Stevens MBA

This course covers the general area of management of operations, both manufacturing and non-manufacturing. The focus of the course is on productivity and total quality management. Topics include quality control and quality management, systems of inventory control, work and materials scheduling, and process management.

Supply chain analytics is one of the fastest growing business intelligence application areas. Important element in Supply Chain Management is to have timely access to trends and metrics across key performance indicators, while recent advances in information and communication technologies have contributed to the rapid increase of data-driven decision making. The topics covered will be divided into strategic and supply chain design and operations, including -among others- supplier analytics, capacity planning, demand-supply matching, sales and operations planning, location analysis and network management, inventory management and sourcing. The primary goal of the course is to familiarize the students with tactical and strategic issues surrounding the design and operation of supply chains, to develop supply chain analytical skills for solving real life problems, and to teach students a wide range of methods and tools -in the areas of predictive, descriptive and prescriptive analytics- to efficiently manage demand and supply networks.

Project success depends, largely, on the human side. Success in motivating project workers, organizing and leading project teams, communication and sharing information, and conflict resolution, are just a few areas that are critical for project success. However, being primarily technical people, many project managers tend to neglect these "soft" issues, assuming they are less important or that they should be addressed by direct functional managers. The purpose of this course is to increase awareness of project managers to the critical issues of managing people and to present some of the theories and practices of leading project workers and teams.

Executives make decisions every day in the face of uncertainty. The objective of this course is to help students understand how decisions are made, why they are often less than optimal, and how decision-making can be improved. This course will contrast how managers do make decisions with how they should make decisions, by thinking about how “rational” decision makers should act, by conducting in-class exercises and examining empirical evidence of how individuals do act (often erroneously) in managerial situations. The course will include statistical tools for decision-making, as well as treatment of the psychological factors involved in making decisions.

This course exposes students to the macro and micro aspects of organizational behavior and theory that are essential to technology management. The macro aspects will focus on structural contingency theory as an approach to effective organizational design. The micro aspects will focus on leadership, teams, and individual behavior (e.g., motivation, job attitudes). Specific issues and problems which are covered include: the relationship of the organization with the external environment, the influence of the organization's strategies, culture, size, and production technology on the organization's design, and strategies for managing organizational processes such as teams, conflict, power/politics and organizational change. Current topics, that are key to technology management (e.g., virtual teams), will be stressed.

This course explores data-driven methods that are used to analyze and solve complex business problems. Students will acquire analytical skills in building, applying and evaluating various models with hands-on computer applications. Topics include descriptive statistics, time-series analysis, regression models, decision analysis, Monte Carlo simulation and optimization models.

Artificial Intelligence (AI) is an interdisciplinary field that draws on insights from computer science, engineering, mathematics, statistics, linguistics, psychology, and neuroscience to design agents that can perceive the environment and act upon it. This course surveys applications of artificial intelligence to business and technology in the digital era, including autonomous transportation, fraud detection, machine translation, meeting scheduling, and face recognition. In each application area, the course focuses on issues related to management of AI projects, including fairness, accountability, transparency, ethics, and the law.

BIA 610 - Applied Analytics (3)

Applied Analytics is a capstone course for the analytic-focused MBA program. It is intended to integrate all previously taken coursed in the program by presenting a set of increasingly complex business problems. These problems can be solved through analytic skill taught in this and previous courses. In particular, the course is intended to reinforce the understanding of analysis as way to build models that can focus attention on parts of the system that can be improved through intervention. The early part of the course uses synthetic data and empirical data readily available for analysis. The second part of the course encourages students to state and solve their own problem, gathering their own data as a part of the analytic process.

MIS 637 - Data Analytics and Machine Learning (3)

This course will focus on Data Mining & Knowledge Discovery Algorithms and their applications in solving real world business and operation problems. We concentrate on demonstrating how discovering the hidden knowledge in corporate databases will help managers to make near-real time intelligent business and operation decisions. The course will begin with an introduction to Data Mining and Knowledge Discovery in Databases. Methodological and practical aspects of knowledge discovery algorithms including: Data Preprocessing, k-Nearest Neighborhood algorithm, Machine Learning and Decision Trees, Artificial Neural Networks, Clustering, and Algorithm Evaluation Techniques will be covered. Practical examples and case studies will be present throughout the course.

BIA 662 - Augmented Intelligence and Generative AI (3)

This course explores the area of cognitive computing and its implications for todays world of big data analytics and evidence-based decision making. Topics covered as part of this seminar include: cognitive computing design principles, natural language processing, knowledge representation, advanced analytics, as well as IBM's Watson DeepQA and Google's TensorFlow deep learning architectures. Students will have an opportunity to build cognitive applications, as well as explore how knowledge-based artificial intelligence and deep learning are impacting the field of data science.

