COURSE INFORMATION Course Title: DATA MINING

Code	Course Type	Regular Semester	Theory	Practice	Lab	Credits	ECTS
CEN 571	B	2	3	2	0	4	7.5

Academic staff member responsible for the design of the course syllabus (name, surname, academic title/scientific degree, email address and signature)	NA
Lecturer (name, surname, academic title/scientific degree, email address and signature) and Office Hours:	Wasito Wasito , Friday, 14-16
Second Lecturer(s) (name, surname, academic title/scientific degree, email address and signature) and Office Hours:	NA
Teaching Assistant(s) and Office Hours:	NA
Language:	English
Compulsory/Elective:	Elective
Classroom and Meeting Time:	E314
Course Description:	Supervised learning and unsupervised clustering strategies in data mining: Data preprocessing techniques, decision trees, k-nearest neighbor, rough sets, genetic algorithms, fuzzy sets, k-means, neural-networks, Bayesian classifier, statistical techniques and association rules. Data mining in time series, text and web mining.
Course Objectives:	1. To equip the students with various Data Mining methods 2. To apply the Data Mining methods for various applications.

COURSE OUTLINE

Week	Topics
1	Introduction
2	Data Preprocessing
3	Data Reduction: Principal Component Analysis
4	Data Reduction: Independent Component Analysis
5	Data Visualisation
6	Classification Algorithm 1
7	Classification Algorithm 2
8	Clustering Algorithm 1
9	Clustering Algorithm 2
10	Mini Project Presentation
11	Mini Project Presentation
12	Mini Project Presentation
13	Mini Project Presentation
14	Final Project Presentation

Prerequisite(s):
Textbook:	Principal of Data Mining
Other References:
Laboratory Work:
Computer Usage:
Others:	No

COURSE LEARNING OUTCOMES

1	The students will be familar with the Data Mining Principal
2	The students be able to apply the various Data Mining applications
3	The students be able to implemented Data Mining in various research fields

COURSE CONTRIBUTION TO... PROGRAM COMPETENCIES (Blank : no contribution, 1: least contribution ... 5: highest contribution)

No	Program Competencies	Cont.
Master of Science in Computer Engineering (2 years) Program
1	Engineering graduates with sufficient theoretical and practical background for a successful profession and with application skills of fundamental scientific knowledge in the engineering practice.	4
2	Engineering graduates with skills and professional background in describing, formulating, modeling and analyzing the engineering problem, with a consideration for appropriate analytical solutions in all necessary situations	5
3	Engineering graduates with the necessary technical, academic and practical knowledge and application confidence in the design and assessment of machines or mechanical systems or industrial processes with considerations of productivity, feasibility and environmental and social aspects.	3
4	Engineering graduates with the practice of selecting and using appropriate technical and engineering tools in engineering problems, and ability of effective usage of information science technologies.	5
5	Ability of designing and conducting experiments, conduction data acquisition and analysis and making conclusions.	5
6	Ability of identifying the potential resources for information or knowledge regarding a given engineering issue.	5
7	The abilities and performance to participate multi-disciplinary groups together with the effective oral and official communication skills and personal confidence.	5
8	Ability for effective oral and official communication skills in foreign language.	5
9	Engineering graduates with motivation to life-long learning and having known significance of continuous education beyond undergraduate studies for science and technology.	3
10	Engineering graduates with well-structured responsibilities in profession and ethics.	3
11	Engineering graduates who are aware of the importance of safety and healthiness in the project management, workshop environment as well as related legal issues.	2
12	Consciousness for the results and effects of engineering solutions on the society and universe, awareness for the developmental considerations with contemporary problems of humanity.	2

COURSE EVALUATION METHOD

Method	Quantity	Percentage
Midterm Exam(s)	1	30
Presentation	2	10
Final Exam	1	40
Attendance		10
	Total Percent:	100%

ECTS (ALLOCATED BASED ON STUDENT WORKLOAD)

Activities	Quantity	Duration(Hours)	Total Workload(Hours)
Course Duration (Including the exam week: 16x Total course hours)	16	3	48
Hours for off-the-classroom study (Pre-study, practice)	14	6	84
Mid-terms	1	2.5	2.5
Assignments	6	8.25	49.5
Final examination	1	2.5	2.5
Other	1	1	1
Total Work Load:			187.5
Total Work Load/25(h):			7.5
ECTS Credit of the Course:			7.5