Information Systems and Technologies | |||||
Bachelor | TR-NQF-HE: Level 6 | QF-EHEA: First Cycle | EQF-LLL: Level 6 |
Course Code: | BST313 | ||||||||
Course Name: | Data Mining | ||||||||
Course Semester: | Fall | ||||||||
Course Credits: |
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Language of instruction: | TR | ||||||||
Course Requisites: | |||||||||
Does the Course Require Work Experience?: | No | ||||||||
Type of course: | Compulsory | ||||||||
Course Level: |
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Mode of Delivery: | Face to face | ||||||||
Course Coordinator : | Öğr.Gör. ENDER ŞAHİNASLAN | ||||||||
Course Lecturer(s): |
Öğr.Gör. ENDER ŞAHİNASLAN |
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Course Assistants: |
Course Objectives: | With this course, students will learn the information discovery processes in databases, data mining concept, methods and frequently used data mining algorithms and apply these algorithms in simple level. |
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 (kmeans; k-medoids; dbscan algorithms; etc.); Association rules concepts (market basket; apriori algorithm; etc.); Case study with apriori algorithm. |
The students who have succeeded in this course;
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Week | Subject | Related Preparation |
1) | Data, information and information concepts | Projection, Computer |
2) | The concept of data mining and introduction to information discovery processes in databases | Projection, Computer, Resource Books |
3) | Databases, data warehouses, data models, OLTP and OLAP | Projection, Computer, Resource Books |
4) | Information discovery processes in databases: data selection and data preprocessing | Projection, Computer, Resource Books |
5) | Information discovery processes in databases: Data reduction | Projection, Computer, Resource Books |
6) | Data mining methods: Classification (Decision trees, Bayes, Naive Bayes) | Projection, Computer, Resource Books |
7) | Data mining methods: Classification (ID3) | Projection, Computer, Resource Books |
8) | Data mining methods: Clustering (AGNES, DIANA, K-Means, K-Medoids and DB-SCAN) | Projection, Computer, Resource Books |
9) | QUIZ | |
10) | Data mining methods: Association Rule (Support and Trust values) | Projection, Computer, Resource Books |
11) | Data mining methods: Association Rule (Market Basket) | Projection, Computer, Resource Books |
12) | Data mining methods: Association Rule (Apriori Algorithm) | Projection, Computer, Resource Books |
13) | Student Presentations (Data Mining Algorithms) | Projection, Computer, Resource Books |
14) | Student Presentations (Data Mining Algorithms) | Projection, Computer, Resource Books |
Course Notes / Textbooks: | Veri Madenciliği Ders Notları - Feridun Özçakır Data Mining Concepts and Tecniques - Jiawei Han, Micheline Kamber – Elsevier 2006 |
References: | Principles of Data Mining – Max Bramer - Springer-Verlag London Limited 2007 Data Mining Methods and Models - Daniel T. Larose - John Wiley & Sons - 2006 |
Learning Outcomes | 1 |
2 |
3 |
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Program Outcomes | ||||
1) Having knowledge and skills in software development for different environments, systems management, network security, data and database management systems. | ||||
2) Keeping up-to-date with current issues about new information systems that are the result of rapid change of information technologies. | ||||
3) Be aware of the importance of Information Systems' stratagic position in the firm and its role in the creation of new business strategies. | ||||
4) To be able to explain the ideas and suggestions that is related to the field of Information Systems as in writing and orally. | ||||
5) ability to carry out an independent study on the subjects requiring expertise in the field of Information Systems. |
No Effect | 1 Lowest | 2 Low | 3 Average | 4 High | 5 Highest |
Program Outcomes | Level of Contribution | |
1) | Having knowledge and skills in software development for different environments, systems management, network security, data and database management systems. | 5 |
2) | Keeping up-to-date with current issues about new information systems that are the result of rapid change of information technologies. | 4 |
3) | Be aware of the importance of Information Systems' stratagic position in the firm and its role in the creation of new business strategies. | 4 |
4) | To be able to explain the ideas and suggestions that is related to the field of Information Systems as in writing and orally. | |
5) | ability to carry out an independent study on the subjects requiring expertise in the field of Information Systems. | 5 |
Written Exam (Open-ended questions, multiple choice, true-false, matching, fill in the blanks, sequencing) | |
Homework | |
Group project | |
Presentation |
Semester Requirements | Number of Activities | Level of Contribution |
Attendance | 42 | % 5 |
Presentation | 1 | % 20 |
Midterms | 1 | % 25 |
Final | 1 | % 50 |
total | % 100 | |
PERCENTAGE OF SEMESTER WORK | % 50 | |
PERCENTAGE OF FINAL WORK | % 50 | |
total | % 100 |
Activities | Number of Activities | Duration (Hours) | Workload |
Course Hours | 14 | 3 | 42 |
Presentations / Seminar | 1 | 10 | 10 |
Homework Assignments | 5 | 2 | 10 |
Quizzes | 3 | 1 | 3 |
Midterms | 1 | 2 | 2 |
Paper Submission | 1 | 4 | 4 |
Final | 1 | 2 | 2 |
Total Workload | 73 |