Financial Technology and Analytics Master's Degree
Program Details
Degree
Master of ScienceAvailable
On Campus & OnlineToday's financial industry is driven by technology, making it essential for professionals to have expertise in areas such as financial technology, data science, and advanced analytical modeling.
At Stevens, financial analysts combine their programming skills with talents in data analysis and statistics to design innovative solutions to finance problems. Financial Analysts also use excellent business and communication skills to assess client needs and make recommendations to executive teams.
The Master's in Financial Technology and Analytics program is designed for STEM students who are looking to pursue careers in the financial industry. The program covers a range of topics in financial technology and data science, including financial technology, blockchain technologies and decentralized finance, digital payment technologies and trends, applied statistics with applications in finance, introduction to financial risk management, and time series with applications to finance or advanced financial econometrics.
Graduates of our program will be well-equipped to lead financial technology and data science teams in both start-ups and established financial firms. They will be able to build advanced analytical models, make enterprise data analytics decisions, and orchestrate advanced financial systems technology resources in a cloud-based data-driven distributed environment. They will also have the skills to construct innovative financial products and apply their expertise to a range of general financial services analytics.
Our faculty are experts in the field and are constantly exploring new concepts in machine learning, data modeling, and optimization in finance. Students will have the opportunity to learn from these professionals and work with the data analysis and visualization tools used on Wall Street in our state-of-the-art financial labs.
The Technology and Financial Analytics program has two concentrations:
• The Financial Data Science concentration focuses on Data Analysis and Machine Learning applications to Finance.
• The Financial Technology concentration is focused on the newest technology emerging in recent years.
As a student, you are required to choose one of the concentrations, and additionally customize your degree with a set of four elective courses, including the chance to pursue a structured specialization tailored to your career interests. A close collaboration between you and your faculty advisor will help you select the right classes for your future.
Core Courses
These 9 credits are required for both concentrations. The students will learn fundamental data techniques, SQL, as well as machine learning techniques. FA582 and FE513 are first semester classes, while the capstone FA800 is to be completed in the last semester.
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.
Concentrations
The students in the M.Sc. in Financial Technology and Analytics are required to choose one of the following two concentrations. Please expand to see the courses in each concentration.
The Fintech concentration is focused on decentralized finance, blockchain, and digital payment systems.
FA 595 Financial Technology
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
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
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
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
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 (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 (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.
Capstone Course or Master's Thesis
Students are required to complete a significant project as part of their Master experience. They can choose to complete the FA 800 Project in Financial Analytics during their final semester. They would work in teams often on projects offered by our industry partners.
Alternatively, the students may complete a thesis option. This means completing FA900 over two consecutive semesters. The project is going to be individual and will prepare the students in case they wish to pursue a Ph.D. degree in Data Science.
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.
Ideal candidates for the program will have a strong background in statistics, mathematics, physics, engineering or a related discipline, or will have several years' experience working in finance with an interest in exploring these disciplines in great depth.
Application deadlines
Full-time applications to the Financial Analytics program are accepted in three distinct cycles. To be considered for admission, all materials must be submitted by the deadline.
Application cycle | Application deadline | Application decision | Acceptance deadline |
Priority | Oct. 15 | Dec. 1 | Jan. 15 |
Standard | Jan. 15 | March 1 | Included in admission offer |
Final | April 15 | May 1 | June 1 |
Part-time applications to this program are accepted on a rolling basis.
Admission criteria
Admission to the Financial Technology and Analytics master’s program is competitive. To be considered for this program, your application must include the following.
Experience recommended
You may apply to the Financial Technologies and Analytics program without any work experience, but students without work experience will need excellent academic credentials from their undergraduate work, with a degree in statistics, mathematics, physics, engineering, or another math or science discipline.
For finance professionals interested in working in an analyst capacity, work experience is preferred. To keep up with the technical nature of coursework, computer science and statistics skills also are required.
A list of general Stevens admissions criteria is available at the Office of Graduate Academics.
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.
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.