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Course Criteria
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3.00 Credits
This course provides an in-depth review of corporate governance, cost and managerial accounting, financial management, strategic planning, and information technology. These concepts correspond with the topics tested in the Business Environment and Concepts (BEC) section of the CPA Exam.
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3.00 Credits
This hands-on introductory course provides students with knowledge of the role of business analytics in modern business decision making and the skills necessary to utilize data and analytics to analyze business problems. The course begins with an overview of business analytics concepts, terminology, and tools. Students will understand the history of business analytics, how business analytics is used across a variety of industries, and the future of analytics in today's interconnected business environment. The three types of business analytics discussed are: descriptive, predictive, and prescriptive. In addition to understanding the environment and role of business analytics, students will gain hands-on knowledge of Excel's intermediate-level capabilities that support the use of data for decision making. The course includes the use of Excel referencing functions, pivot tables to slice and dice data, and graphs and charts to communicate results visually.
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3.00 Credits
This course will develop intermediate to advanced Excel skills using an applied focus on different types of decisions one may analyze using spreadsheet capabilities. The student will develop knowledge of how to evaluate a business process. Additionally, the art of modeling and the process of structuring and analyzing problems so as to develop a rational course of action will be discussed. The course includes the use of pivot tables to slice and dice data, and graphs and charts to communicate complex analytics visually. In addition, the course integrates advanced topics in business statistics such as linear and multiple regression and forecasting, linear programming, and simulation.
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3.00 Credits
Students in this course will utilize data modeling methodologies of least squares and logistic regression, as well as synthesize statistical results into an actionable set of findings and recommendations to guide business decision making. Students will build statistical models and implement regression analysis in real-world problems from business, economics, and marketing research and consumer behavior. Topics include multiple regression models utilizing first-order, second order, and interaction models with quantitative and qualitative variables, regression pitfalls, and residual analysis. Students will acquire skills not only in the mechanics of regression but also in deciding on appropriate models, interpreting results, and diagnosing problems.
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3.00 Credits
This course provides knowledge of how to choose an appropriate time series forecasting method, fit the model, evaluate its performance, and use it for forecasting. The course will focus on the most popular business forecasting methods: regression models, smoothing methods including Moving Average (MA) and Exponential Smoothing, and Autoregressive (AR) models. It will also discuss enhancements such as second-layer models and ensembles, and various issues encountered in practice.
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3.00 Credits
This course provides knowledge of how to develop, implement and use simulation methods for business decision making in the face of uncertainty. Students will build simulation models to answer what-if questions that are motivated by operational business decisions such as determining optimal inventory policies and deciding staffing levels for an organization. The course will utilize Microsoft Excel as well as Excel add-ins as modeling tools.
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3.00 Credits
In this course students will acquire a comprehensive understanding of how an organization can use its customer data to maximize the value of customer relationships. Businesses now have a wide array of tools to convert raw customer transactional data into usable marketing intelligence. Companies can identify, profile, analyze, and interact with both current and prospective customers on a personal basis. Topics covered include upselling and cross-selling, customer lifetime value, customer segmentation, predictive modeling, RFM analysis, customer loyalty and reward programs, and churn management.
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3.00 Credits
This introductory course in data mining will explore various statistical approaches to extract hidden knowledge from large data sets, enabling the analyst to discover complex relationships in data that might otherwise go undetected using manual analytics methodologies. The associated tasks of acquiring data from an enterprise data warehouse and preparing it for analysis, and the documentation of results will also be covered. Topics include building predictive models; exposure to logistic regression; machine learning and decision tree methods; and understanding lift factors and ROC curves. Hands-on use of mining software, business case studies, and a small data mining project will be used to reinforce the concepts.
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3.00 Credits
This course provides knowledge of the data sources, tools, and techniques used in the exploration and analysis of big data such as: text and stream mining, social media and big data, Hadoop, NoSQL, fundamentals of big data programming, cloud-based solutions, and visualization of big data using Tableau and GIS software. The course will utilize business case studies for students to understand big data solutions in the business environment.
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3.00 Credits
Students will gain knowledge of the most effective strategies for analyzing web and social media data generated by online activity. The course will examine social media analytical tools that enable organizations to understand what consumers and bloggers are saying about them, their products, and their competitors. Students will gain knowledge of web analytics to track and analyze the behavior of customers and browsers. Topics include extracting conclusions from abandoned shopping carts, RFM analysis, site usage, domains and URLs, keywords, and search engine placement.
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