AA 620. Data Mining and Predictive Analytics. 3 Credit Hours.
This course will focus on applying data mining methodologies and predictive analytics tools to extract useful patterns from large bodies of data and on interpreting the results in order to take reasoned action to solve problems. Students will work with large data sets from organizations in several diferent domains and analyze the data using SAS Enterprise Miner. Topics covered include: introduction to data mining concepts, data mining applications, the data mining process, profiling and predictive modeling, decision trees, neural networks, cluster analysis, association analysis and text mining. Students will also be introduced to visualization techniques and applications. An emphasis in this course will be placed on segmentation strategies and techniques.
|Spring 2021||AA 620||AG||3527||T||6:00pm - 9:00pm||(T. Dougherty)|
|Spring 2021||AA 620||AG2||3726||Th||6:00pm - 9:00pm||(T. Dougherty)|
AA 630. Data Management and Large Scale Data Analysis. 3 Credit Hours.
This course is an introduction to the principles and techniques for data acquisition, storage and management. In this couse, students will learn how data is stored, accessed, and eventually analyzed. Basic components of database systems, and how data is accessed using SQL will be discussed. The design considerations for more comprehensive data storage systems such as Data Warehouses and Hadoop will also be covered. Lastly, the course will discuss representation methods and techniques that increase the understanding of complex data. Emphasis will be placed on the identification of patterns, trends and differences from data sets across categories, space, and time. SAS Enterprise Miner and Visualization Anaytics will be used during this course.
|Spring 2021||AA 630||AG||3713||W||6:00pm - 9:00pm||(J. Prichard)|
|Spring 2021||AA 630||AG2||3727||M||6:00pm - 9:00pm||(J. Prichard)|
AA 640. Advanced Analytics Techniques and Data Visualization. 3 Credit Hours.
This course will expose students to advanced analytics techniques using unstructured data. Students will understand the challenges of working with unstructured data such as text and images. Students will gain hands on experience completing projects that involve techniques including text mining, sentiment analysis, topic analysis and feature extraction. The second half of the course will focus on data visualizations and the techniques to implement efficient and effective visualizations. The challenges of storytelling through the use of visualizations will be emphasized.
|Summer 2021||AA 640||AG||4445||TTh||6:00pm - 9:30pm||TBD|
AA 651. Analytics Capstone. 3 Credit Hours.
The Analytics Capstone course provides students with the opportunity to apply the knowledge and skills that they have acquired during the GCBA to realistic problems that involve very large data sets ("Big Data"). In addition to using the techniques students have learned in the previous courses, students will be introduced to other important topics related to Big Data such as Hadoop, map-reduce, association rules, large scale supervised machine learning, streaming data, clustering algorithms, and NoSQL systems (Cassandra, Pig, Hive), as well as SAS software packages. The course will culminate with a final project based on a large data set. Students will present the results of their analysis and recommendations to other students in the class and where appropriate to the organization that sponsored the project. Topics in project management will be presented during the course to help students organize their capstone project.
|Summer 2021||AA 651||AG||4438||TTh||6:00pm - 9:30pm||(T. Dougherty)|
AA 691. Directed Independent Study in Applied Analytics. 3 Credit Hours.
This course is designed to allow an individual academic program to be tailored to fit the unique interest of a graduate student. At the initiation of the graduate student, the faculty member and the student will develop an academic plan that is submitted to the College of Business for final approval.