DATA ANALYTICS FOR FINANCE

ECON/FINANCE 300

Concepts and techniques in corporate finance and investments. Topics include the financial environment, securities markets, financial markets, financial statements and analysis, working capital management, capital budgeting, cost of capital, dividend policy, asset valuation, investments, decision-making under uncertainty, mergers, options, and futures.

STAT 801

Statistical theory and methodology for modern financial data. Topics include financial stochastic models based on time series and stochastic calculus, modern statistical inference, and statistical learning for financial data as well as their applications to financial problems.

ECE 204

A hands-on introduction to Data Science using the Python programming language. Data-centric and computational thinking. Describe, analyze, and make predictions using data from real-world phenomena. Programming in Python. Importing, manipulating, summarizing, and visualizing data of various types. Notions of bias, fairness, and ethics in data science.

ACCTIS 640

Analytics has become an integral part of accounting to support decision making, coordination and control, and compliance with regulations. Being able to identify and pursue relevant questions using data is a critical skill for new hires, as is the ability to successfully communicate one's findings. This pursuit and communication requires a strong facility with a variety of analytics technologies.

GENBUS 307

Emphasis on hands-on experience with many commonly used analytic methodologies using the modeling and optimization tools available on almost every professional desktop. The focus is predictive and prescriptive analytics. Predictive approaches use historical data to infer causal relationships and forecast future outcomes from a given action. Prescriptive methods take this a step further, helping managers formulate decision models that identify optimal actions given a set of circumstances.