BIA 667 - Introduction to Deep Learning and Business Applications (3)

This course introduces fundamentals of deep learning with a focus on business applications to students in the School of Business, who, mostly, are beginners of this field. It starts with basic constructs of neural networks and progresses into widely used models including convolutional neural networks, recurrent networks, generative models, and reinforcement learning. Extensive hands-on experiments are provided in class or as assignments for students to practice each model, understand its applicable scenarios, and build practical skills. In addition, various successful deep learning business applications will be studied in this class. Moreover, the potential implications and risks of applying deep learning in the business world will be discussed, and relevant techniques to address such issues will be provided. The objective of this course is to provide students the fundamental concepts of deep learning and to build students’ practical skills of applying deep learning to solve real business problems. Prerequisite course required MIS 637 or equivalent and BIA 660.

BIA 672 - Marketing Analytics (3)

In this course, students will learn about marketing analytics techniques such as segmentation, positioning, and forecasting, which form the cornerstone of marketing strategy in the industry. Students will work on cases and data from real companies, analyze the data, and learn to present their conclusions and make strategic recommendations.

BIA 674 - Supply Chain Analytics (3)

Supply chain analytics is one of the fastest growing business intelligence application areas. Important element in Supply Chain Management is to have timely access to trends and metrics across key performance indicators, while recent advances in information and communication technologies have contributed to the rapid increase of data-driven decision making. The topics covered will be divided into strategic and supply chain design and operations, including -among others- supplier analytics, capacity planning, demand-supply matching, sales and operations planning, location analysis and network management, inventory management and sourcing. The primary goal of the course is to familiarize the students with tactical and strategic issues surrounding the design and operation of supply chains, to develop supply chain analytical skills for solving real life problems, and to teach students a wide range of methods and tools -in the areas of predictive, descriptive and prescriptive analytics- to efficiently manage demand and supply networks.

BIA 658 - Social Network Analytics and Visualization (3)

Given a data matrix of cases-by-variables, a common analytical strategy involves ignoring the cases to focus on relations among the variables. In this course, we examine situations in which the main interest is in dependent relations among cases. Examples of “cases” include individuals, groups, organizations, etc.; examples of “relations” linking the cases include communication, advice, trust, alliance, collaboration etc. Application areas include social media analytics, information and technology diffusion, organization dynamics. We will learn techniques to describe, visualize and analyze social networks.

BIA 670 - Risk Management and Simulation (3)

Theoretical and practical aspects of risk assessment and management will be covered. Major topics include: Importance of innovation and technological changes in current competitive environment, risk and uncertainty, decision trees, binomial methods and derivation of Black-Scholes option pricing formula, extension of option methodology to non-financial (real) options, VAR (value at risk), a framework of risk assessment, and several real-world case studies. The course is designed for all students in the School of Technology Management.

FIN 510 - Financial Statement Analysis (3)

This course deals with (1) interpretation of financial statements, (2) evaluation of the alignment between business strategies and financial performance, (3) identification of potential business risks, and (4) comparison of performance of different companies. The course introduces business analysis and valuation techniques and utilizes real world data to help students comprehend financial statement analysis tools. Topics covers financial statement information, tools of financial statement analysis, and forecasting and valuation techniques.

FIN 526 - Private Equity and Venture Capital (3)

This course addresses the fundamentals of venture capital, which includes the venture capital industry, the structure of venture capital firms and venture capital investments. It addresses in some detail the relationship between venture risk and return, the cost of venture capital and the valuation of high growth companies. The course covers a variety of valuation methods as well as analysis of company capital structure or “cap tables”.

FIN 627 - Investment Management (3)

This course takes a practical approach to managing investments. It covers a wide variety of investment vehicles ranging from pure equity and debt offerings to complex derivatives and options. Various investment strategies are presented which are focused on the different fundamental approaches and tactics used by leading investors to achieve their financial goals. The course also focuses on investment styles, including momentum, growth, income, distressed, asset allocation, and vulture investing, to name just a few. Students participate in real time simulation experiences to create viable portfolios of stocks, bonds and other investments; while tracking their performance against the overall market and the class on a weekly basis throughout the course.

FIN 638 - Corporate Finance (3)

This course serves as a second semester sequence in corporate finance. Students enrolling should have a mastery of the topics of covered in Managerial Finance I (EMT 623), including time value of money, capital budgeting, risk adjusted hurdle rates, managerial accounting, and ratio analysis. Among the topics covered in EMT 638 are: leverage on the balance sheet and weighted average cost of capital; bankruptcy, turnarounds, and recapitalizations; international currency hedging; stock options; private equity valuation; mergers and acquisitions; and the issuance of public and private securities.

