Faculty of Information Technology – Islamic University Gaza
Data Mining
SDEV 3304
Course Syllabus
General Information
• Semester: 2
Semester 2020.
• Department: Department of Software Engineering.
• Instructor: Dr. Iyad Husni Alshami,
• phone: 00970 8 2860700 Ext:2960
• email:
[email protected]• office hours: Saturday – Wednesday 11:00 – 13:00
• office location: I305
• Credits: 3Hrs.
• Meeting time and locations:
• 201: ST 8:00 – 9:30, I101
• 101: ST 9:30 – 11:00, I116
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Course’s Description
This course has been designed to give students an introduction to data mining and hands on experience
with all phases of the data mining process using real data and modern tools. It covers many topics such as data
formats, and cleaning; make prediction using supervised and unsupervised learning using Python and other tools,
and sound evaluation methods; and data/knowledge visualization.
Course’s Objectives
This course is designed to achieve a number of goals for each student such as:
• Providing the fundamental understanding of data mining in order to extract hidden knowledge.
• Exploring the different data mining tasks to extract knowledge:
o Classification,
o Clustering,
o Association Rules extraction, and
o Outlier detection.
• Practicing the data mining project phases
• Presenting the data in the early stage of data mining projects as well as the extracted knowledge.
• Provide the students the latest hot topics in data mining field.
• Strengthen the team work
Course’s Outcome
By the end of this course the students should be able to:
• Identify the meaning of data mining, describe the suitable data for data mining projects, list/identify at
least five different data mining tasks and evaluate the extracted knowledge for each task.
• Collect and prepare data set suitably for data mining projects.
• Use machine learning techniques to perform the different data mining tasks.
• Analysis and build data mining projects individually or as a team member/leader as well .
• Adopt the ethics of profession with the sensitive personal data
Text book & References
• Text Book: “Data Mining: Concepts and Techniques”, 2
edition by Jiawei Han and Micheline
Kamber, Morgan Kaufmann 2006.
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• Additional Books:
• “Data Mining – Practical Machine Learning Tools and Techniques”, 2
edition by Ian H. Witten
and Eibe Frank, Elsevier 2005.
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•
Course’s Outline “topics that will be covered”
Week
#
Topic
Notes
1
Introduction to Data Mining
2
3
Data Understanding and Data Preparation
Knowledge Extraction Using Machine Learning Techniques
4
5
6
Supervised Learning - Classification
7 Supervised Learning - Regression
8
9
Unsupervised Learning - Clustering
10
11
Unsupervised Learning - Association Rules
12 Unsupervised Learning - Outlier Detection
13 Data Visualization and Knowledge Representation
14 Data Science’s Hot Topics
15 Project Presentation and Discussion
Teaching methods
• Lectures,
• Discussion groups,
• Team work,
• Using Videos and Presentations
Evaluation criteria “Grades”
• 10% Quizzes & Assignments,
• 10% Participating in Course’s Activities
• 20% Midterm Exam
• 20% Final Project
• 40% Final Exam.
Course’s Tools
• PyCharm – Python 3.6
• Rapidminer Studio
Course’s Rules
• The course contents and grading can be changed as necessary.
• Missing more than 25% of lectures will provide you “W”.
• There is no predetermined schedule for quizzes.
• No excuses for missing the quizzes or the assignments.
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