ITS 632 –Introduction to Data Mining
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.
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
- Class Participation: Students are expected to:
- Be fully prepared for each class session by studying the assigned reading material and preparation of the material assigned.
- Participate in group discussions, assignments, and panel discussions.
- Complete specific assignments when due and in a professional manner.
- 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.
|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:
- Lectures 0
- Quizzes 0
- Duration 50 hours
- Skill level All levels
- Language English
- Students 0
- Assessments Yes