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: | DATS5026 | ||||
| Course Name: | Data Science Applications in Health Sciences | ||||
| Semester: | Spring | ||||
| 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 goal of this course is to equip students with the necessary knowledge and hands-on experience to effectively apply data science techniques within the realm of health sciences. Students will learn about the unique challenges that health data presents and how to overcome them. The course will cover the full spectrum of data science applications, including data preprocessing, predictive modeling, and interpreting results in a manner that is meaningful to health professionals. We will also address the critical ethical and privacy issues integral to handling health data responsibly. |
| Course Content: | 1. Overview of Health Sciences and Data Science intersection 2. Data handling and preprocessing in Health Data 3. Machine learning models used for health data analysis 4. Evaluation and interpretation of models in health science 5. Ethical considerations and data privacy in Health Data Science |
Learning Outcomes
|
The students who have succeeded in this course;
1) Understand and articulate the unique aspects of health data and the application of data science in health sciences. 2) Acquire the skills necessary to preprocess, clean, and manage health-related datasets effectively. 3) Develop and evaluate machine learning models appropriate for solving problems within the health sciences domain. 4) Communicate findings from data science analyses to non-technical stakeholders in the health sciences. 5) Understand ethical and privacy considerations when dealing with health data and implement best practices to maintain data confidentiality and integrity. |
Course Flow Plan
| Week | Subject | Related Preparation |
| 1) | Introduction to Health Sciences and Data Science: Intersection and Opportunities. | - |
| 2) | Health data structures, types of health data, and their significance. | - |
| 3) | Data Governance, Privacy Laws, and Ethics in Health Data Science. | - |
| 4) | Preprocessing of health data: Dealing with missing data and noise. | - |
| 5) | Visualization techniques for health data analysis and interpretation. | - |
| 6) | Introduction to machine learning algorithms relevant to health data. | - |
| 7) | Feature selection, extraction, and dimensionality reduction techniques for health datasets. | - |
| 8) | Midterm Exam | - |
| 9) | Deep learning in health data: Opportunities and challenges. | - |
| 10) | Time-series analysis in health data for forecasting and trend analysis. | - |
| 11) | Natural Language Processing (NLP) for clinical notes and health records. | - |
| 12) | Predictive modeling and risk stratification in health sciences. | - |
| 13) | Model evaluation, validation, and the concept of overfitting in health datasets. | - |
| 14) | Case studies: From data to decision-making in healthcare settings. | - |
| 15) | Current trends and future directions in health 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 | 2 | 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 | 2 | 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. | 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 | 16 | 32 |
| Midterms | 8 | 61 |
| Final | 8 | 49 |
| Total Workload | 142 | |