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Information
| Code | BİS662 |
| Name | |
| Term | 2023-2024 Academic Year |
| Semester | . Semester |
| Duration (T+A) | 2-2 (T-A) (17 Week) |
| ECTS | 6 ECTS |
| National Credit | 3 National Credit |
| Teaching Language | Türkçe |
| Level | Doktora Dersi |
| Type | Normal |
| Mode of study | Yüz Yüze Öğretim |
| Catalog Information Coordinator |
Course Goal / Objective
The purpose of Data Mining is to find and extract useful information from the data pile and using the discovered information to help explain the current situation and predict future occurrences.
Course Content
Extract information from internal and external sources to support automated data analysis and organizational decision-making processes. Researching different applications, methodologies, techniques and models. Classification, Decision Trees, Association Rules, Clustering. In this course large real-life data sets will be analyzed using R software.
Course Precondition
none
Resources
(Chapman & Hall_CRC Data Mining and Knowledge Discovery Series) Torgo, Luís - Data Mining with R_ Learning with Case Studies, Second Edition-Taylor & Francis_Chapman and Hall_CRC (2017)
Notes
R and Data Mining: Examples and Case Studies by Yanchang Zhao
Course Learning Outcomes
| Order | Course Learning Outcomes |
|---|---|
| LO01 | Extracts useful information from the data stack |
| LO02 | Analyzes, cleans and defragments data. |
| LO03 | Makes classification and clustering with supervised and unsupervised methods |
| LO04 | distinguish the working mechanisms of basic machine learning methods. |
Relation with Program Learning Outcome
| Order | Type | Program Learning Outcomes | Level |
|---|---|---|---|
| PLO01 | Bilgi - Kuramsal, Olgusal | Comprehends the original definitions, concepts and theorems that will bring innovation to the field based on the qualifications gained in the biostatistics master's program. | 5 |
| PLO02 | Bilgi - Kuramsal, Olgusal | Using knowledge that requires expertise, analyzes, evaluates and interprets new and complex ideas in the field and related fields. | |
| PLO03 | Bilgi - Kuramsal, Olgusal | He/She has advanced knowledge about technological tools and software that are frequently used in the field of biostatistics. | 4 |
| PLO04 | Bilgi - Kuramsal, Olgusal | Knows the importance of ethical principles and ethical committees for the individual and society. Comprehends the importance of Biostatistician in ethics committees. | |
| PLO05 | Bilgi - Kuramsal, Olgusal | He/She has advanced knowledge about statistical methods that are frequently used in studies in the field of health. | 3 |
| PLO06 | Beceriler - Bilişsel, Uygulamalı | Evaluates the knowledge in the field of biostatistics with a systematic approach | 5 |
| PLO07 | Beceriler - Bilişsel, Uygulamalı | Develops a new idea, method, design or application that brings innovation to the field of biostatistics, develops a known idea, method, design or application and applies it to a different field. | |
| PLO08 | Beceriler - Bilişsel, Uygulamalı | Design, analyzes critically, interprets and reports observational and clinical researchs for new and complex problems in medicine and health sciences. | |
| PLO09 | Beceriler - Bilişsel, Uygulamalı | He/She uses advanced statistical methods in the decision-making process in diagnosis and treatment in health sciences, and consults to researchers working in this field. | 4 |
| PLO10 | Beceriler - Bilişsel, Uygulamalı | Uses research and analysis methods that require high-level skills in studies related to the field of biostatistics. | 4 |
| PLO11 | Beceriler - Bilişsel, Uygulamalı | Develops and applies advanced statistical methods and techniques frequently used in health sciences at the level of expertise with original thought, research. | |
| PLO12 | Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği | Performs independently an original work that brings innovation to the field of biostatistics | |
| PLO13 | Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği | Performs advanced statistical analysis that can evaluate a scientific article. | |
| PLO14 | Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği | Develops the ability to read and write articles related to the field of biostatistics and apply for articles to national and/or international refereed journals. | |
| PLO15 | Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği | Takes an active role in solving original and interdisciplinary problems | |
| PLO16 | Yetkinlikler - Öğrenme Yetkinliği | Develops new ideas and methods in the field of Biostatistics by using high-level mental processes such as creative and critical thinking, problem solving and decision making. | |
