FIN 501. Programming in Finance. 3 Credit Hours.

This course serves as an introduction to many aspects of Python programming, specifically as it applies to financial applications. Topics include data management, matrix operation, optimization, simulation, linear regression, portfolio management, time-series analysis, and textual analysis. Students will become familiar with and use Python to analyze and manipulate data and accomplish tasks with various financial topics.
Prerequisites: Students need to have a basic understanding of time value of money calculation, valuation of financial assets, and portfolio theory
Corequisites: MBA 524
Session Cycle: Every Spring Semester.

FIN 502. Fintech and Blockchain for Finance. 3 Credit Hours.

New technological innovations are fundamentally transforming the financial industry. This course introduces students to the different ways in which new technologies have led to material changes in business models, products, and customer user interface. The course will explore the application of AI, deep learning, and open APIs in various sectors of finance like payments, credits, trading and risk management. The course will then shift focus towards cutting-edge topics including blockchain, cryptofinance and smart contracts, mobile payments, and applications of blockchains. Along the way the course will focus on the market regulations, security compliance and changes in law needed in this rapidly changing business environment.
Prerequisites: MBA 524
Session Cycle: Every Spring.

FIN 503. Fintech and Digital Innovation Fund. 3 Credit Hours.

This course will focus on two main components to provide students with a deeper understanding of Fintech, Blockchain, and the investment world. The first component will explore the impact of Fintech on different parts of the financial market through the analysis of various case studies. The case studies will cover topics such as Payment, Credit and Lending, Trading, Risk management, Insurtech. In the second component, students will also act as fintech security analysts and manage a portfolio of fintech firms funded by Bryant Alumni in the industry.
Prerequisites: FIN 502
Session Cycle: Every Summer.

FIN 691. Directed Independent Study in Finance. 3 Credit Hours.

This course is designed to allow an individual academic program to be tailored to fit the unique interests of a graduate student. At the initiation of the graduate student, the faculty member and the student will develop an academic plan that is submitted to the College of Business for final approval.

GSCM 601. Corporate Social Responsibility in Global Supply Chain Management. 3 Credit Hours.

This course will focus on the strategic impact of corporate social responsibility on the global supply chain. The goals of this course are to provide students with an in-depth knowledge of the various types of supply chain events that are connected to corporate social responsibility and the strategic best practices to mitigate these events. Lectures will provide a theoretical basis and illustrate the practical application of concepts. Cases, articles from academic journals, short videos, assignments, and one exam will be utilized to reinforce the subject matter and provide a variety of learning modes.

GSCM 603. Advanced Supply Chain Integration. 3 Credit Hours.

A key challenge to successful supply chain management is coordination of activities across the supply chain. This course will provide strategies for supply chain design by identifying the appropriate level of integration and coordination to improve the long-term performance of the individual companies and the supply chain as a whole. Topics include demand forecasting, integrated business management (sales and operations planning), demand management and CPFR, demand planning, and relationship management. Hands-on learning will take place within a global supply chain management simulation.

GSCM 604. Logistics of International Trade. 3 Credit Hours.

This course provides basic preparation in transportation economics and management as well as international transport and logistics. This course provides basic knowledge of import and export requirements for making contracts, payments, insurance, managing risk, arranging transportation, dealing with customs, and international trade law and theory. The course is taught in two modules: International Transport and Logistics, and Logistics Analysis. Attention is given to how transportation pricing and tradeoffs work, shipper and carrier strategies, and logistics processes for moving goods and people internationally. Students will quantitatively develop and assess strategies for transportation and network planning, inventory decision making, facility location planning, and vehicle routing. The course objectives are based on a partial list of the exam requirements for the Certification in Transportation and Logistics (CTL) professional credential offered by America's oldest logistics profession organization, The American Society of Transportation and Logistics (AST&L).
Prerequisites: MBA526.

GSCM 691. Directed Independent Study in Global Supply Chain Management. 3 Credit Hours.

The course is designed to allow an individual academic program to be tailored to fit the unique interests of a graduate student. At the initiation of the graduate studies, the faculty member and student will develop an academic plan that is submitted to the director of the College of Busines for final approval.

GSCM ST600. Warehouse Management: Processes, Inventory and Technology. 3 Credit Hours.

This course will take a systems approach and cover various aspects of warehouse operations including basic and best practices of warehouse processes; warehouse design and layout; inventory control: and technologies utilized. Learning Outcomes 1. Understand the role of warehousing operations in the global supply chain. 2. Demonstrate an understanding of the steps in the following warehouse operations: in bound order receiving; order stocking; order picking; order packing; order loading for out bound delivery. 3. Connect warehouse operations to inventory management concepts such as order tracking, cycle counting and 4. Design warehouse layouts to facilitate effective and efficient operations for various types of inventory handled within a warehouse. 5. Understand the role of current and future technologies in warehouse operations. These technologies include robotics, drones, material handling equipment, order picking walls, RFID, and optimization software.
Session Cycle: Spring.