FE 511 - Introduction to Bloomberg LSEG, and Capital IQ (1)

This course is designed to teach students the nature and availability of financial data available at Stevens. The focus of the course will be on equity, futures, FX, options, swaps, CDS’s, interest rate swaps, etc. Students will learn to how use a Bloomberg terminal, and as part of the course, students will be certified in the four areas that Bloomberg offers certification. We will also cover the LSEG tick history data, S&P Capital IQ, and basics of using these data. The course also introduces basics of applied statistics. Bloomberg terminal access will be arranged for any student taking the course online.

FE 515 - Introduction to R (1)

In this course the students will learn the basics of the open source programming language R. The language will be introduced using financial data and applications. Basic statistical knowledge is required to complete the course. The course is designed so that upon completion the students will be able to use R for assignments and research using data particularly in finance.

FE 520 - Introduction to Python for Financial Applications (1)

This course aims to give students hands-on practical experience with the Python programming language. The course will cover the basic syntax rules, modules, importing packages (e.g., numpy, pandas), data visualization, and introductory topics in machine learning using Python. Students will apply topics learned in this course to solve financial programming problems. This course is designed for students who have limited or no experience with Python.

FE 513 - Financial Lab: Practical Aspects of Database Design (1)

The course provides a practical introduction to fundamental data science techniques. Students will become familiar with databases and working with data analysis tools. Students will be able to manage data in various databases and solve financial problems using R program packages. This course is designed for graduate students in the Finance programs at the School of Business.

FA 582 - Foundations of Financial Data Science (2)

This course will provide an overview of issues and trends in data quality, data storage, data scrubbing, data flows, and data encryption. Topics will include data abstractions and integration, enterprise level data issues, data management issues with collection, warehousing, preprocessing and querying. Furthermore, the Hadoop based programming framework for big data issues will be introduced along with any governance and policy issues. These concepts will be applied to areas such as digital marketing and computational advertising, energy and healthcare analytics, social media and social networks, and capital markets financial data. A one credit Hanlon lab course, FE 513: Practical Aspects of Database Design will be attached to this course in order to facilitate learning of the practical side of data management.

FE 630 - Portfolio Theory and Applications (3)

This course introduces the modern portfolio theory and optimal portfolio selection using optimization techniques such as linear programming. Topics include contingent investment decisions, deferral options, combination options and mergers and acquisitions. The course then focuses on financial risk management with emphasis on Value-at-Risk (VAR) methods using general and parametric distributions and VAR as a risk measure. Real world scenarios are studied.

FE 620 - Pricing and Hedging (3)

Following an introduction to the financial markets for equity, FX, interest rates, commodities and credit, covering the main traded products including derivatives (futures, forwards, and options), this course deals with basic financial derivatives theory, arbitrage, hedging, and risk. The course introduces the risk neutral pricing approach for derivatives valuation and hedging, which is presented both in discrete time and in continuous time. The Black-Scholes theory for option pricing is introduced, and is used to study valuation, the Greeks and hedging of derivatives.

Prerequisite or corequisite: FE 543 or FE610, or permission of the instructor.

Choose either FE 543 or FE 610

FE 543 - Introduction to Stochastic Calculus for Finance (3)

This course introduces stochastic calculus to students of finance and financial engineering. Initially the students are exposed to the Binomial Asset Pricing model, which is used to develop the theory around martingales and risk-neutral pricing with discrete examples. The course then moves into using Geometric Brownian motion which leads to the Black-Scholes-Merton model as its basis for the stock market to develop the theory for continuous models. This course is intended for students who are interested in stochastic calculus but have only a basic background in probability.

FE 610 - Stochastic Calculus for Financial Engineers (3)

This course provides the foundation for understanding modern financial theory through applied mathematics. Stochastic processes are used to model a variety of financial processes such as stock prices, portfolio valuations, and interest rate models. The students will learn how to apply Ito integrals and implement arbitrage free pricing of derivatives. This is then expanded on to include stochastic differential equations and exotic option pricing as well as the basis of jumpdiffusion models. Applications to financial instruments are discussed throughout the course. Students are expected to have a strong background in applied mathematics (analysis and calculus) and probability.

Choose either FE 535 or FE 621

FE 535 - Introduction to Financial Risk Management (3)

This course deals with risk management concepts in financial systems. Topics include identifying sources of risk in financial systems, classification of events, probability of undesirable events, risk and uncertainty, risk in games and gambling, risk and insurance, hedging and the use of derivatives, the use of Bayesian analysis to process incomplete information, portfolio beta and diversification, active management of risk/return profile of financial enterprises, propagation of risk, and risk metrics.

