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: | DATS5018 | ||||
| Course Name: | Human-centered Data Science | ||||
| Semester: | Fall | ||||
| Course Credits: |
|
||||
| Language of instruction: | English | ||||
| Course Condition: | |||||
| Does the Course Require Work Experience?: | No | ||||
| Type of course: | Departmental Elective | ||||
| Course Level: |
|
||||
| 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 equip students with the necessary knowledge and skills to design and implement data science solutions that are responsive to and respectful of human needs and values. Emphasis will be placed on integrating human-centered design principles into data analytics, with a focus on ethical considerations, effective data communication, and the understanding of diverse stakeholder perspectives. By the end of the course, students should be able to create data science projects that are not only technically sound but also socially responsible and user-friendly. |
| Course Content: | 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 |
Learning Outcomes
|
The students who have succeeded in this course;
1) Apply human-centered design principles to data science projects. 2) Address ethical issues and cultivate responsible data practices. 3) Develop techniques for effective data visualization and storytelling. 4) Utilize UX research methods to inform data-driven decision-making. 5) Critically analyze real-world human-centered data science applications. |
Course Flow Plan
| Week | Subject | Related Preparation |
| 1) | Introduction to Human-Centered Data Science and course overview | - |
| 2) | Understanding human-centered design & its application in data science | - |
| 3) | Ethics in data science - Recognizing and addressing bias | - |
| 4) | Privacy and data protection - Best practices and legislation | - |
| 5) | Principles of effective data visualization for user communication | - |
| 6) | Introduction to UX research methods in Data Science | - |
| 7) | Data storytelling for diverse audiences and stakeholders | - |
| 8) | Midterm Exam | - |
| 9) | Case studies in ethical and human-centered data initiatives | - |
| 10) | Engagement strategies - Working with communities and users | - |
| 11) | Quantitative vs qualitative data in human-centered approaches | - |
| 12) | Implementing user feedback in data products and services | - |
| 13) | Iterative design and prototyping for data-driven applications | - |
| 14) | Accessibility in data science - Tools and guidelines | - |
| 15) | Critical analysis of data science in current societal issues | - |
| 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 | 2 | 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. | 2 |
| 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 | 56 |
| Final | 8 | 56 |
| Total Workload | 144 | |