Management Information Systems (English)
Bachelor TR-NQF-HE: Level 6 QF-EHEA: First Cycle EQF-LLL: Level 6

General Introduction Information of the Course

Course Code: MIS304
Ders İsmi: Data Mining
Ders Yarıyılı: Spring
Ders Kredileri:
Theoretical Practical Credit ECTS
3 0 3 5
Language of instruction: EN
Ders Koşulu:
Ders İş Deneyimini Gerektiriyor mu?: No
Type of course: Necessary
Course Level:
Bachelor TR-NQF-HE:6. Master`s Degree QF-EHEA:First Cycle EQF-LLL:6. Master`s Degree
Mode of Delivery: Face to face
Course Coordinator : Prof. Dr. Fazlı YILDIRIM
Course Lecturer(s):
Course Assistants:

Course Objectives and Content

Course Objectives: The course will examine different clustering, classification, regression, feature selection and weighting methods developed within the scope of data mining. In addition, students will be able to practice related techniques with the help of WEKA computer program.
Course Content: Data; information and knowledge concepts; Introduction to data mining; Knowledge discovery in databases (KDD); Databases; OLTP; Data warehouses; Data cubes; OLAP; KDD- data select; KDD- data preprocessing (data cleaning – data transformation); Classification concepts (decision trees; ID3 and bayes algorithms; etc.); Cluster concepts (k-means; k-medoids; dbscan algorithms; etc.); Association rules concepts (market basket; apriori algorithm; etc.); Case study with apriori algorithm.

Learning Outcomes

The students who have succeeded in this course;
Learning Outcomes
1 - Knowledge
Theoretical - Conceptual
1) Data mining concept
2) Data mining basic techniques
3) Learning to use the Weka program;
2 - Skills
Cognitive - Practical
1) Comparison of learned techniques
3 - Competences
Communication and Social Competence
Learning Competence
Field Specific Competence
Competence to Work Independently and Take Responsibility