FE 621 - Computational Methods in Finance (3)

This course provides computational tools used in industry by the modern financial analyst. The current financial models and algorithms are further studied and numerically analyzed using regression and time series analysis, decision methods, and simulation techniques. The results are applied to forecasting involving asset pricing, hedging, portfolio and risk assessment, some portfolio and risk management models, investment strategies, and other relevant financial problems. Emphasis will be placed on using modern software.

FA 582 - Foundations of Financial Data Science (2)

This course will provide an overview of issues and trends in data quality, data storage, data scrubbing, data flows, and data encryption. Topics will include data abstractions and integration, enterprise level data issues, data management issues with collection, warehousing, preprocessing and querying. Furthermore, the Hadoop based programming framework for big data issues will be introduced along with any governance and policy issues. These concepts will be applied to areas such as digital marketing and computational advertising, energy and healthcare analytics, social media and social networks, and capital markets financial data. A one credit Hanlon lab course, FE 513: Practical Aspects of Database Design will be attached to this course in order to facilitate learning of the practical side of data management.

FA 595 - Financial Technology (3)

This course deals with financial technology underlying activities of markets, institutions and participants. The overriding purpose is to develop end-to-end business decision making data analytics tools along with enterprise level systems thinking. Statistical learning algorithms will be connected to financial objects identification and authentication along with the appropriate databases to create enterprise level financial services analytics systems.

FA 591 - Blockchain Technologies & Decentralized Finance (3)

The course will introduce concepts of Blockchain technologies as they apply to decentralized finance. The course starts with cryptocurrency and advances the concept of smart contracts as they apply to financial instruments. The course is technical and requires knowledge of programming in Python as well as financial instruments and concepts. Programming in solidity is learned throughout the class. The course discusses risk management concepts, stable coins as well as how regulations may impact the area.

FA 596 - Digital Payment Technologies and Trends (3)

This course introduces students to the up-to-date payment systems and innovative financial technologies (FinTech) in the payment systems. Common payment systems such as checking, ACH, cards, cash, wire transfer are discussed. Students learn the mechanisms of FinTech innovations, including the Non-Fungible Tokens (NFTs). Students also learn how to set up digital wallets and interact with the blockchain.

FE 513 - Financial Lab: Practical Aspects of Database Design (1)

The course provides a practical introduction to fundamental data science techniques. Students will become familiar with databases and working with data analysis tools. Students will be able to manage data in various databases and solve financial problems using R program packages. This course is designed for graduate students in the Finance programs at the School of Business.

MIS 699 - Digital Innovation (3)

IT organizations must be able to leverage new technologies. This course focuses on how organizations can effectively and efficiently assess trends and emerging technologies in data and knowledge management, information networks, and analyzing and developing application systems. Students will learn how to help their organizations define, select, and adopt new information technologies.

MIS 710 - Process Innovation and Management (3)

This course focuses on the role of information technology (IT) in reengineering and enhancing key business processes. The implications for organizational structures and processes, as the result of increased opportunities to deploy information and streamlining business systems are covered.

MIS 714 - Service Innovation (3)

This course leads students through the identification, analysis, definition, and deployment of service opportunities within public and private organizations. Each of these phases is analyzed in detail to encompass the principal activities, methods, tools and techniques applied in the respective phase. Students will learn how to identify appropriate supporting techniques and information technologies for the different phases of the service life cycle, assess the role of technology, and gauge the organizational impact of service-focused operations. The objective of the course is to enable students to identify, implement and evaluate innovative service offerings in their organization.

MIS 730 - Integrating Information System Technologies (3)

This course focuses on the issues surrounding the design of an overall information technology architecture. The traditional approach in organizations is to segment the problem into four areas - network, hardware, data and applications. This course will focus on the interdependencies among these architectures. In addition, this course will utilize management research on organizational integration and coordination science. The student will learn how to design in the large, make appropriate choices about architecture in relationship to overall organization goals, understand the different mechanisms available for coordination and create a process for establishing and maintaining an enterprise architecture.

MGT 609 - Project Management Fundamentals (3)

This course deals with the basic problems of managing a project, defined as a temporary organization built for the purpose of achieving a specific objective. Both operational and conceptual issues will be considered. Operational issues include definition, planning, implementation, control, and evaluation of the project. Conceptual issues include project management vs. hierarchical management, matrix organization, project authority, motivation, and morale. Cases will be used to illustrate problems in project management and how to resolve them.

