Veri Bilimi (YL) (Tezli)
Ders İçerikleri
1. Semester
| Course Code | Course Name | (T+A+L) | ECTS | Compulsory/Elective |
| VB5101 | Database Applications | 1+2+0 | 9 | Compulsory |
| Designing a database management system by understanding data types and database setup. Discussing and understanding the standard SQL structure structure | ||||
| Course Code | Course Name | (T+A+L) | ECTS | Compulsory/Elective |
| VB5103 | Data-Privacy-Ethics and Scientific Research | 3+0+0 | 9 | Compulsory |
| 1. To be able to determine the research topic and problematic 2. To know the ways to access academic resources 3. To be able to design the research 4. Being able to write a research proposal 5. To be able to determine the research universe and sample 6. To be able to prepare and use qualitative and quantitative data tools 7. To have basic statistical knowledge to describe data 8. Citing and citing references 9. To know ethical issues in research 10. To be able to use qualitative and quantitative data analysis tools | ||||
2. Semester
| Course Code | Course Name | (T+A+L) | ECTS | Compulsory/Elective |
| VB5102 | Legal Regulations in the Field of Data: KVKK, GDPR, FERPA, HIPPA | 3+0+0 | 9 | Compulsory |
| 1. Basic concepts and principles of data protection laws 2. KVKK (Personal Data Protection Law) and its application areas 3. GDPR (General Data Protection Regulation) and its impact 4. FERPA (Family Educational Privacy and Educational Rights Act) and its applications 5. HIPAA (Health Insurance Portability and Accountability Act) and protection of health data | ||||
| Course Code | Course Name | (T+A+L) | ECTS | Compulsory/Elective |
| VB5104 | Seminar | 3+0+0 | 9 | Compulsory |
| Research question development, literature review, determining research methods and theoretical framework, and conducting empirical research. Preparation for academic research and the process of writing a thesis | ||||
3. Semester
| Course Code | Course Name | (T+A+L) | ECTS | Compulsory/Elective |
| VB5289 | Master Thesis | 0+0+0 | 60 | 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 Code | Course Name | (T+A+L) | ECTS |
| VB5004 | 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 Code | Course Name | (T+A+L) | ECTS |
| VB5005 | Reinforcement Learning | 1+2+0 | 6 |
| Course Code | Course Name | (T+A+L) | ECTS |
| VB5006 | Decision Support Systems | 1+2+0 | 6 |
| Rational decision-making and appropriate information support are key components of decision support systems (DSS). These systems encompass various elements such as data, information, databases, database management systems, knowledge bases, data warehouses, rule/model bases, expert system mechanisms, uncertainty factors, system dynamics and simulation, group decision support systems, executive information systems, user interface component recognition, as well as the design, implementation, and evaluation of DSS. | |||
| Course Code | Course Name | (T+A+L) | ECTS |
| VB5007 | Business Intelligence and Data Visualization | 1+2+0 | 6 |
| 1. The concept and basic principles of business intelligence 2. Data visualization tools and techniques 3. Data analysis and reporting methods 4. Data visualization and business intelligence applications 5. Business intelligence and data visualization ethics and issues | |||
| Course Code | Course Name | (T+A+L) | ECTS |
| VB5011 | 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 Code | Course Name | (T+A+L) | ECTS |
| VB5016 | Explainable, Responsible and Reliable Artificial Intelligence | 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 Code | Course Name | (T+A+L) | ECTS |
| VB5017 | Human-Centered Data Science | 1+2+0 | 6 |
| 1. Introducing Data Science and Human Centered Approach 2. Data Collection and Data Preprocessing Techniques 3. Data Visualization and Communication 4. Data-Driven Storytelling and Engagement 5. Data Labeling and Data Privacy | |||
| Course Code | Course Name | (T+A+L) | ECTS |
| VB5019 | Data Science and Artificial Intelligence Applications in Business | 1+2+0 | 6 |
| 1. Basic principles and methods of data science and artificial intelligence concepts. 2. Data collection, data cleaning and data analysis processes in businesses. 3. Usage areas and potential benefits of artificial intelligence applications in business. 4. Application of machine learning and deep learning algorithms to business problems. 5. Data security and ethical issues in business. | |||
| Course Code | Course Name | (T+A+L) | ECTS |
| VB5021 | Sustainability and Data Science | 1+2+0 | 6 |
| 1. The concept and importance of sustainability 2. The relationship between data science and sustainability 3. Data collection and analysis methods for sustainability 4. Data-driven sustainability strategies 5. Sustainability and data ethics issues | |||
| Course Code | Course Name | (T+A+L) | ECTS |
| VB5023 | Data-Driven Opportunities and Threats in AR, VR, MR, XR and Metaverse environments | 1+2+0 | 6 |
| This course will explore the potential opportunities and threats that data-driven technologies present in metaverse environments. Metaverse environments are virtual worlds that allow users to interact with each other and with virtual objects and environments in real-time. These environments are often driven by data and can be accessed through various devices, such as computers, smartphones, and virtual reality headsets. In this course, students will learn about the various data-driven technologies that are used in metaverse environments, including artificial intelligence, machine learning, and data analytics. They will also learn about the ethical and privacy considerations that arise when using data in these environments, as well as the potential risks and benefits of data-driven technologies in metaverse environments. Throughout the course, students will be given the opportunity to engage in hands-on projects and exercises to develop their skills in working with data in metaverse environments. | |||
| Course Code | Course Name | (T+A+L) | ECTS |
| VB5024 | Social Network Analysis and Data-Based Journalism | 1+2+0 | 6 |
| 1. Network Science and Social Networks 2. Basic Concepts (Node, edge, network) 3. Basic Concepts (Centrality, stratification, periphery-centre) 4. Network types 5. Roles in social networks 6. Flow in social networks 7. Visualization of social networks 8. Basic approaches to the analysis of social network data 9. Dissemination of knowledge and influence in social networks 10. Use of programs such as NodeXL, Gephi, R, Pajek, Netdraw, UciNet | |||
| Course Code | Course Name | (T+A+L) | ECTS |
| VB5025 | Data Science Applications in Health Sciences | 1+2+0 | 6 |
| 1. Introduction: The Role and Importance of Machine Learning in Business 2. Supervised and Unsupervised Learning Techniques 3. Data Preprocessing and Feature Engineering 4. Creation and Evaluation of Regression and Classification Models 5. Integration of Machine Learning Algorithms into Business Strategies | |||