The purpose of this course is to empower students to become collaborative, ethical leaders in the globalized, 21st-century workplace. Students learn concepts and practice skills that will enable them to transition from being an engineering sole contributor to managing and leading others as business professional. Students gain a sound understanding of management and leadership; increase awareness of their own management and leadership styles; build and practice competencies essential for team success (e.g., effective communication, collaboration, conflict resolution); and become ethical leaders above reproach. Emphasis is on leading teams in a volatile, complex and interdependent world. 3 units.

This comprehensive course examines core and evolving concepts in the business fundamentals of successful technology-based companies including Business Plan Development & Strategies, Marketing, Product & Process Development processes, Intellectual Property, Accounting, Finance, and Operations. Students will learn the fundamentals essential to understanding all aspects of a business and will be able to converse in some depth in each of the areas studied upon completion. Other topics will include Supply Chain Management, Stage-Gate Development Cycles, Balanced Scorecards, Blue Ocean Strategy, and Disruptive Technologies. 3 units.

This course focuses on moving from small-to-medium software projects, to the design ideas required for larger scale, maintainable code. We will start with core design principles, which we will see manifest in a variety of forms through the course of the semester. We will see these ideas emerge from smaller scale design at the start of the semester to large scale system architecture at the end. Testing will also be an important topic throughout. 3 units.

This course is about minimizing risk when creating software and will focus on the fundamental structure of a Secure Development Life Cycle (SDLC), the advantages and challenges of cryptography, and then explore automated testing solutions. Students will learn to effectively manage risk in the process of creating software. Hands-on experience with specific technologies prepares students to make informed decisions about the design, architecture, and implementation of software. Assignments use automated vulnerability-hunting tools. Students will learn the risk profile of the target software project and an understanding of how these tools add value to the overall secure development lifecycle. 3 units.

This course introduces students to the tools, concepts, and workflow used by industry to craft algorithmic trading systems, as well as the financial concepts involved. Using the Python Dash framework, students will build simple but powerful trading apps that fetch data, pass trade orders, and evaluate performance metrics. Students will gain exposure to modern Python data analytics packages, GitHub Actions, market data feeds, web scraping, and trade execution system APIs. The course assumes an entry-level understanding of Python and finance and is intended for students who wish to take their skills to the next level. 3 units.

An introduction to the most important concepts used in quantitative finance. Students will learn to build practical financial models using MS Excel spreadsheets. This course starts with the most basic, and most important, portfolio and investment models used to evaluate risk and identify profit opportunities. Using Excel, students will learn how to build these models themselves, and to understand the decision-making inputs used by professional investors. The course has a practical focus—how to analyze prices of stocks, bonds, options and other financial instruments using the types of computationally sophisticated tools in wide use today. 3 units.

This course is intended for students who are already comfortable with Python Dash, have some knowledge of finance, trading, and market data, and wish to take a deep dive into the development and evaluation of one trading strategy. Forming teams of 2 to 4, students will produce a Python trading app that implements the team’s strategy to process incoming data into actionable trade orders, pass the orders to a professional execution system, and visualize results and performance metrics as a dynamic web page. At the end of the semester, each team will present its strategy and results to Duke faculty and industry professionals at the annual Alpha Summit event. Prerequisite: FINTECH 533. 3 units.

Robo-Advice brings investment services to a wider audience at lower costs compared to human advisors. Students will construct a very basic advisor using the Python programming language. This will be a short experiential case study with an open-source Python code. Student teams will develop a comprehensive venture capital investment memorandum for a real-world Robo-Advising startup. Teams will analyze the Robo-Advisor’s market environment, including the financial services industry, wealth management segments, competitors and channels; and, internal company characteristics, such as business strategy, asset allocation and portfolio composition, cost of customer acquisition, and financials. 3 units.

