CSE446 Data MiningInstitutional InformationDegree Programs Software Engineering (English)Information For StudentsDiploma SupplementErasmus Policy StatementNational Qualifications
Software Engineering (English)

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Bachelor TR-NQF-HE: Level 6 QF-EHEA: First Cycle EQF-LLL: Level 6

Course General Introduction Information

Course Code: CSE446
Course Name: Data Mining
Course Semester: Spring
Course Credits:
ECTS
6
Language of instruction:
Course Requirement:
Does the Course Require Work Experience?: No
Type of course: Area Ellective
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 : Dr.Öğr.Üyesi KEMAL ÇAĞRI SERDAROĞLU
Course Lecturer(s): Öğr. Üyesi Kemal Çağrı Serdaroğlu
Course Assistants:

Course Purpose and Content

Course Objectives: Fundamentals of data mining, data, information and knowledge, knowledge discovery in databases, the traditional statistical methods, neural networks, decision trees, Bayesian theorem, association rules, data warehouses, business applications, and advanced techniques to know and understand.
Course Content: The course provides an overview of leading data mining methods and applications. The topics covered include: data, information and knowledge, knowledge discovery in databases, traditional statistics, artificial neural networks, decision trees, Bayesian learning, association rules, data warehousing, commercial tools, feature selection and advanced techniques.

Learning Outcomes

The students who have succeeded in this course;
1) Have a good knowledge about the concept of data mining.
2) Knows about forecast models.
3) Knows about classication analysis.

Course Flow Plan

Week Subject Related Preparation
1) Data mining concepts
2) Data mining models and techniques
3) Data warehouses and  OLAP
4) Data warehouses and  OLAP
5) Descriptive statistical techniques
6) Decision trees
7) Forecast models
8) Midterm
9) Database segmentation
10) Link Analysis
11) Associations rules
12) Web mining
13) Web mining
14) Web mining

Sources

Course Notes / Textbooks: DATA MINING Concepts and Techniques, Jiawei HAN- Micheline KAMBER, Morgan Kaufman Pub.,2001
References: Ders Notları

Course - Learning Outcome Relationship

No Effect 1 Lowest 2 Medium 3 Highest
       
Program Outcomes Level of Contribution
1) Sufficient knowledge in mathematics, science and software engineering discipline-specific topics; the theoretical and practical knowledge in these areas, the ability to use in complex engineering problems.
2) The ability to identify, formulate, and solve complex engineering problems; selecting and applying appropriate analysis and modelling methods for this purpose.
3) The ability to design a complex system, process, device or product under realistic constraints and conditions to meet specific requirements; the ability to apply modern design methods for this purpose.
4) Ability to develop, select and use modern techniques and tools necessary for analysis and solution of complex problems in engineering applications; ability to use information technologies effectively.
5) Ability to design experiments, conduct experiments, collect data, analyse and interpret the results of complex engineering problems or discipline-specific research topics.
6) Disiplin içi ve çok disiplinli takımlarda etkin biçimde çalışabilme becerisi; bireysel çalışma becerisi.
7) Awareness of the need for lifelong learning; access to knowledge, ability to follow developments in science and technology, and constant self-renewal.
8) Effective communication skills in Turkish oral and written communication; at least one foreign language knowledge; ability to write effective reports and understand written reports, to prepare design and production reports, to make effective presentations, to give clear and understandable instructions and to receive.
9) Conformity to ethical principles, professional and ethical responsibility; Information on standards used in engineering applications.
10) Information on practices in business, such as project management, risk management and change management; awareness about entrepreneurship, innovation; information on sustainable development.
11) Information on the effects of engineering applications on health, environment, and safety in universal and social dimensions, and on the problems of the modern age in engineering; awareness of the legal consequences of engineering solutions.
12) Adequate skills in the analysis, design, verification, evaluation, implementation, implementation, and maintenance of software systems

Learning Activity and Teaching Methods

Bireysel çalışma ve ödevi
Course
Proje Hazırlama

Measurement and Evaluation Methods and Criteria

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

Assessment & Grading

Semester Requirements Number of Activities Level of Contribution
Homework Assignments 3 % 10
Project 1 % 20
Midterms 1 % 25
Final 1 % 45
total % 100
PERCENTAGE OF SEMESTER WORK % 55
PERCENTAGE OF FINAL WORK % 45
total % 100

İş Yükü ve AKTS Kredisi Hesaplaması

Activities Number of Activities Aktiviteye Hazırlık Aktivitede Harçanan Süre Aktivite Gereksinimi İçin Süre Workload
Course Hours 13 3 39
Study Hours Out of Class 2 20 40
Project 1 30 30
Homework Assignments 3 10 30
Midterms 1 2 2
Final 1 2 2
Total Workload 143