8000

Course covers the essential exploratory technique for summarizing data.  Modeling techniques covered include predictive modeling, also known as predictive analysis; cluster analysis, segmenting, affinity analysis, and applied predictive analytics.  Course is dependent on a sound understanding of statistical analysis techniques, such as regression analysis and hypothesis testing.  Prerequisite:  Undergraduate statistics course.

Delve into prescriptive analytics techniques to understand and improve a firm's organizational processes.  Specifically, diversity analytics, predicting employee turnover, predicting employee performance, recruitment analytics, and intervention impact will be covered by using analytics process models.

This course explores customer data analysis techniques and their theoretical foundations to help students acquire analytic skills that can be applied to real-world market-related problems from empirical data in such areas as segmentation, market positioning, satisfaction management, customer lifetime analysis, customer choice, and drive marketing maximization, effectiveness and optimization return on investment (ROI).  The course covers how to analyze data to understand customers and inform marketing decisions, evaluate the quality and usefulness of available data and analyses conducted by others, and communicate analysis-based conclusions to colleagues and managers.

Explore techniques to understand and improve businesses operational capabilities.  Process Analytics, focusing on individual processes to improve method performance, quantify the impact of randomness, and visualize process quality is covered.  The course analyzes issues related to the design and management of the Supply Chains using quantitative tools such as linear, integer, and non-linear techniques, regression, and statistical analysis to forecast uncertain demand, optimize inventory, and design distribution networks to ensure the supply demand.

This course provides foundational skills for exploring both structured and unstructured business problems. The course will cover techniques to systematically approach decision-making, enabling students to become more comfortable handling tasks or projects that are not initially well-defined. Methods will include exercises in brainstorming and iterating as well as the use of more traditional analytical tools. The course will offer applications across different functional areas and disciplines. After the course, a student should understand a broader set of the types of problems that analytics professionals confront and solve, the data need to analyze such problems, and modeling/analytic techniques used to solve them. Throughout the course, a hands-on approach and communication of results are emphasized.

Students participate in a seminar led by a faculty member.  Students are also required to be employed in a training position related to their concentration.  Coursework in the seminar is directly tied to the performance of activities of a regularly scheduled employee in operations and the student must complete a minimum of 80 contact hours with the employer during the practicum course.

Students participate in a seminar led by a faculty member.  Students are also required to be employed in a training position related to their concentration.  Coursework in the seminar is directly tied to the performance of activities of a regularly scheduled employee in operations and the student must complete a minimum of 80 contact hours with the employer during the practicum course.

Course provides end-to-end coverage of cloud computing topics as they pertain to both technology and business considerations.  Topics include fundamental cloud computing terminology and concepts; challenges and risks of contemporary cloud computing platforms and cloud services; cloud delivery and calculating cloud on premise solution costs and service level agreements (SLAs) for cloud based IT resources.  Course includes hands-on exercises in topical areas.

Course focuses on the design, development, and structuring of programs that store large collections of data. Topics include the development and implementation of software solutions to enhance business needs.  Several types of databases may be used, including relational and multimedia.  Also covered are programming languages such as Python and Structured Query Language (SQL) to create databases, manipulate big data sets, and generate reports.

Continuation of previous CPT practicum.