| PLO17 | Yetkinlikler - Öğrenme Yetkinliği | Comprehends the ways to reach the evidence and evaluates the evidence critically. | |
| PLO18 | Yetkinlikler - Öğrenme Yetkinliği | He/She determines the principles of lifelong learning and professional development as an attitude and displays this attitude in his/her works. | |
| PLO19 | Yetkinlikler - İletişim ve Sosyal Yetkinlik | Understands the dynamics of social relations required by the health profession and critically evaluates and develops the norms that guide these relations. | |
| PLO20 | Yetkinlikler - İletişim ve Sosyal Yetkinlik | Discusses the issues in the field with other experts in interdisciplinary studies, using effective communication skills, and provides academic consultancy by defending his/her original views. | |
| PLO21 | Yetkinlikler - İletişim ve Sosyal Yetkinlik | Communicates written, verbal and visual with foreign language knowledge in international scientific environments | |
| PLO22 | Yetkinlikler - Alana Özgü Yetkinlik | By using the knowledge of biostatistics and medical informatics, he/she contributes to the society's becoming an information society by presenting his/her knowledge and skills to his/her society. | |
| PLO23 | Yetkinlikler - Alana Özgü Yetkinlik | Establishes functional interaction by defending original views in solving problems related to biostatistics | |
| PLO24 | Yetkinlikler - Alana Özgü Yetkinlik | Consults using effective communication skills, takes part in teamwork in research, defends scientific ethical rules | |
| PLO25 | Yetkinlikler - Alana Özgü Yetkinlik | He/She has the experience of working with other health disciplines as a requirement of the field. | |
| PLO26 | Yetkinlikler - Alana Özgü Yetkinlik | He/she chooses and applies the correct statistical methods in his/her studies in the field of health and interprets them correctly. Performs advanced analysis and synthesis. | 4 |
| PLO27 | Yetkinlikler - Alana Özgü Yetkinlik | Uses current developments and information in the field of health for the benefit of society in line with the realities of the country. |
Week Plan
| Week | Topic | Preparation | Methods |
|---|---|---|---|
| 1 | Introduction to Data Mining | reading | Öğretim Yöntemleri: Anlatım |
| 2 | Data Mining Concepts and Data Preprocessing | reading | Öğretim Yöntemleri: Anlatım, Tartışma |
| 3 | Data Reduction and Discretization-I | reading | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 4 | Data Reduction and Discretization-II | reading | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama, Soru-Cevap, Tartışma |
| 5 | Decision Trees and Decision Rules | reading | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama, Soru-Cevap, Tartışma |
| 6 | Classification by Statistical Methods - Naive Bayes Classifier | reading | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama, Soru-Cevap |
| 7 | Evaluation of Classification and Clustering Methods, Class Confusion Matrix | reading | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama, Tartışma |
| 8 | Mid-Term Exam | none | Ölçme Yöntemleri: Ödev |
| 9 | Clustering and Similarity Measures | reading | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama, Tartışma |
| 10 | Clustering Methods - K-Means Algorithm | reading | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama, Tartışma |
| 11 | Clustering Methods - Hierarchical Clustering | reading | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama, Soru-Cevap, Tartışma |
| 12 | Association Rules, Market Basket Analysis, Apriori Algorithm | reading | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 13 | Current Technology and Tools Used in Data Mining | reading | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Tartışma |
| 14 | Comparison of performance of classification and clustering methods using R program. | reading | Öğretim Yöntemleri: Alıştırma ve Uygulama |
| 15 | Comparison of performance of classification and clustering methods using R program. II | reading | Öğretim Yöntemleri: Anlatım, Tartışma, Soru-Cevap |
| 16 | Term Exams | none | Ölçme Yöntemleri: Ödev, Proje / Tasarım |
| 17 | Term Exams | none | Ölçme Yöntemleri: Ödev, Proje / Tasarım |
Student Workload - ECTS
| Works | Number | Time (Hour) | Workload (Hour) |
|---|---|---|---|
| Course Related Works | |||
| Class Time (Exam weeks are excluded) | 14 | 4 | 56 |
| Out of Class Study (Preliminary Work, Practice) | 14 | 4 | 56 |
| Assesment Related Works | |||
| Homeworks, Projects, Others | 1 | 2 | 2 |
| Mid-term Exams (Written, Oral, etc.) | 1 | 12 | 12 |
| Final Exam | 1 | 28 | 28 |
| Total Workload (Hour) | 154 | ||
| Total Workload / 25 (h) | 6,16 | ||
| ECTS | 6 ECTS | ||