Veri Bilimi (YL) (Tezli) (İngilizce)
Ders İçerikleri


1. Semester

Course CodeCourse Name(T+A+L)ECTSCompulsory/Elective
DATS5101 Database Applications 1+2+0 9 Compulsory
1. Overview of database systems, architectures, models, and components. 2. Principles and methodologies for database design and data modeling. 3. Evaluation and utilization of SQL for database manipulation and querying. 4. Introduction to NoSQL databases and their applications in modern data environments. 5. Implementing and managing database solutions in various application contexts.
Course CodeCourse Name(T+A+L)ECTSCompulsory/Elective
IGE5500 Scientific Research Methods and Ethics 3+0+ 9 Compulsory
1. Introduction to Research Methods - Overview of scientific research and its importance - Types of research: qualitative, quantitative, and mixed methods 2. Critical Literature Review - Techniques for conducting systematic and critical reviews - Identifying research gaps and formulating research questions 3. Research Philosophy and Paradigms - Understanding positivism, interpretivism, and other paradigms - Aligning research design with philosophical foundations 4. Research Design and Methodology - Developing coherent research frameworks - Selecting appropriate methodologies for research objectives 5. Ethics in Research - Ethical considerations in research design and execution - Gaining access to data and maintaining confidentiality 6. Data Collection Techniques - Sampling strategies: probability and non-probability - Primary data collection: interviews, questionnaires, and diaries - Secondary data evaluation and analysis 7. Data Analysis Methods - Quantitative analysis: statistical tools and techniques - Qualitative analysis: thematic, narrative, and content analysis 8. Writing and Presenting Research - Structuring academic and consultancy reports - Effective presentation techniques for research findings 9. Practical Applications and Case Studies - Hands-on exercises in designing and conducting research - Real-world examples of ethical dilemmas and solutions 10. Final Project - Development of a mini research proposal - Peer presentations and constructive feedback sessions

2. Semester

Course CodeCourse Name(T+A+L)ECTSCompulsory/Elective
DATS5102 Regulations in the Field of Data: PDPA, GDPR, FERPA, HIPPA 3+0+0 9 Compulsory
1. Introduction to Data Regulation: PDPA, GDPR, FERPA, HIPAA 2. Understanding the General Data Protection Regulation (GDPR) Framework 3. Exploring the Personal Data Protection Act (PDPA) and its implications 4. The role of the Family Educational Rights and Privacy Act (FERPA) in protecting educational information 5. Health Insurance Portability and Accountability Act (HIPAA) Privacy, Security, and Breach Notification Rules
Course CodeCourse Name(T+A+L)ECTSCompulsory/Elective
DATS5189 Seminar 0+0+0 9 Compulsory
1. Advanced topics in Management Information Systems including recent research and case studies. 2. Critical analysis and discussion of scholarly articles and professional publications in the field. 3. Presentation and critique of emerging technologies impacting business strategies. 4. Designing and conducting original research in the field of Management Information Systems. 5. Development of communication skills through presentations and written assignments.

3. Semester

Course CodeCourse Name(T+A+L)ECTSCompulsory/Elective
DATS5289 Thesis Study 1 1+0+0 30 Compulsory
1. Examination and application of research methodologies. 2. Literature review techniques and information management. 3. Scientific data analysis, modelling and interpretation. 4. Academic writing techniques and ethical rules. 5. Thesis writing process and presentation techniques.

4. Semester

Course CodeCourse Name(T+A+L)ECTSCompulsory/Elective
DATS5290 Thesis Study 2 1+0+0 30 Compulsory
1. Examination and application of research methodologies. 2. Literature review techniques and information management. 3. Scientific data analysis, modeling and interpretation. 4. Academic writing techniques and ethical rules. 5. Thesis writing process and presentation techniques.