Course Weekly Plan

Week Subject Related Preparation
1) Introduction to data minning and concepts Data Mining Concepts and Tecniques - Jiawei Han, Micheline Kamber – Elsevier 2006 Principles of Data Mining – Max Bramer - Springer-Verlag London Limited 2007 Data Mining Methods and Models - Daniel T. Larose - John Wiley & Sons – 2006 Data Mining: Concepts and Techniques (The Morgan Kaufmann Series in Data Management Systems) 3rd Edition - Jiawei Han, Micheline Kamber, Jian Pei -Morgan Kaufmann publications - 2011
2) Data types and statistical descriptions Data Mining Concepts and Tecniques - Jiawei Han, Micheline Kamber – Elsevier 2006 Principles of Data Mining – Max Bramer - Springer-Verlag London Limited 2007 Data Mining Methods and Models - Daniel T. Larose - John Wiley & Sons – 2006 Data Mining: Concepts and Techniques (The Morgan Kaufmann Series in Data Management Systems) 3rd Edition - Jiawei Han, Micheline Kamber, Jian Pei -Morgan Kaufmann publications - 2011
3) Data visulations and measuring similiarity Data Mining Concepts and Tecniques - Jiawei Han, Micheline Kamber – Elsevier 2006 Principles of Data Mining – Max Bramer - Springer-Verlag London Limited 2007 Data Mining Methods and Models - Daniel T. Larose - John Wiley & Sons – 2006 Data Mining: Concepts and Techniques (The Morgan Kaufmann Series in Data Management Systems) 3rd Edition - Jiawei Han, Micheline Kamber, Jian Pei -Morgan Kaufmann publications - 2011
4) Proprocessing Data Data Mining Concepts and Tecniques - Jiawei Han, Micheline Kamber – Elsevier 2006 Principles of Data Mining – Max Bramer - Springer-Verlag London Limited 2007 Data Mining Methods and Models - Daniel T. Larose - John Wiley & Sons – 2006 Data Mining: Concepts and Techniques (The Morgan Kaufmann Series in Data Management Systems) 3rd Edition - Jiawei Han, Micheline Kamber, Jian Pei -Morgan Kaufmann publications - 2011
5) Data Wharehouses Data Mining Concepts and Tecniques - Jiawei Han, Micheline Kamber – Elsevier 2006 Principles of Data Mining – Max Bramer - Springer-Verlag London Limited 2007 Data Mining Methods and Models - Daniel T. Larose - John Wiley & Sons – 2006 Data Mining: Concepts and Techniques (The Morgan Kaufmann Series in Data Management Systems) 3rd Edition - Jiawei Han, Micheline Kamber, Jian Pei -Morgan Kaufmann publications - 2011
6) Data Cubes Data Mining Concepts and Tecniques - Jiawei Han, Micheline Kamber – Elsevier 2006 Principles of Data Mining – Max Bramer - Springer-Verlag London Limited 2007 Data Mining Methods and Models - Daniel T. Larose - John Wiley & Sons – 2006 Data Mining: Concepts and Techniques (The Morgan Kaufmann Series in Data Management Systems) 3rd Edition - Jiawei Han, Micheline Kamber, Jian Pei -Morgan Kaufmann publications - 2011
7) Sıklık Kalıpları Bulma Data Mining Concepts and Tecniques - Jiawei Han, Micheline Kamber – Elsevier 2006 Principles of Data Mining – Max Bramer - Springer-Verlag London Limited 2007 Data Mining Methods and Models - Daniel T. Larose - John Wiley & Sons – 2006 Data Mining: Concepts and Techniques (The Morgan Kaufmann Series in Data Management Systems) 3rd Edition - Jiawei Han, Micheline Kamber, Jian Pei -Morgan Kaufmann publications - 2011
8) Mid-term
9) Advenced pattern matching Data Mining Concepts and Tecniques - Jiawei Han, Micheline Kamber – Elsevier 2006 Principles of Data Mining – Max Bramer - Springer-Verlag London Limited 2007 Data Mining Methods and Models - Daniel T. Larose - John Wiley & Sons – 2006 Data Mining: Concepts and Techniques (The Morgan Kaufmann Series in Data Management Systems) 3rd Edition - Jiawei Han, Micheline Kamber, Jian Pei -Morgan Kaufmann publications - 2011
10) Data classification Data Mining Concepts and Tecniques - Jiawei Han, Micheline Kamber – Elsevier 2006 Principles of Data Mining – Max Bramer - Springer-Verlag London Limited 2007 Data Mining Methods and Models - Daniel T. Larose - John Wiley & Sons – 2006 Data Mining: Concepts and Techniques (The Morgan Kaufmann Series in Data Management Systems) 3rd Edition - Jiawei Han, Micheline Kamber, Jian Pei -Morgan Kaufmann publications - 2011
11) Gelişmiş Veri Sınıflandırma Data Mining Concepts and Tecniques - Jiawei Han, Micheline Kamber – Elsevier 2006 Principles of Data Mining – Max Bramer - Springer-Verlag London Limited 2007 Data Mining Methods and Models - Daniel T. Larose - John Wiley & Sons – 2006 Data Mining: Concepts and Techniques (The Morgan Kaufmann Series in Data Management Systems) 3rd Edition - Jiawei Han, Micheline Kamber, Jian Pei -Morgan Kaufmann publications - 2011
12) Cluster analysis Data Mining Concepts and Tecniques - Jiawei Han, Micheline Kamber – Elsevier 2006 Principles of Data Mining – Max Bramer - Springer-Verlag London Limited 2007 Data Mining Methods and Models - Daniel T. Larose - John Wiley & Sons – 2006 Data Mining: Concepts and Techniques (The Morgan Kaufmann Series in Data Management Systems) 3rd Edition - Jiawei Han, Micheline Kamber, Jian Pei -Morgan Kaufmann publications - 2011
13) Küme analizi devam etti Data Mining Concepts and Tecniques - Jiawei Han, Micheline Kamber – Elsevier 2006 Principles of Data Mining – Max Bramer - Springer-Verlag London Limited 2007 Data Mining Methods and Models - Daniel T. Larose - John Wiley & Sons – 2006 Data Mining: Concepts and Techniques (The Morgan Kaufmann Series in Data Management Systems) 3rd Edition - Jiawei Han, Micheline Kamber, Jian Pei -Morgan Kaufmann publications - 2011
14) Outlier Detection Data Mining Concepts and Tecniques - Jiawei Han, Micheline Kamber – Elsevier 2006 Principles of Data Mining – Max Bramer - Springer-Verlag London Limited 2007 Data Mining Methods and Models - Daniel T. Larose - John Wiley & Sons – 2006 Data Mining: Concepts and Techniques (The Morgan Kaufmann Series in Data Management Systems) 3rd Edition - Jiawei Han, Micheline Kamber, Jian Pei -Morgan Kaufmann publications - 2011
15) Datamining tools Data Mining Concepts and Tecniques - Jiawei Han, Micheline Kamber – Elsevier 2006 Principles of Data Mining – Max Bramer - Springer-Verlag London Limited 2007 Data Mining Methods and Models - Daniel T. Larose - John Wiley & Sons – 2006 Data Mining: Concepts and Techniques (The Morgan Kaufmann Series in Data Management Systems) 3rd Edition - Jiawei Han, Micheline Kamber, Jian Pei -Morgan Kaufmann publications - 2011
16) Final Exam

