Course Name Code Semester T+U Hours Credit ECTS
Introduction To Data Mining ENM 424 8 3 + 0 3 5
Precondition Courses
Recommended Optional Courses
Course Language Turkish
Course Level Bachelor's Degree
Course Type Optional
Course Coordinator Arş.Gör.Dr. CANER ERDEN
Course Lecturers Arş.Gör.Dr. CANER ERDEN, Arş.Gör.Dr. CANER ERDEN,
Course Assistants
Course Category Teaching Suitable For Field
Course Objective

1) Introducing Data Mining and Promoting Its Usage
2) To give ability to the students on analyzing large scale databases

Course Content

This course covers the fundamentals of statistical, machine learning and database aspects of data mining . The course is composed of three parts. The first part will cover the fundamentals of statistics and machine learning approaches for data mining. In the second part, we will cover the fundamental data mining concepts and algorithms for tasks such as Online Analytical Processing (OLAP), association rules, clustering, etc. The final part of the course will focus on research areas such as text mining, collaborative filtering, link analysis and mining in biological domains (as time permits).

# Course Learning Outcomes Teaching Methods Assessment Methods
1 Utilizing classification algorithms Lab / Workshop, Self Study, Project Based Learning, Project / Design, Testing, Homework,
2 Utilizing clustering algorithms Project Based Learning, Self Study, Lab / Workshop, Homework, Project / Design, Testing,
3 Utilizing market basket analysis Lab / Workshop, Self Study, Project Based Learning, Testing, Homework, Project / Design,
4 Ability of using data mining software Project Based Learning, Project / Design, Performance Task,
Week Course Topics Preliminary Preparation
1 Data Mining: An Introduction
2 Areas of interest in data mining
3 Introduction to Data Mining computer programs - Data Mining in Spreadsheet Programs
4 Prepering data to analysis (steps)
6 Classification and Clustering
7 Decision tree
8 Statistics in data mining
9 Artificial intelligent in data mining
10 Neural Networks in data mining
11 Association rules
12 Other mining techniques in data mining- web and text mining
13 Sample applications
14 Data Mining Applications in Industry
Course Notes <p></p> <p>lecture notes on this link</p>
Course Resources

1. Gökhan Silahtaroğlu, Basic Data Mining with Concepts and Algorithms, Papatya Publications, (2008)
2. Pang-Ning Tan, Michael Steinbach, Vipin Kumar, Introduction to Data Mining, Addison Wesley, (2005).

Order Program Outcomes Level of Contribution
1 2 3 4 5
1 Engineering graduates with sufficient knowledge background on science and engineering subjects of their related area, and who are skillful in implementing theoretical and practical knowledge for modelling and solving engineering problems. X
2 Engineering graduates with skills in identifying, describing, formulating and solving complex engineering problems, and thus,deciding and implementing appropriate methods for analyzing and modelling. X
3 Engineering graduates with skills in designing a complex system, process, device or product under realistic constraints and conditions to meet specific requirements; for this purpose, skills in implementing modern design methods. X
4 Engineering graduates with skills in developing, selecting and implementing modern techniques and tools required for engineering applications as well as with skills in using information technologies effectively. X
5 Engineering graduates with skills in designing and conducting experiments, collecting data, analyzing and interpreting the results in order to evaluate engineering problems. X
6 Engineering graduates who are able to work within a one discipline or multi-discipline team,as well as who are able to work individually X
7 Engineering graduates who are able to effectively communicate orally and officially in Turkish Language as well as who knows at least one foreign language
8 Engineering graduates with motivation to life-long learning and having known significance of continuous education beyond undergraduate studies for science and technology X
9 Engineering graduates with well-structured responsibilities in profession and ethics X
10 Engineering graduates having knowledge about practices in professional life such as project management, risk management and change management, and who are aware of innovation and sustainable development. X
11 Engineering graduates having knowledge about universal and social effects of engineering applications on health, environment and safety, as well as having awareness for juridical consequences of engineering solutions. X
Evaluation System
Semester Studies Contribution Rate
1. Kısa Sınav 15
1. Ödev 7
2. Ödev 70
Total 92
1. Yıl İçinin Başarıya 50
1. Final 50
Total 100
ECTS - Workload Activity Quantity Time (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) 16 2 32
Mid-terms 1 4 4
Assignment 2 10 20
Performance Task (Laboratory) 1 16 16
Total Workload 120
Total Workload / 25 (Hours) 4.8
dersAKTSKredisi 5