Financial Technology and Analytics Master's Degree

Your application must include official transcripts from all universities you have attended, or in which you are currently enrolled. These records must show your name, the name of the university attended, enrollment dates, coursework completed and grades assigned. Your bachelor's degree must be in science, mathematics, computer science, engineering or a related discipline. Your degree also must come from an accredited institution, and you must have attained a B average, to be considered.

Work experience is not a requirement for this master's program. However, the admissions committee values applicants with at least one year of professional experience. You must include a résumé with your application that highlights:

• Academic record.

• Work and internship experience.

• Leadership abilities.

• Professional aspirations.

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.

Your application must include two letters of recommendation. The strongest applications will include one letter from a current supervisor, and one from a former supervisor or previous employer who can speak to your leadership potential and discuss your professional performance. 

This course provides an overview of issues and trends in data quality, data storage, data scrubbing and data flows. Topics include data abstractions and integration, enterprise-level data issues, data management issues, similarity and distances, clustering methods, classification methods, text mining, and time series. Furthermore, the Hadoop-based programming framework for big data issues will be introduced, along with any governance and policy issues. 
Corequisite: FE 513

The course introduces required techniques and fundamental knowledge in data science techniques. It familiarizes students with database and data analysis tools, teaching them to manage databases and solve financial problems using R. 

This course provides an applied overview of both classical linear approaches to statistical learning and more modern statistical methods. The classical linear approaches include logistic regression, linear discriminant analysis, k-means clustering and nearest neighbors, while modern approaches include generalized additive models, decision trees, boosting, bagging and support vector machines.

This course is designed for students undertaking a research or analytical project either individually or as a group. The goal of this course is to train students' ability to work on a research-oriented project in a group environment, and also train their professional presentation and scientific writing skills.

The Fintech concentration is focused on decentralized finance, blockchain, and digital payment systems.

FA 595 Financial Technology (3)
This course deals with the networking and machine-learning technologies underlying activities of markets, institutions and participants. The overall purpose is to give students a working understanding of a wide variety of the technological tools that permeate modern life. The successful student will be able to extend this knowledge, understand systems currently in place and use new developments in the field as they are created.

FA 591 Blockchain Technologies and 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 domestic and cross-border payment systems such as checking, ACH, cards, cash, wire transfer are discussed. Students learn hands-on skills of using blockchain technology as an innovative form of digital payments. Students also learn the mechanisms of recent fintech innovations, including Smart Contracts and Non-Fungible Tokens (NFTs). The course also trains students’ blockchain coding skills, including interpreting and writing smart contract codes.

The Financial Data Science concentration is focusing on automating financial advising with Data Analytics, and managing exposure to financial risk. 

FA 541 Applied Statistics with Applications in Finance (3)
The course prepares students to employ essential ideas and reasoning of applied statistics. Topics include data analysis, data production, maximum likelihood, method of moments, Bayesian estimators, hypothesis testing, tests of population, multivariate analysis, categorical data analysis, multiple regression, analysis of variance, nonlinear regression, risk measures, bootstrap methods and permutation tests. The course is designed to familiarize students with statistical software needed for analysis of the data. Financial applications are emphasized but the course serves areas of science and engineering where statistical concepts are needed. This course is a graduate course and is covering topics for a deeper understanding than undergraduate courses such as MA331 and BT221. Furthermore, the course will cover fundamental statistical topics which are the basis of any advanced course applying statistical notions such as MGT718, BT652 as well as courses on machine learning, knowledge discovery, big data, time series, etc.

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.

In addition to the two courses above, students take one of the time series classes below.

FA 542 Time Series with Applications in Finance (3) - Fall Semester
In this course the students will learn how to estimate financial data model and predict using time series models. The course will cover linear time series (ARIMA) models, conditional heteroskedastic models (ARCH type models), non-linear models (TAR, STAR, MSA), non-parametric models (kernel regression, local regression, neural networks), non-parametric methods of evaluating fit such as bootstrap, parametric bootstrap and cross-validation. The course will also introduce multivariate time series models such as VAR.

FIN 620 Advanced Financial Econometrics (3) - Spring Semester
This course will cover the main topics of the analysis of time series to evaluate risk and return of the main products of capital markets. Students will work with historical databases, conduct their analysis, and conduct tests based on the techniques reviewed during the class. The significant amount of historical information available for most financial instruments requires a systematic and analytical approach to select an optimal portfolio. Time series analysis facilitates this process understanding, modeling, and forecasting the behavior of financial assets.

This course is designed for students undertaking a research or analytical project either individually or as a group. The goal of this course is to train students' ability to work on a research-oriented project in a group environment, and also train their professional presentation and scientific writing skills.

A minimum of six credit hours is required for the thesis. Hours and credits to be arranged. You will need both an advisor and a reader to complete this course; interested students should contact their academic advisor for complete details.