An introduction to modern Data Analytics, Cloud Databases, and AI integration for Intelligence Analysis.
IA 340 provides a comprehensive introduction to modern data analysis, teaching students how to collect, organize, query, and quantitatively analyze data. Moving beyond basic spreadsheets, this course introduces the tools and techniques used in modern cloud and AI-assisted environments.
Students will learn how to set up an analytics workspace, structure and query relational databases, interact with NoSQL systems for social data, and apply artificial intelligence to practical data mining scenarios.
This course follows a modern data-analysis pipeline:
- Workspace & Foundations: Google Colab, Python, and generative AI integration.
- Relational Data: Relational database workflows and structural querying.
- NoSQL & Social Data: Document databases, unstructured data, and social media analysis.
- AI-Assisted Analysis: Vector embeddings, AI-assisted data mining, and applied insights.
- Final Project: End-to-end data collection, modeling, and discovery.
See the official JMU Intelligence Analysis Undergraduate Curriculum and the current university catalog for official IA 340 prerequisites and course descriptions.
Dr. Xuebin Wei
Associate Professor, James Madison University (Geography / Intelligence Analysis)
Email: weixx@jmu.edu
Official JMU Faculty Profile
Dr. Wei's research and teaching focus on data science, artificial intelligence, cloud computing, GIS/geospatial analysis, and social data analytics.
This repository (JMU-Data/IA340) contains the public course source and materials. It is designed for transparency and reuse. Private student data, grading operations, and internal Canvas details are managed securely outside of this public repository.