies and analytical tools, you will learn how to align your development vision and decisions with best practices and current trends within the industry.

GB-657. Urban Design Zoning & Land Use. 3.00 Credits.

This course introduces key areas within planning such as housing, land use and transportation. Other topics explored include zoning, entitlements, environmental impact assessments and legal and regulatory issues. More broadly students will learn about risks and opportunities for urban planning and design and land use, that considers multiple stakeholders and an inclusive approach.

GB-661. E-Commerce Technology. 3.00 Credits.

This course provides an understanding of e-Commerce as a modern business methodology that addresses the needs of organizations, merchants, and consumers for the delivery of goods and services using information technology. The course will provide an introduction to the network and system architectures that support high volume business to consumer web sites and portals, and will provide insight into the structure of the modern web enabled storefront and its integration with "back-office" business applications.

GB-667. Disaster Recovery. 3.00 Credits.

This course covers the identification of vulnerabilities and the steps necessary to mitigate risks. It examines creating a continuity plan and building an infrastructure that supports its effective implementation. Practical skills will be acquired through interactive workshops and case study. Topics include performing a threat and impact analysis, developing strategies for systems and communications recovery, organizing an emergency team, and creating a disaster recovery plan.

GB-669. Decision Support Systems. 3.00 Credits.

A hands-on survey of various software packages to aid a manager in his/her decision making functions. Packages include enterprise resource planning, financial, administrative, report-writers, project management and scheduling, graphics, publishing and multimedia. Students will conduct an evaluation on top software products in the marketplace.

GB-671. Health Care Financing & Risk Management. 3.00 Credits.

An examination of concepts related to health care financing. Emphasis will be placed on budget preparation, cost benefit analysis, managed care and on developing an understanding of reimbursement systems.

GB-672. Current Issues & Policies in Health Care. 3.00 Credits.

This course covers political, social, and economic issues affecting health care organizations. Topics include the role of government in determining health care policy, the U.S. health care delivery system, costs and financing of health care, and social welfare gains and losses. Candidates will engage in interactive discussions of current trends and economic and social issues related to efforts to reform or revise the health care system.

GB-673. Health Care Administration. 3.00 Credits.

Management, marketing, and financing of the delivery of health care will be explored. Healthcare economics is emphasized from an administrative perspective. The examination of quality versus quantity, the allocation of resources as well as relationships and conflicts among consumers and providers of health care services. Concepts related to technology, including the Electronic Medical Record (EMR) affecting health care organizations is discussed.

GB-674. Health Care Administration II. 3.00 Credits.

An examination of quality issues and measures utilized in healthcare, human resource management in healthcare settings including physician and labor relations, recruiting, retaining and developing clinical staff, as well as medical malpractice, compliance and Medicare fraud and abuse issues.

GB-693. Credited Internship. 3.00 Credits.

GB-694. Mindfulness/Meditative Practice/ Success. 3.00 Credits.

This class will teach students the history and application of mindfulness and meditation as practiced around the world. Students will learn how the practice of mindfulness and meditation can help develop the skills necessary for success in their future as business professionals, lawyers, and leaders generally. Mindfulness directs a person's thoughts to the present which enhances one's ability to focus thought and concentration as well as to respond most appropriately and ethically to others, to cross cultural barriers and maintain deeper empathy for all. Meditation practice enhances students' abilities to become more consciously aware, feel gratitude, and in turn to have respect, kindness, and consideration for others. We will explore these concepts as celebrated in different cultures and how application of both will propel students to their greatest potential. Course Type(s): International (Travel).

GB-695. Global Business Policy. 3.00 Credits.

This course develops a comprehensive approach to problem solving and decision making. Students demonstrate a mastery of concepts as they analyze projects with a setting in a specific international environment. Develops skills in strategic planning and making decisions and recommendations in operational and financial areas.

GB-697. Global Business Cultural Experience. 3.00 Credits.

This course seeks to foster a global mind set among participants by exposing them to the business cultures and ethics of different countries. The course involves overseas travel to selected countries for students to experience at first hand the milieu of cultures that underpin global business in the 21st century. Additional travel course fee of $50. Course Type(s): International (Travel).

GB-698. Exploring Legal Concepts Overseas. 3.00 Credits.

In this course we will discuss different legal concepts as they pertain to foreign countries and as compared/contrasted to the American Legal System. Such topics may include, but are not limited to, the structure of the legal system, the origin and philosophy of law, the social and economic effects of the law in the foreign country, contemporary and controversial legal issues in the foreign country, and the impact all of these concepts may have had and/or continue to have on American Law. Additional course fee of $50. Course Type(s): International (Travel).

GB-699. Capstone in Corporate Strategy. 3.00 Credits.

This course is to be taken within the last 9 credits of the MBA Program and covers the integration of management, marketing, and finance in modern organizations. It incorporates the best practices in strategic planning and decision making in complex and changing environments. Current trends and strategies are examined in a variety of areas including ethics, social responsibility, and risk management. Additional course fee of $45. Course Type(s): Capstone.