ISA 500. Programming Foundations for Analytics. 0 Credit Hours.

This course serves as a pre-requisite to MSDS/MABA programs for students who do not have sufficient background in programming. This course delves into the theory and pragmatics of programming with a special focus on the Python programming language. No previous experience in computer science or programming is required. You will learn basic computer programming concepts and terminologies in Python such as variables, constants, operators, expressions, conditional statements, loops, and functions. This course includes hands-on exercises to help you understand the components of Python programming while incrementally developing more significant programs, data structures and algorithms.

ISA 501. Math and Statistics Foundations for Analytics. 0 Credit Hours.

This course serves as a pre-requisite to MSDS/MABA programs for students who do not have sufficient background in math and statistics. It is for learners who have basic math skills but may not have taken algebra or pre-calculus. This course introduces the core math that data science/analytics is built upon, with no extra complexity, introducing unfamiliar ideas and math symbols one-at-a-time. Students who complete this course will master the vocabulary, notation, concepts, and algebraic rules necessary before moving on to more advanced material. Topics covered in this course include linear algebra, basic probability, statistics, and calculus.

ISA 510. Probability and Statistics for Data Analytics. 3 Credit Hours.

Probability and statistics are at the foundation of data science and artificial intelligence. The objective of this course is to provide students with an understanding of how to analyze and understand data through statistics and probability. As such, this course provides an overview of more foundational probability and statistics topics, before delving into more advanced topics through projects. Students will work with data in Python Notebooks to demonstrate their analytical skills.
Session Cycle: Fall
Yearly Cycle: Annual.

ISA 520. Data Visualization and Communication. 3 Credit Hours.

This course examines the art and science of data visualization. It teaches how to visually explore data and how to criticize, design, and implement data visualizations. It teaches the fundamentals of human perception and data visualization, exploratory data analysis and the importance of interaction in exploration, techniques for data visualization of specific data sets (networks, temporal data, geographic data, business data, etc..), and storytelling. The course will enable students to describe a visualization problem, to explore the data using visualizations, to discuss and design appropriate visualization concepts, and to implement and critically reflect on them. We will learn multiple popular data visualization tools such as Power BI, Tableau, and Python to implement our data visualization projects throughout the course.
Session Cycle: Fall
Yearly Cycle: Annual.

ISA 530. Fundamentals of Machine Learning. 3 Credit Hours.

This is a fundamental machine learning course requiring background knowledge including probability theory, linear algebra, calculus as well as good programming skills. The programming environment used in the lecture examples, assignments, and projects will be using the following tools including Python/Pytorch/Keras. The course will cover many of the most important mathematical foundations and computational tools of modern machine learning as well as advanced methods and frameworks used in modern machine learning. We will examine specific models from the literature and examine how they can be used for modeling particular types of data. This course treats both the art of designing efficient machine learning algorithms as well as the science of analyzing and evaluating the properties and computation efficiency of algorithms. This course will help students to select and potentially develop appropriate methods and approaches to problems in real applications.
Session Cycle: Fall
Yearly Cycle: Annual.

ISA 540. Large Scale Data Management and Data Ethics. 3 Credit Hours.

This course introduces data preparation and data management with a focus on applications in large-scale analytics projects utilizing relational, document, and graph database systems. Students learn about the relational model, the normalization process, and structured query language. They learn about data cleaning and integration, and database programming for extract, transform and load operations. Students work with unstructured data, indexing and scoring documents for effective and relevant responses to user queries. They learn to load, store and process big data in a cloud environment. In addition, they explore the social and ethical dimensions of data science and critically evaluate all stages of the data lifecycle from data collection and storage to data analysis and use.
Session Cycle: Fall
Yearly Cycle: Annual.

ISA 691. Directed Independent Study. 3 Credit Hours.

Students interested in exploring an idea, contributing to research, or developing a project may do so under the guidance of an affiliated faculty member in the Data Science/Business Analytics program. At the initiation of the graduate student, the faculty member and the student will develop an academic plan that is submitted to the Chair of the ISA department for approval.

ISA 692. Data Science/Business Analytics Internship. 3 Credit Hours.

ISA internships give students the opportunity for supervised employment in an area where they can apply the Data Science and/or Business Analytics skills they have studied through our curriculum. Interns work at least ten hours per week, meet periodically with a supervising faculty member, and prepare a substantive report on their work experience.
Prerequisites: ISA 510, ISA 520, ISA 530, and ISA 540.

MBA 506. Microeconomics for Business. 0 Credit Hours.

