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Information
| Unit | INSTITUTE OF MEDICAL SCIENCES |
| BIOSTATISTICS (MEDICINE) (MASTER) (WITHOUT THESIS) (EVENING EDUCATION) | |
| Code | BIST514 |
| Name | Data Science in Biostatistics and Artificial Intelligence |
| Term | 2025-2026 Academic Year |
| Term | Spring |
| Duration (T+A) | 1-2 (T-A) (17 Week) |
| ECTS | 5 ECTS |
| National Credit | 2 National Credit |
| Teaching Language | Türkçe |
| Level | Lisansüstü Dersi |
| Type | Normal |
| Mode of study | Yüz Yüze Öğretim |
| Catalog Information Coordinator | Doç. Dr. İLKER ÜNAL |
| Course Instructor |
Doç. Dr. İLKER ÜNAL
(Bahar)
(A Group)
(Ins. in Charge)
|
Course Goal / Objective
The aim of this course is to enable students to understand data science and artificial intelligence methods used in health sciences, correctly interpret AI-based results from statistical and clinical perspectives, and integrate these outputs into evidence-based practice.
Course Content
This course covers the application of data science and artificial intelligence approaches in health sciences from a biostatistical perspective. Topics include large-scale health data, data preprocessing, feature selection, supervised and unsupervised learning concepts, classification and clustering approaches, model performance metrics, prediction and risk scoring, overfitting, model validation, clinical interpretation of results, and ethical considerations. Applications are conducted using health-related datasets.
Course Precondition
No prerequisites. (Basic knowledge of biostatistics and statistical software is recommended.)
Resources
James, G., Witten, D., Hastie, T., Tibshirani, R. An Introduction to Statistical Learning
Notes
Recent research articles on data science and AI in healthcare
Course Learning Outcomes
| Order | Course Learning Outcomes |
|---|---|
| LO01 | Explain data science and artificial intelligence concepts used in health sciences. |
| LO02 | Describe data preprocessing steps for health-related data. |
| LO03 | Distinguish between supervised and unsupervised learning approaches. |
| LO04 | Interpret outputs of basic machine learning models. |
| LO05 | Evaluate model performance using appropriate metrics. |
| LO06 | Discuss the clinical use of prediction and risk models. |
| LO07 | Explain ethical, privacy, and data security principles in AI applications. |
Relation with Program Learning Outcome
| Order | Type | Program Learning Outcomes | Level |
|---|---|---|---|
| PLO01 | Bilgi - Kuramsal, Olgusal | Explain basic biostatistics, probability, and demographic concepts used in health sciences. | |
| PLO02 | Bilgi - Kuramsal, Olgusal | Define research designs, sampling methods, and data types used in health research. | |
| PLO03 | Bilgi - Kuramsal, Olgusal | Explain the foundations of statistical approaches used in healthcare decision-making processes. | 2 |
| PLO04 | Bilgi - Kuramsal, Olgusal | Explain the basic logic of regression, modeling, and advanced statistical methods used in health sciences. | 2 |
| PLO05 | Beceriler - Bilişsel, Uygulamalı | Analyze and interpret data obtained from health research using appropriate statistical methods. | 3 |
| PLO06 | Beceriler - Bilişsel, Uygulamalı | Perform statistical analyses and generate outputs using statistical software packages. | 3 |
| PLO07 | Beceriler - Bilişsel, Uygulamalı | Apply basic data science, artificial intelligence, and machine learning applications in health sciences. | 5 |
| PLO08 | Beceriler - Bilişsel, Uygulamalı | Evaluate multiple regression and survival analysis results in a clinical context. | |
| PLO09 | Beceriler - Bilişsel, Uygulamalı | Analyze genetic and biomedical data using basic analytical approaches. | |
| PLO10 | Beceriler - Bilişsel, Uygulamalı | Apply scale development, validity, and reliability analyses. | |
| PLO11 | Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği | Conduct data analysis and reporting within the scope of a term project. | |
| PLO12 | Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği | Apply and manage sampling procedures in health studies. | |
| PLO13 | Yetkinlikler - Öğrenme Yetkinliği | Critically evaluate scientific studies from a statistical perspective. | 2 |
| PLO14 | Yetkinlikler - İletişim ve Sosyal Yetkinlik | Present analysis results in accordance with ethical principles. | 2 |
| PLO15 | Yetkinlikler - Alana Özgü Yetkinlik | Applies fundamental concepts of epidemiology and health statistics to clinical and field settings. | 3 |
Week Plan
| Week | Topic | Preparation | Methods |
|---|---|---|---|
| 1 | Course introduction; overview of data science and AI | Reading | Öğretim Yöntemleri: Anlatım |
| 2 | Structure of health data and big data | Reading | Öğretim Yöntemleri: Anlatım |
| 3 | Data preprocessing and feature selection | Reading | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 4 | Introduction to supervised learning | Reading | Öğretim Yöntemleri: Anlatım |
| 5 | Classification approaches | Reading | Öğretim Yöntemleri: Anlatım |
| 6 | Unsupervised learning and clustering | Reading | Öğretim Yöntemleri: Anlatım |
| 7 | Model evaluation metrics | Reading | Öğretim Yöntemleri: Anlatım |
| 8 | Mid-Term Exam | Ölçme Yöntemleri: Sözlü Sınav |
|
| 9 | Prediction and risk scores | Reading | Öğretim Yöntemleri: Anlatım |
| 10 | Model validation and overfitting | Reading | Öğretim Yöntemleri: Anlatım |
| 11 | AI applications in healthcare I | Reading | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 12 | AI applications in healthcare II | Reading | Öğretim Yöntemleri: Anlatım, Alıştırma ve Uygulama |
| 13 | Ethics, privacy, and data security | Reading | Öğretim Yöntemleri: Anlatım |
| 14 | General review and applications I | Reading | Öğretim Yöntemleri: Soru-Cevap, Tartışma, Alıştırma ve Uygulama |
| 15 | General review and applications II | Reading | Öğretim Yöntemleri: Tartışma, Soru-Cevap, Alıştırma ve Uygulama |
| 16 | Term Exams | Ölçme Yöntemleri: Ödev |
|
| 17 | Term Exams | Ölçme Yöntemleri: Ödev |
Student Workload - ECTS
| Works | Number | Time (Hour) | Workload (Hour) |
|---|---|---|---|
| Course Related Works | |||
| Class Time (Exam weeks are excluded) | 14 | 3 | 42 |
| Out of Class Study (Preliminary Work, Practice) | 14 | 3 | 42 |
| Assesment Related Works | |||
| Homeworks, Projects, Others | 1 | 15 | 15 |
| Mid-term Exams (Written, Oral, etc.) | 1 | 15 | 15 |
| Final Exam | 1 | 15 | 15 |
| Total Workload (Hour) | 129 | ||
| Total Workload / 25 (h) | 5,16 | ||
| ECTS | 5 ECTS | ||