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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.

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.

 

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In One Year, Advance Your Career

This STEM-designated hybrid program is tailored for the next generation of quantitative analysts, investment professionals, and fintech innovators. Students can complete the 30+ credit curriculum in as little as one year, combining in-person and online courses for maximum flexibility.

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.

Through simulations, case studies, and applied research, students gain firsthand insight into the analytics that power financial decision-making. The capstone project challenges them to synthesize technical, analytical, and strategic skills to address a complex financial problem faced by real institutions.

  • Professional Opportunities

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    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.

    With a focus on employability, the program leverages Manhattan University’s connections with investment banks, asset management firms, consulting agencies, and regulatory bodies. Graduates are equipped not only to interpret complex data—but to lead teams that make critical financial decisions in today’s algorithmic economy.

  • Find Learning That Matches Your Lifestyle

    The M.S. in Financial Analytics is offered in a hybrid format, combining the flexibility of online coursework with opportunities for in-person learning and networking.

    Students can pursue the degree full-time and complete it in one year or study part-time while maintaining professional commitments. Each course is designed for immediate workplace application, enabling students to upskill while they study.

  • Build Valuable Skills

    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.

  • Featured Courses: M.S. in Financial Analytics
    COURSE Name credits Earned
    BUAN 605: Programming for Business Analytics 3
    BUAN 610: Data Mining Methods for Business Analytics 3
    BUAN 620: Data Management for Business Analytics 3
    FINAN 600: Corporate Finance 3
    FINAN 602: Investment Analysis 3
    FINAN 604: Econometrics: Theory & Applications 3
    FINAN 609: Hedge Funds 1.5
    FINAN 611: Futures & Forwards 1.5
    FINAN 613: Venture Capital 1.5
    FINAN 615: Private Equity 1.5
    FINAN 617: Options 1.5
    FINAN 619: Measurement & Management of Market Risk 1.5
    FINAN 621: Measurement & Management of Credit Risk 1.5
    FINAN 623: Measurement & Management of Operational Risk 1.5
    FINAN 625: Machine Learning in Investment & Applications 1.5
    FINAN 627: Special Topics: ESG Investing 1.5
    FINAN 629: Special Topics: Behavioral Finance 1.5
    FINAN 631: Special Topics: Convertible Bonds & Restructuring 1.5
    FINAN 640: Mergers
    FINAN 650: Real Estate Finance 3
    FINAN 651: Capstone Project 3
    FINAN 660: Financial Econometrics 3
    FINAN 670: Advanced Fixed Income Analysis 3
  • Admission Requirements

    Applicants to the M.S. in Financial Analytics online program: 

    • Must have undergraduate bachelor’s degree (4-year degree or equivalent) from an accredited college or university is required to apply to the program. 
    • Should have a cumulative GPA of 3.00 (on a 4.0 scale) and must have at least 36 semester hours of business classes. 

    Required Documents

    The following documents are required when submitting an application, in addition to a $75 non-refundable application fee:

    • Application form
    • Official Transcript(s)
    • A resume that includes examples of academic, co-curricular and extracurricular achievement, which can be used to assess personal qualities and ability to complete the program.
    • 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). 

    Start Your Application

Meet the Faculty

  • Browse Faculty

    More than instructors, our faculty bring extensive experience from investment banking, risk management, and fintech innovation. Their teaching integrates practical market insights with cutting-edge research in computational finance.