ITS 632 –Introduction to Data Mining

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COURSE DESCRIPTION:

The goal of the course is to introduce students to the current theories, practices, tools and techniques in data mining. Because many topics and concepts in data mining are learned most efficiently through hands-on work with data sets, we will spend time with software analyzing and mining data. The goal is to gain a better understanding of how data mining is applied and what is involved in data mining projects.

COURSE OBJECTIVES:

Upon completion of the course, students will be able to:

  • Explain how businesses can gain competitive advantage through the mining of data.
  • Describe when and how various data mining techniques should be applied.
  • Understand the basic process and mechanics of data mining.
  • Be able to make strategic recommendations based on data mining results.

COURSE STRUCTURE (Course Activity Examples):

  • Watch weekly lecture
  • Participate in class discussion via iLearn forums
  • Reading assigned texts
  • Complete quizzes based on assigned reading and lecture
  • Complete cases based upon a given scenario
  • Complete homework assignments from the text and other sources

COURSE EXPECTATIONS:

  1. Class Participation: Students are expected to:
  2. Be fully prepared for each class session by studying the assigned reading material and preparation of the material assigned.
  3. Participate in group discussions, assignments, and panel discussions.
  4. Complete specific assignments when due and in a professional manner.
  5. Take exams when specified on the attached course schedule

Syllabus Disclaimer:  This syllabus is intended as a set of guidelines for our course and the instructor reserves the right to make modifications in content, schedule, and requirements as necessary to promote the best education possible within conditions affecting this course. Any changes to the syllabus will be discussed with the students.

 

Week Date Content
1 7-Jan Chapter 1: Introduction
2 14-Jan Chapter 2: Data
3 21-Jan Chapter 3: Classification: Basic Concepts and Techniques
4 28-Jan Chapter 4: Classification: Alternative Techniques
5 4-Feb Residency Feb 1 – Feb 3 (Residency Session: UC Northern KY – Florence, KY)
6 11-Feb Chapter 5_1: Association Analysis: Basic Concepts and Algorithms (5.1: 5.4)
7 18-Feb Chapter 5_2: Association Analysis: Basic Concepts and Algorithms (5.5: 5.8)
8 25-Feb Chapter 6_1: Association Analysis: Advanced Concepts (6.1: 6.4)
9 4-Mar Chapter 6_2: Association Analysis: Advanced Concepts (6.5: 6.6)
10 11-Mar Chapter 7_1: Cluster Analysis: Basic Concepts and Algorithms (7.1: 7.3)
11 18-Mar Chapter 7_2: Cluster Analysis: Basic Concepts and Algorithms (7.4: 7.5)
12 25-Mar Chapter 8_1: Cluster Analysis: Additional Issues and Algorithms (8.1: 8.3)
13 1-Apr Chapter 8_2: Cluster Analysis: Additional Issues and Algorithms (8.4: 8.6)
14 8-Apr Chapter 9_1: Anomaly Detection (9.1: 9.3)
15 15-Apr Chapter 9_2: Anomaly Detection (9.4: 9.5)
16 22-Apr chapter 10: Avoiding False Discoveries

 

This course is a hybrid course with a required residency session:

Course Features

  • Lectures 0
  • Quizzes 0
  • Duration 50 hours
  • Skill level All levels
  • Language English
  • Students 0
  • Assessments Yes
Curriculum is empty
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$600.00 $400.00