Department/Programme Elective Courses


Course CodeCourse Name(T+A+L)ECTS
AO5001 Artificial Intelligence Principles 3+0+0 9
Akıllı yazılım aracıları ve çok aracılı sistemlerin tasarımı, uygulanması ve seçilmiş uygulamaları. Akıllı davranışın hesaplamalı modelleri, problem çözme, bilgi temsili, akıl yürütme, planlama, karar verme, öğrenme, algılama, eylem, iletişim ve etkileşimi içerir.
Course CodeCourse Name(T+A+L)ECTS
AO5012 Human-Computer Interaction 3+0+0 6
Teaching the basic principles of user interfaces. Introduce students to usability models and principles. Get students to carry out user and task analyses. Teach design, prototype development, and evaluation by having students complete term projects. Discuss the effects of interface properties such as color and typography. Teach new user interface techniques.
Course CodeCourse Name(T+A+L)ECTS
AO5104 Data Science 3+0+0 6
This course covers fundamental topics in data science, starting with an introduction to key concepts, applications, and tools (Python, R, SQL, Jupyter). Students will learn data preprocessing techniques, including cleaning (handling missing values, outliers), transformation (normalization, encoding), and feature selection. Exploratory Data Analysis (EDA) will be taught using statistical summaries and visualization (Matplotlib, Seaborn, Tableau). The course includes supervised learning (regression, classification) and unsupervised learning (clustering, dimensionality reduction), along with model evaluation (accuracy, precision, recall, ROC). Additional topics include big data technologies (Hadoop, Spark), data storytelling, and ethical considerations. Real-world case studies and projects ensure practical experience.
Course CodeCourse Name(T+A+L)ECTS
COE5021 Machine Learning 3+0+0 6
This course provides a comprehensive overview of machine learning, covering both supervised and unsupervised learning approaches. It begins with an exploration of supervised learning, addressing regression problems and classification problems, including logistic regression, K-Nearest Neighbor, decision trees, handling imbalanced datasets, random forests, and techniques like cross-validation. The course delves into exploratory data analysis and data pre-processing techniques essential for effective machine learning. It then progresses to advanced topics such as hyperparameter tuning, dimensionality reduction, and unsupervised learning. Ensemble learning methods, particularly boosting techniques, are covered, along with an in-depth study of artificial neural networks, including perceptrons and multi-layer networks.
Course CodeCourse Name(T+A+L)ECTS
DATS5002 Data Driven Learning 1+2+0 6
Ders Text
Course CodeCourse Name(T+A+L)ECTS
DATS5005 Deep Learning 1+2+0 6
1. Mathematical foundations and basic concepts of deep learning. 2. Multilayer perceptrons, hyperparameter optimization, and feed-forward networks. 3. Convolutional neural networks and their applications to image processing. 4. Backpropagation algorithm, optimization methods and weighting updates. 5. Long Short Term Memory (LSTM) and recurrent neural networks (RNNs); studies on sequence data.
Course CodeCourse Name(T+A+L)ECTS
DATS5007 Decision Support Systems 1+2+0 6
1. Introduction to Decision Support Systems: key concepts and components 2. Decision Making Process: types of decisions, decision making under uncertainty 3. Decision Support System Modeling: linear, nonlinear, and discrete models 4. Data Warehousing and Data Mining for Decision Support: ETL processes and techniques 5. Advanced Topics in DSS: machine learning, artificial intelligence, and real-time decision systems
Course CodeCourse Name(T+A+L)ECTS
DATS5008 Business Intelligence and Data Visualization 1+2+0 6
1. Introduction to Decision Support Systems: key concepts and components 2. Decision Making Process: types of decisions, decision making under uncertainty 3. Decision Support System Modeling: linear, nonlinear, and discrete models 4. Data Warehousing and Data Mining for Decision Support: ETL processes and techniques 5. Advanced Topics in DSS: machine learning, artificial intelligence, and real-time decision systems
Course CodeCourse Name(T+A+L)ECTS
DATS5012 Natural Language Processing 1+2+0 6
1. Fundamentals and Application Areas of Natural Language Processing 2. Text Preprocessing and Cleaning Methods 3. Algorithms and Models for Natural Language Processing 4. Deep Learning and Natural Language Processing 5. Current Developments in Natural Language Processing and Application Studies
Course CodeCourse Name(T+A+L)ECTS
DATS5017 Explainable, Responsible and Trustworthy AI 1+2+0 6
1. Introduction and importance of the concepts of Explainable Artificial Intelligence (XAI). 2. Principles of responsible AI and ethical frameworks. 3. Trustworthy Artificial Intelligence system design and security standards. 4. Practical review of XAI methods and techniques. 5. Social and legal aspects of responsible and trustworthy AI systems.
Course CodeCourse Name(T+A+L)ECTS
DATS5018 Human-centered Data Science 1+2+0 6
1. Introduction to Human-centered Design Principles in Data Science 2. Ethical considerations and responsible data science 3. Data visualization and communication for diverse audiences 4. User experience (UX) research methods in Data Science 5. Case studies of human-centered data science applications
Course CodeCourse Name(T+A+L)ECTS
DATS5020 Data Science and AI Applications in Business 1+2+0 6
1. Overview of machine learning concepts and algorithms in a business context. 2. Data preprocessing, feature engineering, and data visualization techniques. 3. Supervised learning models for regression and classification in business decision-making. 4. Unsupervised learning for customer segmentation, market basket analysis, and anomaly detection. 5. Evaluation of machine learning models and deployment strategies for business applications.
Course CodeCourse Name(T+A+L)ECTS
DATS5022 Sustainability and Data Science 1+2+0 6
1. Introduction to Sustainability and its Intersections with Data Science 2. Fundamentals of Environmental Data and Metrics 3. Sustainable Practices in Data Collection and Analysis 4. Case Studies on Big Data for Sustainability 5. Implementing AI and Machine Learning for Sustainable Solutions
Course CodeCourse Name(T+A+L)ECTS
DATS5024 Data-driven Opportunities and Threats in AR,VR, MR, XR and Metaverse 1+1+0 6
1. Introduction to AR, VR, MR, XR, and Metaverse environments. 2. Understanding data opportunities in immersive technologies. 3. Identifying and evaluating threats and vulnerabilities in extended reality (XR). 4. Data analytics for user behavior and interaction within virtual spaces. 5. Legal and ethical considerations of data collection in immersive environments.
Course CodeCourse Name(T+A+L)ECTS
DATS5025 Social Network Analysis 1+2+0 6
1. Introduction to Social Network Analysis (SNA) and its applications in journalism. 2. Data collection methods for SNA in the context of news and societal trends. 3. The role of metrics and algorithms in understanding network structures and patterns. 4. Case studies of data-driven journalism projects utilizing SNA techniques. 5. Ethical considerations and privacy concerns in SNA applied to journalism.
Course CodeCourse Name(T+A+L)ECTS
DATS5026 Data Science Applications in Health Sciences 1+2+0 6
1. Overview of Health Sciences and Data Science intersection 2. Data handling and preprocessing in Health Data 3. Machine learning models used for health data analysis 4. Evaluation and interpretation of models in health science 5. Ethical considerations and data privacy in Health Data Science
Course CodeCourse Name(T+A+L)ECTS
DATS5027 Machine Learning Applications in Business 1+2+ 6
1. Overview of machine learning concepts and algorithms in a business context. 2. Data preprocessing, feature engineering, and data visualization techniques. 3. Supervised learning models for regression and classification in business decision-making. 4. Unsupervised learning for customer segmentation, market basket analysis, and anomaly detection. 5. Evaluation of machine learning models and deployment strategies for business applications.