Minor in Financial Analytics

This course covers the fundamental skills for business analytics: compiling summary statistics, data visualization, descriptive data mining and statistical methods such as hypothesis testing and linear regression. Students gain experience with widely used software tools and learn to report analysis in a presentable format. Topics covered will be useful for further analytical studies in financial analytics, econometrics, marketing analytics, HR analytics, health care analytics and supply chain analytics. Pre-requisite: MA-131/MA-133/MA-331
Prerequisite: Take MA-131or MA-331

This course is a primer on Python programming language for students with little or no prior programming knowledge. In this course, students will start from the fundamentals of Python programming, including developing environment, language syntax, variable types, data structures, functions and control flow. Along the way, students will work with dedicated libraries for data science, such as Pandas, NumPy, SciPy, MatPlotLib, that allow students to develop analytics on data to solve challenging business problems. This course places emphasis on hands-on learning experiences that allow students to experiment with trial and error, and learn from their mistakes.

This is an introductory course on Database concepts and Structured Query Language (SQL) with a focus on business applications. This course provides a step-by-step overview and instructions on a variety of topics, including database and database management system (DBMS), data types, table structure and relationships, field definitions and naming conventions, data access queries using SQL, data definition language (DDL) and data manipulation language (DML) commands; table creation using SQL statements, database security and evolution of data models. No prior database or programming experience is required.

Python is a very powerful programming language that is widely used in the financial industry. This course will equip students with the programming knowledge and skills in Python to solve practical problems in quantitative finance. Specifically, students will learn how to conduct time-series and cross-sectional analyses with real-world financial data, apply and implement financial models in Python, formulate and code up simple algorithmic trading strategies, and eventually evaluate that strategy's performance and test its robustness. This course includes multiple hands-on coding exercises and projects with a focus on financial applications. Some familiarity with basic statistical and programming (in any language) concepts is necessary. Pre-requisite: BUAN 301
Prerequisite: Take BUAN-301 or CS-111

Provides an overview of the principles and techniques used in financial management and an introduction to financial markets. Topics include time value of money, measures of risk, models for pricing bonds and stocks, financial analysis, capital structure, cost of capital, capital budgeting, and working capital management. Students are introduced to financial problem-solving using Microsoft Excel.
Prerequisite: Take AC 221