APPLIED ANALYTICS - CASE STUDIES

GENBUS 760

Focuses on various technologies needed to perform data analytics. Techniques of extracting structured and unstructured data from databases, applications, or social networks. Transform and combine data with other relevant information and load into targeted systems. How to use programming languages to collect data from the web and leverage libraries for other, more, advanced data analysis.

GENBUS 895

Complete a team project as a capstone to graduate studies in business analytics. Use analytics and project management to develop a solution to a provided business case.

GENBUS 306

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.

GENBUS 720

Introduce students to principles of data visualization and provide hands-on experience using data visualization tools and techniques for business applications. Develop proficiency in current visualization software tools, and leverage these tools for data exploration, insight into decision-making, and data presentation. Recommended for students to have general computing skills and familiarity with MS Word, MS Excel and MS PowerPoint; introductory-level exposure to coding in any language; some R experience; basic statistical literacy, equivalent to at least one semester of statistics.

GENBUS 883

Provides experience in data visualization and cloud technologies to support business analytics. Create and share compelling data visualizations to enhance decision making. Use cloud technologies to build scalable data warehouses, analyze big data, and develop and deploy machine learning models.