ISTINYE UNIVERSITY BİLGİ PAKETİ / DERS KATALOĞU
| Data Science (Master) (with Thesis) (English) | |||||
| Master | TR-NQF-HE: Level 7 | QF-EHEA: Second Cycle | EQF-LLL: Level 7 | ||
Course Introduction and Application Information
| Course Code: | DATS5024 | ||||
| Course Name: | Data-driven Opportunities and Threats in AR,VR, MR, XR and Metaverse | ||||
| Semester: | Fall | ||||
| Course Credits: |
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| Language of instruction: | English | ||||
| Course Condition: | |||||
| Does the Course Require Work Experience?: | No | ||||
| Type of course: | Departmental Elective | ||||
| Course Level: |
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| Mode of Delivery: | E-Learning | ||||
| Course Coordinator: | Doç. Dr. ŞEBNEM ÖZDEMİR | ||||
| Course Lecturer(s): | |||||
| Course Assistants: |
Course Objective and Content
| Course Objectives: | This course aims to provide students with a comprehensive understanding of the various data-driven opportunities and threats inherent in augmented reality (AR), virtual reality (VR), mixed reality (MR), extended reality (XR), and Metaverse environments. Students will gain in-depth knowledge of the latest innovations, discuss real-world case studies, and explore the techniques required to analyze and leverage data effectively. The course will also cover the ethical and privacy aspects critical for responsible data management and utilization in these immersive domains. The goal is to equip students with the skills necessary to navigate the emerging landscape of XR technologies in a data-centric world. |
| Course Content: | 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. |
Learning Outcomes
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The students who have succeeded in this course;
1) - Students will understand the key concepts and distinctions of AR, VR, MR, XR, and Metaverse. 2) - Students will be able to identify unique data opportunities and methods to capture data in XR environments. 3) - Students will recognize potential threats and learn how to implement security measures to protect user data. 4) - Students will develop analytical skills for assessing user behavior and enhancing virtual experiences. 5) - Students will be aware of legal and ethical implications related to data in the context of immersive technologies. |
Course Flow Plan
| Week | Subject | Related Preparation |
| 1) | Introduction to extended realities: AR, VR, MR, XR definitions—evolution and current state. | |
| 2) | Overview of the Metaverse concept: implications for data science and analytics. | |
| 3) | Data opportunities in AR and VR: collection, tracking, and visual analytics. | |
| 4) | MR and XR use cases: analyzing data for business and entertainment applications. | |
| 5) | Data security and privacy in immersive environments: threats and prevention strategies. | |
| 6) | Behavioral data analysis: understanding user engagement and interaction patterns. | |
| 7) | The role of artificial intelligence in enhancing XR experiences: data-driven personalization. | |
| 8) | Midterm Exam | |
| 9) | Ethical considerations and the responsibility of data handling in XR. | |
| 10) | User-generated content and data in the Metaverse: benefits and challenges. | |
| 11) | Networking and data transmission in XR technologies: latency, bandwidth, and reliability. | |
| 12) | Building data pipelines for real-time interaction analysis in XR environments. | |
| 13) | Case studies: successes and failures in leveraging data for AR and VR. | |
| 14) | Predictive analytics in XR: forecasting trends and user behavior. | |
| 15) | Review session and discussion on emerging trends and future research in XR data science. | |
| 16) | final exam |
Sources
| Course Notes / Textbooks: | Herhangi bir ders kitabı bulunmamaktadır. There is no textbook. |
| References: | Güncel makaleler, kitaplar kullanılacaktır. Current articles and books will be used. |
Course - Program Learning Outcome Relationship
| Course Learning Outcomes | 1 |
2 |
3 |
4 |
5 |
|---|---|---|---|---|---|
| Program Outcomes | |||||
| 1) Students who successfully complete this program, Knows the scope of technical applications of data science and the tools that can be used. | 3 | 3 | 3 | 3 | 3 |
| 2) Students who successfully complete this program, Knows the effects of application results on society-culture-law. | 3 | 3 | 2 | 3 | 3 |
| 3) Students who complete this program; Recognize the mathematics and code in application processes | 3 | 3 | 3 | 3 | 3 |
| 4) Students who complete this program; Explain the effects of the processes in data science on the output and the individual | 3 | 3 | 3 | 3 | 3 |
| 5) Students who successfully complete this program, Understands the insight-foresight and foresight created by data science as a whole in the face of a certain discipline/case. | 3 | 3 | 3 | 3 | 3 |
Course - Learning Outcome Relationship
| No Effect | 1 Lowest | 2 Average | 3 Highest |
| Program Outcomes | Level of Contribution | |
| 1) | Students who successfully complete this program, Knows the scope of technical applications of data science and the tools that can be used. | 2 |
| 2) | Students who successfully complete this program, Knows the effects of application results on society-culture-law. | 3 |
| 3) | Students who complete this program; Recognize the mathematics and code in application processes | 3 |
| 4) | Students who complete this program; Explain the effects of the processes in data science on the output and the individual | 3 |
| 5) | Students who successfully complete this program, Understands the insight-foresight and foresight created by data science as a whole in the face of a certain discipline/case. | 3 |
Assessment & Grading
| Semester Requirements | Number of Activities | Level of Contribution |
| Midterms | 1 | % 40 |
| Final | 1 | % 60 |
| total | % 100 | |
| PERCENTAGE OF SEMESTER WORK | % 40 | |
| PERCENTAGE OF FINAL WORK | % 60 | |
| total | % 100 | |
Workload and ECTS Credit Calculation
| Activities | Number of Activities | Workload |
| Course Hours | 14 | 51 |
| Midterms | 8 | 29 |
| Final | 8 | 74 |
| Total Workload | 154 | |