MGT 610 - Strategic Perspectives on Project Management (3)

This course provides a theoretical perspective on project management for a better understanding of project implementation in modern organizations. The course is based on the premise that success in project leadership depends on a proper managerial style and attitude, and not on specific tools for planning and controlling. The course focuses on developing the manager’s conceptual thinking and on building “the project manager’s mind.” The course helps managers see the entire project landscape and the long-term issues that are critical to project success. It will also address the organizational aspects of initiating and running the program.

MGT 611 - Project Analytics (3)

Formalized procedures, tools, and techniques used in conceptual and detailed planning of the project. Development of work breakdown structure as the foundation for project cost and project duration. Application of project data in monitoring the project progress and in formulating remedial actions in response to unexpected occurrences.

MGT 619 - Leading Across Projects (3)

This course focuses on key leadership skills for addressing the complex challenges posed by program management, highly- matrixed environments and cross-national collaborations It’s purpose is enhance individuals’ abilities to develop others, strategically integrate efforts across groups, and drive change. The concepts presented are theory and research driven so that participants can deepen their conceptual understanding. At the same time, the course calls upon learners to address real-life challenges they face as program and or director level leaders. Each session presents effective techniques and uses experiential exercises or assignments to provide plenty of practice. The course also requires participants to further transfer learning to their workplaces through focused development planning and coaching support.

The MBA Project Management Concentration is accredited by the PMI Global Accreditation Center for Project Management Education Programs (GAC).

SM 510 - Perspectives in Environmental Management (3)

This course addresses environmental management and its role in sustainability from multiple perspectives, including but not limited to that of a natural scientist, an engineer, a marketing manager, an economist, an environmental lawyer, and a policy maker. The course also introduces students to some of the many tools used by environmental managers, such as life cycle analysis, environmental audit, etc. Students will learn from the course instructor and invited subject matter experts, who will explain in a non-technical manner that is intended for adequate comprehension by students from diverse fields of study on how their respective disciplines contribute to proper management of our environment, thereby making our world more sustainable.

SM 530 - Sustainable Business Strategies (3)

This course will focus on best practices and emerging trends in sustainable business management. Topics will include corporate social responsibility, sustainable business theories, green business models, value chain management, green marketing, triple bottom line reporting, benefit-cost analysis and sustainability metrics and reporting. Students will explore the relationship between business management and sustainability goals for a number of industrial sectors. The course will include case studies as a tool for assessing strategies, identifying opportunities for improvements and recommending future actions. Students will be introduced to commonly used sustainability reporting frameworks and will use them to evaluate objective-setting and progress towards green goals.

SM 587 - Environmental Law and Management (3)

This class addresses a survey of legal and regulatory approaches to environmental protection. Topics include: environmental ethics, National Environmental Policy Act, state and federal environmental agencies; Clean Water Act, Safe Drinking Water Act, Superfund, Resource Recovery and Conservation Act, Right-to-know, Environmental Cleanup Responsibility Act, and wetlands protection.

SM 531 - Sustainable Development (3)

This course addresses issues of sustainable development at the local, regional and global scales. Topics include understanding of the definitions, history, current status and future outlook of sustainable development. Population dynamics, wealth distribution, principles of economic growth, social dimensions of sustainable growth (poverty, food security, health, education, social inclusion), biodiversity and ecosystem dynamics, climate change. Sustainable development stakeholders and their roles and responsibilities including individuals, advocacy groups, local, regional and country-level governing bodies, NGO’s and corporations. Legal, policy and regulatory aspects of sustainable development. A systems view of sustainability and sustainable development including the concepts of global boundaries and resiliency.

All candidates to this program are required to submit GMAT or GRE scores with their application; part-time applicants with work experience may be eligible to waive this requirement. Only students with excellent test scores will be deemed a fit for the coursework. However, it's important to keep in mind that your test scores are only one feature of your application, and will be considered along with your other credentials. Please use the following reporting codes to submit test scores to Stevens:

• GRE: 2819

• GMAT: 638LX12

International students also must include TOEFL or IELTS scores along with their applications.

Your application must include official transcripts from all universities you have attended. These records must show your name, the name of the university attended, enrollment dates, coursework completed, and grades assigned.

Given the highly technical nature of this degree, students are required to have completed one semester of calculus and one semester of basic probability, hypothesis testing and estimation prior to starting the program. Stevens offers noncredit courses for students needing to satisfy this requirement.

You must include a résumé with your application that highlights: 

• Academic record

• Work and internship experience

• Leadership abilities

Stevens often invites master's candidates to interview prior to making an admissions decision. If you are selected for an interview after submitting your application, you will receive instructions via email.