Program Overview
Financial Analytics - M.S.
The M.S. in Financial Analytics equips students to make data-driven decisions at the intersection of finance, technology, and analytics—preparing them for high-impact careers across Wall Street, fintech, and beyond.
Why Choose the M.S. in Financial Analytics?
Finance today is fueled by data, algorithms, and automation. The Masters of Science in Financial Analytics is designed for students who want to lead this transformation. Unlike traditional finance programs, it combines rigorous financial theory with advanced computational methods—helping students model market behavior, predict investment outcomes, and quantify risk with precision.
Program Overview
Students explore both the art and science of finance, developing skills in corporate finance, econometrics, machine learning, and portfolio analytics. Graduates leave prepared to apply predictive modeling, algorithmic trading, and AI tools to real-world financial challenges—from equity valuation to risk forecasting.
Curriculum
The program's curriculum emphasizes hands-on experience—students work with Bloomberg terminals, Python, R, SQL, Tableau, and proprietary financial databases used by leading banks, hedge funds, and regulators. Coursework builds progressively from corporate finance fundamentals to advanced econometric modeling and machine learning applications in financial markets.
Featured Courses
- FINAN 600 – Corporate Finance | 3 credits
- FINAN 602 – Investment Analysis | 3 credits
- FINAN 604 – Econometrics: Theory & Applications | 3 credits
- FINAN 650 – Real Estate Finance | 3 credits
- FINAN 660 – Financial Econometrics | 3 credits
- FINAN 670 – Advanced Fixed Income Analysis | 3 credits
- FINAN 625 – Machine Learning in Investment & Applications | 3 credits
- FINAN 629 – Behavioral Finance | 3 credits
- FINAN 611 – Futures & Forwards | 3 credits
- FINAN 617 – Options | 3 credits
- FINAN 651 – Capstone Project | 1 credit
- BUAN 607 – Data Visualization & Communication | 3 credits
- BUAN 610 – Data Mining Methods for Business Analytics | 3 credits
- BUAN 606 – Privacy & Data Protection | 3 credits
- BUAN 636 – Supply Chain Analytics | 3 credits
- BUAN 627 – Advanced Machine Learning & AI | 3 credits
- BUAN 605 – Programming for Business Analytics | 3 credits
- BUAN 620 – Data Management for Business Analytics | 3 credits
Admission Requirements
Applicants to the M.S. in Financial Analytics program must have:
- An undergraduate bachelor's degree (4-year degree or equivalent) from an accredited college or university.
- The following documents are required when submitting an application:
- Application form
- Official Transcript(s)
- A resume that includes examples of academic, co-curricular, and extracurricular achievement.
- One letter of recommendation attesting to the applicant's intellectual ability, leadership potential, and ability to complete the program.
- Official GMAT or GRE equivalent score of at least 500 is preferred (waivers may apply).
Professional Opportunities
The program's industry alignment ensures that graduates are ready to meet the growing demand for data-literate financial professionals. Students are prepared for roles such as Quantitative Analyst (Quant), Financial Data Scientist, Risk Manager, Investment Strategist, or Fintech Product Analyst.
Program Format
The M.S. in Financial Analytics program is offered in a Low-Residency format with most courses online with asynchronous content and weekly "live" virtual sessions. It is ideal for anyone looking to obtain their Master's while working full-time. This program can be completed in one year, with classes taken full time in the fall, spring, and summer semesters.
Skills and Knowledge
The curriculum integrates the analytical rigor of finance with the computational depth of data science. Students gain fluency in financial econometrics, machine learning, data visualization, and AI-driven decision-making while mastering tools used by leading institutions. Students will learn to price assets, model volatility, assess risk exposure, and evaluate investment performance through advanced quantitative methods. With exposure to emerging areas like ESG investing, behavioral finance, and fintech innovation, graduates develop a holistic view of finance's evolving landscape.