Business Analytics

This course covers key concepts related to predictive and prescriptive analytics by combining information technologies and statistical techniques to extract meaning from organizational data. The course includes hands-on work with data and software. Topics covered include data manipulation, decisions under uncertainty, and decision analytics tools (linear and nonlinear optimization). Students apply predictive and prescriptive analytics techniques in order to understand the business environment and guide business-related decisions. Fall even years. Prerequisite: BUSN 260 Business Analysis Tools; must be a junior or senior or have permission from the instructor.

This course introduces advanced multivariate regression analysis and residual diagnostics, logistic regression, analysis of variance (ANOVA and MANOVA), time series models, and analysis of categorical variables. Business applications involving multiple explanatory and response variables require advanced statistical models that go beyond the basic inferential tools (e.g., confidence intervals and hypothesis tests). Fall even years. Prerequisite: BUSN 270; must be a junior or senior or have permission from the instructor.

This course focuses on business intelligence as an information technology approach to data collection and data analysis to support a wide variety of management tasks, from performance evaluation to trend spotting and policymaking. Students learn analytical components and technologies used to create dashboards and scorecards, data/text/Web mining methods for trend and sentiment analysis, and artificial intelligence techniques used to develop intelligent systems for decision support. Spring odd years. Prerequisite: must be a junior or senior or have permission from the instructor.

This course focuses on how computer models support managerial decision-making. The aim of the course is to help students become intelligent users and consumers of these data models. The course will cover the basic elements of data modeling, how to formulate a model, how to use and interpret the information a model produces. The course emphasizes "learning by doing" so are expected to formulate, solve, and interpret a number of different optimization and simulation models using software. An important theme in the course is to understand the appropriate use of data models in business. Spring odd years. Prerequisite: must be a junior or senior or have permission from the instructor.

In this course students learn how to apply fundamental programming concepts, computational thinking, and data analysis techniques to solve real-world data science problems. There is a rising demand for people with programming skills to work with Big Data sets and this course introduces students to a number of programming languages and software packages specifically designed for data analytics. Spring even years. Prerequisite: must be a junior or senior or have permission from the instructor.

Data visualization is the graphical representation of information and data. This course focuses on building skills and strategies to recognize trends, outliers, and patterns for a better understanding of real-world big data problems. Students use data visualization tools to design charts, graphs, and maps to analyze massive amounts of information and make data-driven decisions. Spring even years. Prerequisite: must be a junior or senior or have permission from the instructor.

This course introduces the languages, applications, and programming used to design and maintain business databases. Students gain an understanding of database models and environments as they learn to manage database components. Topics discussed include data input, data sorting, database troubleshooting, and database security. These skills prepare students to plan, design and set-up relational, network and object-oriented databases. Students also learn to perform and design database functions like report generation, data analysis using multiple constraints, data recovery and transfer and maintenance of data consistency and integrity. Fall odd years. Prerequisite: must be a junior or senior or have permission from the instructor.

This course provides an overview of the principles and techniques of data mining. Topics covered include the data mining process, data preprocessing, data mining techniques and data mining evaluation. The course will involve a combination of lectures, labs, projects, and case studies. Data mining is the science of discovering structure and making predictions in large, complex data sets. The course introduces the basic concepts, principles, methods, implementation techniques, and applications of data mining, with a focus on two major data mining functions, pattern discovery and cluster analysis. Fall odd years. Prerequisite: must be a junior or senior or have permission from the instructor.