Unstructured Data Analytics

(1.5 Credit Hours) · Undergraduate

This course introduces students to the analysis of unstructured data, including text, documents, and other data formats that do not conform to traditional relational database structures. Unstructured data is a dominant source of information in modern organizations and plays a central role in analytics, machine learning, and AI-enabled systems. Students learn how to identify, collect, and prepare unstructured data from sources such as emails, social media, online reviews, and business documents. The course covers foundational analytic techniques for extracting information from unstructured data, including text preprocessing, word counts, document-term representations, and classification methods. Students are also introduced to deep learning approaches for unstructured data and the role of generative AI models in tasks such as text generation, summarization, and information extraction. Through applied exercises and business-focused examples, students develop practical skills for transforming unstructured data into actionable insights. Emphasis is placed on understanding how traditional text analytics, deep learning, and generative AI techniques can be used responsibly to support automation, prediction, and data-driven decision making. Students are strongly encouraged to complete ITAO 40230 before enrolling in this course.