We know there’s a lot to consider when getting a graduate degree. That’s why we recently redesigned our Master of Finance degree to combine the best of in-person and online education. Flexible, seven-week courses let you balance your busy schedule while still reaping the benefits of once-per-week face-to-face classes, like networking, developing interpersonal skills, and participating in an immersive capstone experience.
The curriculum covers a variety of topics, ranging from practical business applications used to manage current financial challenges to advanced financial theories that can address future trends. You may pursue the new Financial Data Analytics option, which focuses on big data and analytical problems in finance and culminates in a capstone course that immerses students in real-world case analyses.
Our respected graduate degree program is fully accredited by the Association to Advance Collegiate Schools of Business (AACSB) International, a standing earned by less than five percent of the world’s business programs. The Master of Finance program helps prepare students for various professional certifications, such as the Chartered Financial Analyst® (CFA®) certification.
This program is STEM designated. A STEM designated degree from Penn State Great Valley allows 36 months total of Optional Practical Training (OPT) compared to 12 months for non-STEM degrees.
Students in the program learn about:
- Financial modeling, including capital budgeting, basic statistics, and forecasting
- Financial accounting and its principles, and use of accounting information for decision making
- Advanced topics involving strategic financial decisions, including capital structure and cost of capital, valuation, and corporate control
- Multinational financial management for companies subject to foreign exchange risk exposure and different tax regulations in foreign countries
- Financial derivative securities covering options, forwards, futures, and OTC derivatives
Students in the Financial Data Analytics option learn about:
- Machine learning and data mining tools used in the financial industry
- Advanced financial data visualization techniques
- Real-world applications and projects related to financial data analytics, such as
- Fraud detection in credit card companies
- Real-time credit evaluation
- Insurance companies’ pricing decisions for customers’ changing financial situations
- Market sentiment analysis to help make investment decisions