IA 340: Data Mining, Modeling, and Knowledge Discovery in Databases

Module 1: Introduction to Data Mining & AI

Welcome to IA 340! In this first week, we will introduce the core concepts of the course, discuss the evolving landscape of AI in data analysis, and set the foundation for your analytical workspace.

Welcome / Syllabus Highlights

See the Syllabus for grading, attendance, communication, and AI policies.

Intelligence Analysis Technical Curriculum

These courses are complementary within the Intelligence Analysis curriculum (note: IA 342 does not depend on concurrent IA 340). To understand where you are, let’s look at how the three core courses fit together:

IA 340
(You are here)
Data Mining: Collect, organize, and quantitatively analyze data.
IA 342
Visual Analytics: BI / visualization helps humans understand data.
IA 343
Interactive and AI Analytics: Interactive and AI systems help humans and AI interpret data together.

Humans, AI Agents, and Teaching Stacks in GeoAI Education

In 2026, data analysis is fundamentally intertwined with Artificial Intelligence. We are moving from manual coding and querying to a hybrid approach.

UCGIS 2026 Presentation: Humans, AI Agents, and Teaching Stacks in GeoAI Education

Human vs. AI Capabilities

To effectively work with AI, you must understand the division of labor. AI can generate code, but students still need to understand data, context, verification, and workflow design.

🧠 Human Capability

  • Purpose: Defining what matters and why.
  • Care: Ethical boundaries and responsibility.
  • Judgment: Evaluating context and making critical decisions.

🤖 AI Capability

  • Speed: Instant synthesis and generation.
  • Scale: Processing massive amounts of information.
  • Memory: Retaining vast technical syntax and patterns.

Prompt → Context → Harness

How does this philosophy connect to modern data analysis? You need to master three core layers of interacting with AI:

1. Prompt
Asking the right question. Define specific constraints and intent.
2. Context
Providing the environment. Supplying schemas, data samples, and constraints so the AI is grounded in your reality.
3. Harness
Building the workflow. Combining human judgment, AI generation, and reliable data infrastructure into repeatable tools.

Fall 2026 Semester Journey

Here is a high-level look at our learning progression this semester:

1

Workspace / Cloud Data

Tools: Google Colab, Drive, GitHub

Major Skills: Cloud foundation, object storage, version control

Expected Output: A functioning, accessible cloud analytics environment

2

Python Analysis

Tools: Python, Pandas

Major Skills: Data manipulation, cleaning, scripting

Expected Output: Processed, structured datasets ready for modeling

3

Relational Database / SQL

Tools: Cloud SQL databases

Major Skills: ER modeling, structured querying

Expected Output: Designing and retrieving data from tabular structures

4

MongoDB / Social & API Data

Tools: APIs, MongoDB

Major Skills: Semi-structured data collection, NoSQL aggregation

Expected Output: Scraping and storing rich, real-world web data

5

AI / Vector Analytics

Tools: GenAI APIs, Vector Embeddings

Major Skills: AI-assisted classification, semantic search

Expected Output: Using AI to interpret and query complex text at scale

6

Final Project

Tools: Combined Stack

Major Skills: Workflow design, independent problem solving

Expected Output: An end-to-end data pipeline discovering new insights

Week 1 Action Items

To get started, you only have one assignment this week. You will set up your GitHub account, link your JMU Google account for Google Colab/Drive, enroll in the Coursera AI Certificate program, and optionally explore the recommended Gemini AI Pro student offer. We are using a “just-in-time” onboarding approach, so we will set up Google Cloud and other tools later when we actually need them.

👉 Go to Week 1 Assignment: Account Setup & AI Program Registration

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