Sources

Course Notes / Textbooks: Data Mining Concepts and Tecniques - Jiawei Han, Micheline Kamber – Elsevier 2006
Principles of Data Mining – Max Bramer - Springer-Verlag London Limited 2007
Data Mining Methods and Models - Daniel T. Larose - John Wiley & Sons – 2006
Data Mining: Concepts and Techniques (The Morgan Kaufmann Series in Data Management Systems) 3rd Edition - Jiawei Han, Micheline Kamber, Jian Pei -Morgan Kaufmann publications - 2011
References: Data Mining Concepts and Tecniques - Jiawei Han, Micheline Kamber – Elsevier 2006
Principles of Data Mining – Max Bramer - Springer-Verlag London Limited 2007
Data Mining Methods and Models - Daniel T. Larose - John Wiley & Sons – 2006
Data Mining: Concepts and Techniques (The Morgan Kaufmann Series in Data Management Systems) 3rd Edition - Jiawei Han, Micheline Kamber, Jian Pei -Morgan Kaufmann publications - 2011

Relationship Between Course and Program Learning Outcomes

Course Learning Outcomes

1

2

4

3
Program Outcomes
1) To be able to use the information obtained in the fields of Information Technologies and Business Science with appropriate tools for decision making.
2) To be able to recognize the computer hardware, to distinguish the technical features of the parts, to compare, to classify and to choose the appropriate hardware.
3) To have knowledge about software types, software selection and supply, and to plan and manage software development processes.
4) Performing the database design required for applications.
5) To be able to establish a computer network system, to solve the problems encountered in networks and hardware.
6) To be able to determine the data needs in MIS based problem solving of different disciplines, to obtain these data and to compile the data to produce information and make it ready for use.
7) To be able to determine the information system requirements, to make system analysis and design.
8) To be able to design a project for the solution of an MIS or social problem and to propose different solution methods.
9) To be able to design projects as a MIS specialist, to contribute to the project as both a manager and an employee, and to produce innovative ideas.
10) In cases where an information system problem is solved as a team, to take individual responsibility at every stage of the problem, to contribute to the team and to lead the team when necessary.
11) To be able to follow professional, current and developing trends by using Turkish and English languages, to convey necessary information in written and oral form.
12) To be able to evaluate the knowledge and skills acquired in the field with a critical perspective by integrating them with different disciplines.
13) To be able to act according to social ethical values in professional studies.

Relationship Between Course and Learning Outcome

No Effect 1 Lowest 2 Low 3 Medium 4 High 5 Highest
           
Program Outcomes Level of Contribution
1) To be able to use the information obtained in the fields of Information Technologies and Business Science with appropriate tools for decision making. 5
2) To be able to recognize the computer hardware, to distinguish the technical features of the parts, to compare, to classify and to choose the appropriate hardware. 5
3) To have knowledge about software types, software selection and supply, and to plan and manage software development processes. 4
4) Performing the database design required for applications. 5
5) To be able to establish a computer network system, to solve the problems encountered in networks and hardware. 4
6) To be able to determine the data needs in MIS based problem solving of different disciplines, to obtain these data and to compile the data to produce information and make it ready for use. 4
7) To be able to determine the information system requirements, to make system analysis and design. 3
8) To be able to design a project for the solution of an MIS or social problem and to propose different solution methods.
9) To be able to design projects as a MIS specialist, to contribute to the project as both a manager and an employee, and to produce innovative ideas. 1
10) In cases where an information system problem is solved as a team, to take individual responsibility at every stage of the problem, to contribute to the team and to lead the team when necessary. 3
11) To be able to follow professional, current and developing trends by using Turkish and English languages, to convey necessary information in written and oral form. 4
12) To be able to evaluate the knowledge and skills acquired in the field with a critical perspective by integrating them with different disciplines. 3
13) To be able to act according to social ethical values in professional studies. 1

Learning Activities and Teaching Methods

Anlatım
Course
Labs
Homework

Assessment and Evaluation Methods and Criteria

Yazılı Sınav (Açık uçlu sorular, çoktan seçmeli, doğru yanlış, eşleştirme, boşluk doldurma, sıralama)
Homework

Assessment & Grading

Semester Requirements Number of Activities Level of Contribution
Homework Assignments 2 % 30
Midterms 1 % 30
Final 1 % 40
total % 100
PERCENTAGE OF SEMESTER WORK % 60
PERCENTAGE OF FINAL WORK % 40
total % 100

Workload and ECTS Credit Calculation

Activities Number of Activities Duration (Hours) Workload
Course Hours 15 3 45
Midterms 1 45 45
Final 1 45 45
Total Workload 135