AI for Text Analytics and Insights

About This Course

This module teaches students a structured text analytics pipeline. Students begin with classical techniques: preprocessing, feature extraction using methods such as TF-IDF and word frequency analysis, and classification models for scalable tasks such as sentiment scoring, topic detection, and entity recognition across large volumes of text. Students then learn when and how to apply LLMs for tasks where context and nuance matter such as summarizing themes across document collections, semantic search, and contextual interpretation that classical methods cannot capture. Students learn to construct effective context for LLM-based text tasks by selecting and assembling relevant document segments, maintaining analytical memory across multi-step queries, and structuring inputs that ground model outputs in the source material. Given a body of unstructured text, students learn to scope the task (volume, complexity, business question), apply classical methods first across the full dataset to establish patterns at low cost, then apply LLMs selectively where deeper interpretation is needed. Students verify findings through ground truth sampling, multi-model comparison, and manual inspection of flagged disagreements between methods. Students learn to select and justify the right technique for each task based on scale, cost, accuracy requirements, and explainability.