Develop and practice three aspects of successful communication: writing, presenting, and listening. A theoretical foundation provides a method of deep audience analysis; apply that analysis when producing a variety of written genres and when preparing content for formal presentation. Through a collaborative workshop process, revise your own written work. Enroll Info: None
Development of quantitative intuition through practical applications and use of analysis tools. Specifically, emphasis will be on how to manage, summarize, explore, and visualize databases. The essentials of probability will be introduced and applied to decision problems where there is uncertainty. Emphasis on hypothesis testing and regression analysis and include an introduction to simulation methods. Throughout, attention will be paid to effective communication of data analysis. The use of business cases will connect the course material to both real world settings and recent advances in data analysis, including big data and data mining. Enroll Info: None
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.
Internship which allows students to augment their business education and gain professional experience in their major through related work experience. Enroll Info: Intended for undergraduates in the School of Business. Not available with firms who participate in the ACCT I S 600 internship. See listing on Accounting Dept. website.
A compact primer in statistics and an introduction to programming as a foundation for data-driven business analyses. The first part covers elementary concepts such as random variables, probability distributions, estimation, and ordinary least-squares regression. In the second part, the course exposes students to Python and R programming, including numerical and statistical packages that are relevant for practical applications in business. Enroll Info: None