Financial Technology & Analytics Master's Degree

Program Overview

---

The Master of Science in Financial Technology and Analytics equips STEM students with the skills needed to succeed in the financial industry. Through its concentrations in Financial Technology and Financial Data Science, students develop expertise in data analytics, financial modeling, and risk management. The program fosters meta-learning skills, providing graduates with the adaptability and problem-solving abilities essential for careers in the evolving financial landscape.

Program Outline

---

Degree Overview:

The Master of Science in Financial Technology and Analytics is a program designed to equip students with the skills and knowledge needed to succeed in the rapidly evolving financial industry. The program emphasizes the intersection of finance and technology, focusing on areas like financial technology, data science, and advanced analytical modeling.

---

Objectives:

• Job Readiness: The program aims to prepare graduates for immediate employment in the finance industry, equipping them with the in-demand skills sought by top employers.
• Wall Street Expertise: Students gain hands-on experience with the latest technologies used by Wall Street firms and global financial institutions, providing them with a competitive edge in the job market.
• Personalized Learning: The program allows students to customize their degree through a strategic selection of electives, ensuring their educational journey aligns with their individual goals and career aspirations.
• Professional Networking: Faculty members maintain close ties with the finance industry, connecting students with valuable professional networks and opportunities.
• Meta-Learning Skills: The program fosters the development of meta-learning skills, including research abilities, communication prowess, and data-driven problem-solving techniques.
• Fintech Research: Students have the opportunity to engage in research at the Center for Research toward Advancing Financial Technologies (CRAFT), a collaboration between Stevens and Rensselaer Institute of Technology (RPI).

---

Outline:

---

Program Structure:

• The program consists of core courses, concentrations, and elective courses.
• Students are required to choose one of two concentrations: Financial Technology or Financial Data Science.
• The program culminates in a capstone consulting or research experience.

---

Course Schedule:

• The program can be completed on campus or fully online.
• The program is designed for STEM students looking to pursue careers in the financial industry.
Topics include data abstractions, integration, enterprise-level data issues, data management, similarity and distances, clustering methods, classification methods, text mining, and time series. The Hadoop-based programming framework for big data is introduced, along with governance and policy issues.
• FE 513 Practical Aspects of Database Design (1 Credit): This course introduces data science techniques, database and data analysis tools, and teaches students to manage databases and solve financial problems using R.
• FA 590 Statistical Learning in Finance (3 Credits): This course provides an applied overview of classical linear approaches to statistical learning and modern statistical methods.
Topics include logistic regression, linear discriminant analysis, k-means clustering, nearest neighbors, generalized additive models, decision trees, boosting, bagging, and support vector machines.
• FA 800 Project in Financial Analytics (3 Credits): This course is designed for students undertaking a research or analytical project individually or as a group.
The goal is to train students' ability to work on research-oriented projects in a group environment and develop professional presentation and scientific writing skills.

---

Concentrations:

---

Financial Technology Concentration:

• FA 595 Financial Technology (3 Credits): This course deals with the networking and machine-learning technologies underlying market activities, institutions, and participants.
The goal is to provide students with a working understanding of the technological tools that permeate modern life.
• FA 591 Blockchain Technologies and Decentralized Finance (3 Credits): This course introduces blockchain technologies as they apply to decentralized finance.
Topics include cryptocurrency, smart contracts, risk management concepts, stable coins, and regulatory impacts.
• FA 596 Digital Payment Technologies and Trends (3 Credits): This course introduces students to up-to-date payment systems and innovative financial technologies (fintech) in the payment systems.
Topics include domestic and cross-border payment systems, blockchain technology as a form of digital payments, smart contracts, and Non-Fungible Tokens (NFTs).

