ISTINYE UNIVERSITY BİLGİ PAKETİ / DERS KATALOĞU
| Data Science (Master) (with Thesis) | |||||
| Master | TR-NQF-HE: Level 7 | QF-EHEA: Second Cycle | EQF-LLL: Level 7 | ||
Course Introduction and Application Information
| Course Code: | VB5025 | ||||
| Course Name: | Data Science Applications in Health Sciences | ||||
| Semester: |
Fall Spring |
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| 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: | E-Learning | ||||
| Course Coordinator: | Doç. Dr. ŞEBNEM ÖZDEMİR | ||||
| Course Lecturer(s): | |||||
| Course Assistants: |
Course Objective and Content
| Course Objectives: | The aim of this course is to teach students how to use machine learning techniques to understand and solve business problems. The course will focus on basic machine learning concepts, algorithms and how to incorporate these techniques into business strategies. Students will learn to work with real-world business data using data science and machine learning tools. |
| Course Content: | 1. Introduction: The Role and Importance of Machine Learning in Business 2. Supervised and Unsupervised Learning Techniques 3. Data Preprocessing and Feature Engineering 4. Creation and Evaluation of Regression and Classification Models 5. Integration of Machine Learning Algorithms into Business Strategies |
Learning Outcomes
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The students who have succeeded in this course;
1) Understand the basic concepts and terminology of machine learning. 2) Apply supervised and unsupervised learning techniques on business data. 3) Gain data preprocessing and feature engineering skills. 4) Learn to build and validate various regression and classification models. 5) Will be able to effectively integrate machine learning algorithms in business decision-making processes. |
Course Flow Plan
| Week | Subject | Related Preparation |
| 1) | Basic Concepts of Business and Data Science | |
| 2) | Introduction to Machine Learning | |
| 3) | Data Preprocessing Techniques | |
| 4) | Fundamentals of Supervised Learning | |
| 5) | Fundamentals of Supervised Learning | |
| 6) | Classification Algorithms | |
| 7) | Unsupervised Learning and Clustering | |
| 8) | Midterm Exam | |
| 9) | Model Evaluation Metrics | |
| 10) | Feature Engineering and Model Selection | |
| 11) | Time Series Analysis and Forecasting Models | |
| 12) | Decision Trees and Forests | |
| 13) | Artificial Neural Networks | |
| 14) | Machine Learning Applications in Business Strategies | |
| 15) | Case Study and Project Presentations | |
| 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; Recognizes mathematics and code in application processes. | 3 | 3 | 3 | 3 | 3 |
| 4) Students who complete this program; Explain the effects of processes in data science on output and individual. | 3 | 3 | 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 | 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; Recognizes mathematics and code in application processes. | 3 |
| 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 |
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 | 42 |
| Midterms | 8 | 55 |
| Final | 8 | 59 |
| Total Workload | 156 | |