Explores the history, current environment, and near-term outlook of Machine Learning, focusing on the applications within financial innovation (FinTech). The course provides hands-on experience in applying machine learning tools in a number of situations, as well as understanding the applications across finance. This class will delve into elements of the current environment of Fintech and how machine learning has contributed to the disruption. The goal of this course is that students leave with not only knowledge but hands-on experience implementing machine learning to solve problems and observe how this tool works and where the present and future value may be. 3 units.

Quantitative Risk Management offers a hands-on introduction to the science and implementation of risk analytics. Topics include probability theory, regression and time series analysis, risk metrics such as Value at Risk and Excepted Shortfall, derivative valuation methods, stress testing and scenario analysis, factor models, and portfolio construction and optimization. 3 units.

Blockchain technology is being embraced in finance and other industries as an encryption base for all types of applications. This course explores the history, current environment, and near-term outlook of financial innovation (FinTech), focusing on applications of Blockchain technology. Topics range from digital stores of value to documents and transactions. Students will learn to formulate an accurate image and a deep practical understanding of the capabilities and limitations of various blockchain techniques. Students will gain hands-on experience creating a simple Blockchain contract and will be able to converse on a practical basis about what Blockchain can and cannot do. 3 units.

This course follows the basic blockchain course to provide students with hands-on experience and instruction in Solidity coding via a number of exercises and programming assignments. These provide a basis from which students will be introduced to the details of smart contracts and the application of the coding skills acquired to develop and deploy these programs. Deployment will be primarily via public blockchains using developer functions. Prerequisite: FINTECH 564. 3 units.

Developing compelling products in FinTech often requires the harmonization of user requirements, the industry/regulatory architecture, and opportunities for business model innovation with the disruptive potential of technology. The course “FinTech Product Development” is designed to provide students in the Master of Engineering in FinTech, and related programs, with the fundamental knowledge and skills needed to achieve this orchestration in creating innovative and impactful products in the dynamic FinTech landscape. By the end of the course, students will have developed a fundamental toolkit for the creation of FinTech products. They will be equipped with the skills and empathy to effectively collaborate within and make significant contributions to multidisciplinary product development teams.

Explores the world of mobile application development with a focus on the needs of engineers. Centered on the Apple environment, with the development environment being on OS X and the target environment being an iOS device – iPad, iPhone, iPod Touch or Apple Watch. Real-world context focused on the common programming “patterns” for engineers in academia or business – standalone apps, apps connected to other systems, and apps connected to the cloud. Covers fundamentals essential to understanding all aspects of app development. Taught in a team environment. Students are required to present their project proposals and deliver an app as a final project. 3 units.

Students learn best practices for presenting discoveries and “calls to action” that are the primary aims of business data analysis. Learning about human visual perception, in particular the science of how the choice of color, form, and other design elements can assist pre-attentive information processing. Origins of modern data visualization in the pre-computer age are considered, starting with the use of overlay maps, and Galton’s Quincunx and Correlation Diagram. Students learn to recognize the most commonly utilized types of data-visualization metaphors, as well as rules of thumb for various types of data analysis. No prior software experience is required.

This class will study the environment of FinTech services to understand and acquire assessment techniques to model the motivation behind, for example: individual companies and offerings, the technology that has enabled many of these companies, and the business models that frequently challenge the customer service status quo. Applications of Game Theory—the ways in which businesses compete in the financial marketplace—will provide significant insights into the strategic behavior of current and future FinTech companies. The ever-increasing pace at which technology disrupts long-standing business models will be reviewed in terms of both past, current, and possible future applications. 3 units.

The goal of this course is for students to understand the business models in the major FinTech value chain segments (businesses include, but are not limited to, marketplace lending, neo-banking, robo-advisory, cryptocurrency, and other blockchain applications). In this course, we analyze the business models of selected FinTech companies with a special focus on the role of data. In some industries, such as banking, data has spurred and supported the new business models of the FinTechs. Therefore, data is most relevant for creating an overview of the actors in the FinTech, and broader financial services ecosystem. 3 units.

