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: | DATS5022 | ||||
| Course Name: | Sustainability and Data Science | ||||
| Semester: | Spring | ||||
| 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: | Araş. Gör. KAZIM TİMUÇİN UTKAN | ||||
| Course Lecturer(s): | |||||
| Course Assistants: |
Course Objective and Content
| Course Objectives: | The aim of this course is to provide students with an in-depth understanding of how Data Science can be applied to address and promote sustainability. Students will explore the role of data, metrics, and analytical techniques in understanding environmental issues and developing sustainable practices. This course will empower students to merge technical data science skills with sustainability concepts to drive innovation in tackling ecological challenges. Throughout the course, students will engage with case studies, practical exercises, and current research to synthesize a data-driven approach to sustainability. |
| Course Content: | 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 |
Learning Outcomes
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The students who have succeeded in this course;
1) Gain a comprehensive understanding of sustainability concepts within data science frameworks. 2) Develop proficiency in environmental data collection, management, and interpretation. 3) Learn to critically analyze and evaluate sustainability practices using data analytics. 4) Be able to design and implement strategies that employ data science for sustainable development goals. 5) Acquire hands-on experience with tools and technologies pivotal for sustainability-focused data projects. |
Course Flow Plan
| Week | Subject | Related Preparation |
| 1) | Course Introduction and Understanding the Sustainability Data Science Paradigm. | - |
| 2) | Exploring Environmental Datasets and Global Sustainability Indicators. | - |
| 3) | Data Management Principles for Sustainability - Ethical and Legal Considerations. | - |
| 4) | Statistical Methods and Machine Learning for Environmental Data. | - |
| 5) | Visualization Techniques for Communicating Sustainability Data. | - |
| 6) | Using Big Data to Drive Environmental Policy and Decision-making. | - |
| 7) | Lifecycle Assessment and Environmental Impact through Data Analytics. | - |
| 8) | Midterm Exam - Application of Concepts in Real-World Sustainability Challenges. | - |
| 9) | IoT and Remote Sensing for Real-Time Environmental Monitoring. | - |
| 10) | Advanced Machine Learning Models for Predicting Environmental Outcomes. | - |
| 11) | Project Management and Workflow Optimization for Sustainability Data Projects. | - |
| 12) | Industry Case Studies: Energy, Agriculture, and Conservation. | - |
| 13) | Workshops on AI Tools and Frameworks for Sustainable Solutions Design. | - |
| 14) | Student Presentations of Sustainability Data Projects. | - |
| 15) | Review of Current Research and Future Trends in Sustainability 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 | 3 | 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. | 3 |
| 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 | 16 | 32 |
| Midterms | 8 | 83 |
| Final | 9 | 27 |
| Total Workload | 142 | |