| Data Science (Master) (with Thesis) | |||||
| Master | TR-NQF-HE: Level 7 | QF-EHEA: Second Cycle | EQF-LLL: Level 7 | ||
| Course Code: | VB5021 | ||||
| Course Name: | Sustainability and Data Science | ||||
| Semester: | Fall | ||||
| Course Credits: |
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| Language of instruction: | Turkish | ||||
| Course Condition: | |||||
| Does the Course Require Work Experience?: | No | ||||
| Type of course: | Departmental Elective | ||||
| Course Level: |
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| Mode of Delivery: | Face to face | ||||
| Course Coordinator: | Doç. Dr. ŞEBNEM ÖZDEMİR | ||||
| Course Lecturer(s): | Şebnem Özdemir | ||||
| Course Assistants: |
| Course Objectives: | The aim of this course is to introduce students to sustainability from a data science perspective and to provide them with the skills to achieve sustainability goals through data use and analytics. |
| Course Content: | 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 |
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The students who have succeeded in this course;
1) 1. Understand and evaluate the concept of sustainability 2) 2. To be able to use data science tools in sustainability studies 3) 3. Apply data collection and analysis methods for sustainability 4) 4. Develop data-driven sustainability strategies 5) 5. Understand and apply the principles of sustainability and data ethics |
| Week | Subject | Related Preparation |
| 1) | Week 1 Introduction to sustainability and data science | No Preliminary Preparation Required. |
| 2) | Week 2: The concept and importance of sustainability | No Preliminary Preparation Required. |
| 3) | Week 3: Data science fundamentals and tools | No Preliminary Preparation Required. |
| 4) | Week 4: Collecting and analyzing sustainability data | No Preliminary Preparation Required. |
| 5) | Week 5: Data-driven sustainability strategies | No Preliminary Preparation Required. |
| 6) | Week 6: Sustainability projects and practices | No Preliminary Preparation Required. |
| 7) | Week 7: Sustainability and data ethics | No Preliminary Preparation Required. |
| 8) | Week 8 Midterm exam | No Preliminary Preparation Required. |
| 9) | Week 9: Using data analytics techniques for sustainability | No Preliminary Preparation Required. |
| 10) | Week 10: Big data and sustainability | No Preliminary Preparation Required. |
| 11) | Week 11: Data visualization and sustainability reporting | No Preliminary Preparation Required. |
| 12) | Week 12: Machine learning and sustainability | No Preliminary Preparation Required. |
| 13) | Week 13: Data security and sustainability | No Preliminary Preparation Required. |
| 14) | Week 14: Social data and sustainability | No Preliminary Preparation Required. |
| 15) | Week 15: Data management and sustainability | No Preliminary Preparation Required. |
| 16) | Week 16 Final exam | No Preliminary Preparation Required. |
| Course Notes / Textbooks: | 1. Kitchin, R. (2014). The data revolution: Big data, open data, data infrastructures and their consequences. Sage. |
| References: | 1. Borgman, C. L. (2015). Big data, little data, no data: Scholarship in the networked world. MIT Press. |
| 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 | 2 | 2 |
| 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; Recognizes mathematics and code in application processes. | 2 | 3 | 3 | 3 | 3 |
| 4) Students who complete this program; Explain the effects of processes in data science on output and individual. | 3 | 2 | 3 | 2 | 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 | 2 | 3 |
| 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; Recognizes mathematics and code in application processes. | 2 |
| 4) | Students who complete this program; Explain the effects of processes in data science on output and 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 |
| 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 | |
| Activities | Number of Activities | Workload |
| Course Hours | 14 | 28 |
| Midterms | 7 | 55 |
| Final | 8 | 67 |
| Total Workload | 150 | |