ISMG 6470 - Text Data Analytics

This course includes two topics. The first topic covers algorithms and tools to perform quantitative analysis of unstructured text data. Concepts and algorithms that will be introduced in the class include Zipf’s Law, Power Law Distribution, Pattern Discovery (using algorithms of Entropy, Inverse Document Frequency, Clustering etc.), and Machine Learning etc. SAS Enterprise Miner/Text Miner will be introduced as a practice tool to carry out quantitative analysis of unstructured text data. By using the SAS Text Analytics software, students will learn the skills to uncover underlying themes and concepts contained in a large text document corpus. The second topic covers seminal theories and practical methods necessary to perform qualitative analyses of text data. Many qualitative research methods using text data (e.g., grounded theory, ethnographic study, case study etc.) will be introduced. NVivo 11 software will be used as a practice tool to conduct qualitative analyses of text data. Restrictions: Restricted to graduate majors and NDGR majors with a sub-plan of NBA within the Business School, graduate majors within the College of Engineering, Design and Computing, PHCS PhD majors and PhD majors. Max Hours: 3 Credits. Semester Hours: 3 to 3