This course serves as a pre-requisite to all on-campus MBA programs for students that do not have sufficient background in the subject. The course provides an overview of microeconomics with an emphasis on understanding key concepts and principles used in business management today. The objective of the course is to teach students how to analyze an organization's performance by applying economic analysis to an array of business situations.

MBA 507. Macroeconomics for Business. 0 Credit Hours.

This course serves as a pre-requisite to all on-campus MBA programs for students that do not have sufficient background in the subject. The course provides an overview of macroeconomics with an emphasis on understanding key concepts and principles used in business management today. The objective of the course is to examine the economy in the long run (when prices are flexible) before examining the economy in the short run (when prices are sticky).

MBA 508. Statistics for Business. 0 Credit Hours.

This course serves as a pre-requisite to all on-campus MBA programs for students that do not have sufficient background in the subject. The course provides a basic background in statistics for students without prior knowledge of statistical analysis and important mathematical ratios which will be utilized throughout the MBA program.

MBA 509. Accounting for Business. 0 Credit Hours.

This course serves as a pre-requisite to all on-campus MBA programs for students that do not have sufficient background in the subject. The course provides an overview of accounting with an emphasis on understanding key concepts and principles used in business management today. The objective of the course is to teach students how to analyze an organization's performance by applying accounting principles to an array of business situations.

MBA 514. Finance for Business. 0 Credit Hours.

This course serves as a pre-requisite to all on-campus MBA programs for students that do not have sufficient background in the subject. The course provides an overview of finance with an emphasis on understanding key concepts and principles used in business management today. The objective of the course is to teach students how to analyze an organization's performance by applying finance principles to an array of business situations.

MBA 515. Management Concepts and Skills. 1 Credit Hour.

This course provides all entering MBA students with a foundation of key management perspectives and skills that will heighten student opportunity for successful program completion. During an intensive, multi-day course, MBA students will be exposed to and participate in instructional sessions addressing technology, research resources, team-building, leadership, communication skills, and case analysis exercises.

MBA 520. Managing Corporate Enterprise. 3 Credit Hours.

Successful management of a corporate enterprise begins with a coherent, well-defined strategy. This course develops the knowledge and skills necessary to analyze, formulate and implement strategy effectively. The course will address the complexity of leading a business in this era of globalization, social and technological change, and dynamic firm and industry boundaries.

MBA 521. Leading Effective Organizations. 3 Credit Hours.

This course emphasizes the importance of understanding the diverse ways that people interpret and respond to situations, emphasizing the complexity of organizational problems, especially in project-oriented, team-based environments. It discusses ways to align individual behavior with the organizations mission and objectives and encourages decision making that is consistent with established models of effective leadership and standards of ethical behavior. It equires students to create personal leadership development profiles and self-improvement plans for their professional practice to aid in the career development.

MBA 522. Reporting and Controlling Resources. 3 Credit Hours.

This course emphasizes the role of accounting in controlling the operations of an organization and the relationship between cost, profits and volume, decision making techniques using accounting data, and the use of programmed budgets as a control mechanism.

MBA 523. Managing Information Resources. 3 Credit Hours.

This course emphasizes knowledgeable and effective use of information systems, IS decision making, knowledge management, and information systems as an element of corporate strategy development.

MBA 524. Managing Financial Resources. 3 Credit Hours.

This course emphasizes the tools and techniques necessary for sound financial decision making including the time value of money, risk and return, capital budgeting, working capital management, and acquisition of long-term capital.

MBA 525. Marketing for Competitive Advantage. 3 Credit Hours.

This course emphasizes markets, innovation and opportunities, consumer characteristics affecting demand, marketing institutions, ethics and government business relations, product planning and pricing problems, distribution channels, promotion, and competitive strategy.

MBA 526. Value Formation Through Operations. 3 Credit Hours.

This course emphasizes the theories and techniques used to manage world class operations for competitive advantage including: operations strategy, process design, quality, inventory control, and project management.

MBA 528. Global Immersion Experience. 3 Credit Hours.

The Global Immersion Experience has been designed to embed the knowledge and skills needed for today’s managers to operate effectively in a globalized world. The course typically encourages students to explore a given country or region in depth and in the process become aware of the economic opportunities and pitfalls in doing business in that country/region. Students will be able to take this experience and apply it to a different country. An important aspect of GIE is a student project with an overseas firm in the destination country that builds on the first semester of academic study covering areas of strategy/leadership, supply chain management and accounting/finance. The Global Immersion Experience is required for the One Year MBA program and optional but highly recommended for the Two Year MBA. While class meetings for MBA 528 begin in the fall term, the GIE takes place during the winter term and entails travelling to a foreign country for approximately ten days.

MBA 621. Business Consulting. 3 Credit Hours.