---

Financial Data Science Concentration:

• FA 541 Applied Statistics with Applications in Finance (3 Credits): This 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.
• FE 535 Introduction to Financial Risk Management (3 Credits): This course deals with risk management concepts in financial systems.
Topics include identifying sources of risk, classification of events, probability of undesirable events, risk and uncertainty, risk in games and gambling, risk and insurance, hedging and the use of derivatives, Bayesian analysis, portfolio beta and diversification, active management of risk/return profile, propagation of risk, and risk metrics.
• FA 542 Time Series with Applications in Finance (3 Credits): This course teaches students how to estimate financial data models and predict using time series models.
Topics include 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, and multivariate time series models (VAR).
• FIN 620 Advanced Financial Econometrics (3 Credits): This course covers the main topics of time series analysis to evaluate risk and return of capital market products.
Students work with historical databases, conduct analysis, and conduct tests based on the techniques reviewed during the class.

---

Capstone Course or Master's Thesis:

• FA 800 Project in Financial Analytics (3 Credits): Students can complete a research or analytical project individually or as a group, often on projects offered by industry partners.
• FA 900 Master's Thesis in Financial Analytics (3 Credits): Students can complete a thesis option, working individually on a project that prepares them for a Ph.D. degree in Data Science.

---

Assessment:

---

Assessment Methods:

• The program utilizes a variety of assessment methods, including:
• Assignments:
Students complete assignments to demonstrate their understanding of course concepts.
• Exams: Exams are used to assess students' knowledge and comprehension of course material.
• Projects: Students work on individual or group projects to apply their skills and knowledge to real-world scenarios.
• Presentations: Students present their work to their peers and faculty.
• Capstone Project/Thesis: The capstone project or thesis provides a comprehensive assessment of students' abilities.

---

Assessment Criteria:

• Academic Performance: Students are evaluated based on their performance in coursework, projects, and presentations.
• Critical Thinking: Students are assessed on their ability to analyze complex problems, develop creative solutions, and communicate their findings effectively.
• Technical Skills: Students are evaluated on their proficiency in using data analysis and visualization tools, programming languages, and financial modeling techniques.
• Professionalism: Students are expected to demonstrate professionalism in their communication, teamwork, and work ethic.

---

Teaching:

---

Teaching Methods:

• The program employs a variety of teaching methods, including:
• Lectures:
Faculty deliver lectures to introduce key concepts and theories.
• Discussions: Students engage in discussions to explore different perspectives and deepen their understanding.
• Case Studies: Students analyze real-world case studies to apply their knowledge to practical situations.
• Hands-on Projects: Students work on hands-on projects to develop their technical skills and problem-solving abilities.
• Guest Speakers: Industry professionals share their insights and experiences with students.

---

Faculty:

• The program is taught by experienced faculty members who are experts in their fields.
• Faculty members maintain close ties with the finance industry, providing students with valuable insights and connections.

---

Unique Approaches:

• Meta-Learning Focus: The program emphasizes the development of meta-learning skills, which are essential for success in the rapidly changing financial industry.
• Industry Partnerships: The program has strong partnerships with industry leaders, providing students with opportunities for internships, research projects, and networking.
• State-of-the-Art Facilities: Students have access to state-of-the-art financial labs equipped with the latest data analysis and visualization tools.

---

Careers:

---

Potential Career Paths:

• Data Analyst
• Data Engineer
• Financial Specialist
• Quantitative Researcher
• Quantitative Risk Analyst
• Risk Management Analyst
• Trading Analyst

---

Opportunities:

• Graduates of the program are well-equipped to work in both start-ups and established financial firms.
• They can 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 can also construct innovative financial products and apply their expertise to a range of general financial services analytics.

---

Outcomes:

• The program has a 100% employment rate six months after graduation.
• Graduates earn an average compensation of $95,600 and an average signing bonus of $13,667.

---

Other:

• The program is available on campus or fully online.
• The program is designed for STEM students looking to pursue careers in the financial industry.
• The program has strong partnerships with industry leaders, providing students with opportunities for internships, research projects, and networking.
• Students have access to state-of-the-art financial labs equipped with the latest data analysis and visualization tools.
• The program emphasizes the development of meta-learning skills, which are essential for success in the rapidly changing financial industry.
• The program has a 100% employment rate six months after graduation.
• Graduates earn an average compensation of $95,600 and an average signing bonus of $13,667.