TEXT ANALYTICS AND BUSINESS APPLICATION

COMPSCI 769

Develop algorithms and mathematical models for natural language processing tasks, including text categorization, information retrieval, speech recognition, machine translation, and information extraction. Focus is on the state-of-the-art computational techniques as they are applied to natural language tasks. Students are strongly encouraged to have knowledge of introductory artificial intelligence (e.g.,COMP SCI 540).

GENBUS 891

An introduction to text mining and generation for business applications. Includes an overview of text data and approaches for making text data useful for descriptive and predictive analytics. Also, includes key applications of natural language processing, such as chatbots and recommender systems.

INFOSYS 365

Exploration of subject areas possibly to be introduced into the business curriculum.

INFOSYS 423

An introduction to data analysis procedures covering topics including: data collection, pre-processing, analysis, and presentation. Content covers important data analysis concepts, such as structured and unstructured data. Learn about supervised, unsupervised learning methods and advanced analytics topics, such as text mining, recommendation systems, and algorithm bias.

LIS 501

Introduces computational methods and tools for processing, analyzing, and understanding text data. Topics include text data preparation and preprocessing, models of text content and meaning, exploratory text analytics, text classification, information extraction from texts, ethical issues in natural language processing (NLP), and related applications in information sciences and other fields. Develops practical skills to design and implement text mining solutions using popular NLP tools and programming packages.