The central idea is that consulting services, both internal and external to business organizations are useful, are in high demand, and are lucrative. The course is intended for students who wish to understand and use consulting principles and practices for competitive advantage, whether as an intrapreneur, entrepreneur, or a traditional consultant. The course introduces the taxonomy and nature of consulting, provider models and business forms such as feasibility studies, proposals, contracts, reports, and billing practices. It includes the strategic application of technology for improved productivity and performance. It helps students to understand and apply methods of thinking, process analysis, client relations, and reporting that are essential to effective consulting.

MBA 641. Long Term Career Planning. 1 Credit Hour.

MBA 645. MBA Business Practicum. 3 Credit Hours.

Students will work with a corporation or non-profit organization to develop and implement solutions to business problems or plans to exploit business opportunities. Teams will work closely with company executives to develop a project that adds value to the firm and provides students with hands-on experience working with a company.

MBA 651. Mastering Strategic Analysis. 3 Credit Hours.

This MBA Capstone course emphasizes managerial decision-making that involves all aspects of a firm and crosses all functional lines, focusing on the integration of acquired knowledge for strategy development.

MBA 691. Directed Independent Study in Business. 3 Credit Hours.

This course is designed to allow an individual academic program to be tailored to fit the unique interests of a graduate student. At the initiation of the graduate student, the faculty member and the student will develop an academic plan that is submitted to the College of Business for final approval.

MSDS 515. Preparing for MSDS Success. 1 Credit Hour.

This course is designed to provide entering MSDS students with the skills necessary to be successful in a graduate data science program. Spanning two full days, it focuses on foundational knowledge in statistics, programming, data visualization, and communication. Moreover, the course offers insights into program expectations and introduces students to the available computing resources.
Session Cycle: Every Semester.

MSDS 610. Deep Learning. 3 Credit Hours.

This is an advanced course requiring background knowledge including probability theory, linear algebra, calculus, understanding of machine learning methods as well as good programming skills. The course will cover many of the most important mathematical foundations and computational tools of deep learning as well as advanced methods, frameworks, and programming tools used in modern deep learning and artificial intelligence. We will examine popular deep learning models from the literature and examine how they can be used for modeling a variety of types of data. This course treats both the art of designing efficient deep learning and artificial intelligence models as well as the science of analyzing and evaluating the properties and computation efficiency of such models. This course will help students to select and potentially design appropriate models to solve real problems. There will be a heavy emphasis on implementation using Python, Keras (for deep learning), and Pytorch (for deep).
Prerequisites: ISA 510 and ISA 530
Session Cycle: Spring
Yearly Cycle: Yearly.

MSDS 620. Natural Language Processing. 3 Credit Hours.

There are many business and artificial intelligence applications that need to process unstructured text data. This course teaches students how to overcome the unique challenges of working with unstructured text in machine learning and deep learning models. Students learn about how to create text representations, embeddings, and features for modeling purposes. Natural language processing applications include sentiment classification, topic modeling, text generation, and named entity recognition. Students in this course will implement these artificial intelligence models in Python, gaining experience with libraries such as NLTK and Hugging Face.
Prerequisites: ISA 530
Session Cycle: Spring
Yearly Cycle: Yearly.

MSDS 630. Large Scale Data Analytics. 3 Credit Hours.

The course focus on manipulating, storing, analyzing, and visualizing big data. The emphasis of the course will be on mastering Spark which emerged as the most important big data processing framework. We will examine Spark SQL, Spark Machine Learning, Spark Graph Analytics, Spark Natural Language Processing, Spark Deep Learning, and Spark Streaming which allows the analysis of data in near real-time. Students will implement machine learning algorithms and execute them on real cloud systems like Amazon AWS and Databricks.
Prerequisites: ISA 530, ISA 540
Session Cycle: Summer Term 1
Yearly Cycle: Yearly.

MSDS 640. Data Science Capstone. 3 Credit Hours.

Students will execute a full data science project, developing their skills as data scientists with a focus on real-world applications and situations. The final project provides an opportunity to integrate all of the core skills and concepts learned throughout the program and prepares students for long-term professional success in the field. It provides experience in formulating and carrying out a sustained, coherent, and influential course of work resulting in a tangible data science project using real-world data. This capstone project will test student skills in data pre-processing, data preparation, data transformation, feature engineering, machine learning/deep learning, data visualization, data communication, and presentation. Projects will be drawn from real-world problems and will be conducted with industry, government, and academic partners. Emphasis will be placed on problem-solving via state-of-the-art data science pipelines and practices and on the ability to “tell a story” using verbal, analytical, written, and visualization skills.
Prerequisites: ISA 530 and MSDS 630
Session Cycle: Summer Term II
Yearly Cycle: Yearly.

MSDS 691. Directed Independent Study in Data Science. 3 Credit Hours.

This course is designed to allow an individual academic program to be tailored to fit the unique interests of a graduate student. At the initiation of the graduate student, the faculty member and the student will develop an academic plan that is submitted to the College of Business for final approval.