This course takes students through a variety of issues related to managing innovation in the context of a technology-based organization. This includes managing know-how and innovation processes as well as creating an organizational culture that fosters and supports innovation. Students study best practices and benchmarks but must develop their own approach to managing innovation given each unique situation, including the organizational strategy, the competitive landscape, the strengths/weaknesses of the employees involved, etc. Nonetheless, there are accepted practices and concepts that will help guide students in developing a deeper understanding of this area.

Learning objectives include:

• Understanding the different processes related to innovation in a technology-based firm
• How to create a culture of innovation in an organization
• The critical role of champions
• Key concepts of innovation strategy

FinTech Law and Policy will seek to understand the architectures, principal legal and regulatory issues, and the dynamics of modern financial marketplaces as these are shaped by technology. The seminar will help prepare students for a rapidly evolving framework in which successful business and legal practice must become technologically “bilingual.” Requires prior or current registration in a financial regulatory course. Please discuss with instructors if you think your prior course might be eligible. 3 units.

An introduction to the most important concepts used in quantitative finance. Students will learn to build practical financial models using MS Excel spreadsheets. This course starts with the most basic, and most important, portfolio and investment models used to evaluate risk and identify profit opportunities. Using Excel, students will learn how to build these models themselves, and to understand the decision-making inputs used by professional investors. The course has a practical focus—how to analyze prices of stocks, bonds, options and other financial instruments using the types of computationally sophisticated tools in wide use today. 3 units.

Quantitative Risk Management offers a hands-on introduction to the science and implementation of risk analytics. Topics include probability theory, regression and time series analysis, risk metrics such as Value at Risk and Excepted Shortfall, derivative valuation methods, stress testing and scenario analysis, factor models, and portfolio construction and optimization. 3 units.

Two course sequence that provides a quantitative backstop for practitioners in the alternatives space. Alternatives as an asset class is a fast-evolving segment that has experienced tremendous growth in the recent past, without the lengthy gestation periods that have provided our traditional asset classes with robust institutions, processes, oversight and data sets. As a result, it is critical the future leaders in the alternatives space adopt a” full stack” approach at the intersection of the advances of finance and technology.

This course is designed to provide students with a good understanding of graduate level corporate finance tools and models, in particular as they apply to investments. Investments covered will focus on Venture and Private Equity/Debt, as well as Public Offerings. The coursework will be augmented with lectures from professionals in the investing space.

An introduction to the basic concepts of mathematical finance. Topics include modeling security price behavior, Brownian and geometric Brownian motion, mean variance analysis and the efficient frontier, expected utility maximization, Ito’s formula and stochastic differential equations, the Black-Scholes equation and option pricing formula.

A rigorous introduction to financial derivatives with applications. Topics include: binomial trees and geometric Brownian motion; European options, American options, forwards, and futures; put-call parity; the Black-Scholes-Merton pricing formula and its derivations; Delta and Gamma hedging; implied volatility; Merton jump-diffusion model; Heston model; GARCH(1,1) model.

The Master of Engineering Internship or Project requirement provides an opportunity for all MENG students to apply their classroom learning to real-world work experience. Although students are responsible for finding their own internship, Duke provides an experienced career development team to help with your search.

Internships are typically 8-12 weeks. The minimum hourly requirement for the internship is 320 hours, equivalent to 8 weeks, 40 hours per week.

Online students in the FinTech program have the option of seeking approval from their faculty advisor to complete a project in lieu of a traditional internship, providing the project meets the same learning objectives.

Learning Objectives

All internships/projects must:

• Apply engineering principles to solving one or more problems outside the classroom environment
• Define a problem and determine potential solutions
• Appreciate the importance of organizational dynamics and work relationships
• Practice professional communication both written and orally
• Complement material presented in Master of Engineering courses
• Include a self-assessment upon completion in MENG 551

This assessment course is the culmination of your internship or project work. You will prepare a substantive assessment of your internship or project experience via a written report and/or oral presentation. A polished analysis should include:

• Problem or task conducted
• Challenges faced
• Solutions incorporated
• Workplace communication and interpersonal relationship critique
• Individual performance review