EMIS, Lyle School of Eng., SMU

EMIS/CSE 5/7331: Data Mining, Fall 2017

Dr. Michael Hahsler

> Course Syllabus
> Assignments on Canvas

Course Description

Analytics is based on collecting, managing, exploring and acting on large amounts of data and has become a source of competitive advantage for many organizations. This course provides an overview of descriptive analytics and introduces major data-mining techniques (classification, association analysis and cluster analysis) used in predictive analytics. All material covered will be reinforced through hands-on experience using state-of-the art tools to design and execute data mining processes.

Prerequisites: Introduction to programming (CSE 1342 or any programming language), probability and statistics (CSE 4340/EMIS 3340/STAT 4340 or CSE 7370/EMIS 7370), databases (EMIS 3309 or CSE 3330, requirement for undergraduates only).

Outline

  1. Introduction
  2. Data and Exploration
  3. Classification
  4. Association Analysis
  5. Clustering
  6. Advanced Topics

Textbooks

Projects

Project assignments are available on Canvas.

Models, Code and Data Used in Class

Tool

Learning resources

Data Sets


Michael Hahsler, Lyle School of Engineering, SMU
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