Institute of Graduate Studies

Biological Data Science

  1. About the Program

Biological data science, which is an interdisciplinary branch, has a wide range of applications and can be defined as the organization, processing and interpretation of big data generated in health and other biological sciences.

With the Internet age, it has utmost importance to process and interpret information accumulated exponentially in every field. Since the accumulated data in the field of biology and health is directly or indirectly related to human health, it is possible to produce new groundbreaking results with the analysis of recent data. The main objective of the program is, with the help of opportunities offered by technology, to make all kinds of information gathered in the field of biology and health available and useful. In order to accomplish this objective, the program aims to train data scientists who

• Know scientific steps to obtain meaningful results from complex data,

• Can clean and organize data,

• Can visualize, describe and find patterns in the data

• Know the stages in data analysis,

• Know the basic methods in data analysis,

• Know and can apply the recently developed data science methods and approaches parallel to their theses and/or undergraduate education,

• Have knowledge in different data science related disciplines and can communicate about its various aspects,

The importance and need for a “Data Scientist”, which is called the profession of the future in the world, is increasing day by day. Since the program requires interdisciplinary qualifications, undergraduate education is not available yet. With the help of this program, students who comes from different disciplines will be prepared for a doctorate program and will have an opportunity to learn common language. In addition, the theoretical knowledge and technological/computational tools made in the basic sciences will be implemented more rapidly and turned into beneficial resources for human health.

  1. Admission Requirements

Admission of students to the program will be made in accordance with the conditions specified in the Istanbul Medeniyet University Regulations on Graduate Education published in the Official Gazette dated January 13, 2012 and numbered 28172.

Graduates of Health, Biology, Engineering and social sciences may apply for this program. The student profile needed is discussed in each academic year by our program faculty and the admission requirements are announced on our website.

  1. Competencies of the Program

• Ability to use and develop theoretical and applied data analysis techniques in interdisciplinary work environments

• Ability to develop appropriate strategy, policy and implementations in collecting, analyzing, and interpreting data by considering psychological, sociological, cultural and ethical values

• Ability to follow up-to-date developments in the field of biological data science in both Turkish and English and to communicate in written, oral and/or visual forms by using recent developments

• Ability to demonstrate a critical approach and leadership in detecting and solving problems in biological data sciences

• Ability to understand the registry and record systems in health, propose new databases or make alterations in existing databases

• Ability to develop advanced technology products needed in health and to take active roles in related projects

• Ability to participate in molecular studies as a researcher and to analyze the data generated in these studies by using bioinformatics methods and tools

• Ability to model the nature of diseases and develop diagnostic tools

• Ability to take part in planning research projects

• Ability to teach basic biostatistics courses

  1. Academic Staff

Prof. Handan Ankaralı, PhD

School of Medicine, Department of Biostatistics and Medical Informatics

Prof. Hasan Güçlü, PhD

School of Medicine, Department of Biostatistics and Medical Informatics

Prof. Mahmut Gümüş, MD

School of Medicine, Department of Internal Medicine

Prof. Işıl Maral, MD, PhD

School of Medicine, Department of Public Health

Prof. Seyit Ankaralı, MD

School of Medicine, Department of Physiology

Prof. Nihat Kabaoğlu, PhD

School of Engineering and Natural Sciences, Department of Electrical and Electronic Engineering

Assoc. Prof. Rahmet Savaş, PhD

School of Engineering and Natural Sciences, Department of Mathematics

Assist. Prof. M. Erkan Karabekmez, PhD

School of Engineering and Natural Sciences, Department of Bioengineering

Assist. Prof. Ulaş Vural, PhD

School of Engineering and Natural Sciences, Department of Computer Engineering

Assist. Prof. Ayşe Betül Oktay, PhD

School of Engineering and Natural Sciences, Department of Computer Engineering

Assist. Prof. Mahmut Alpertunga Kara, PhD

School of Medicine, Department of Medical History and Ethics

Assist. Prof. Filiz Kısaayak Çollak, PhD

School of Engineering and Natural Sciences, Molecular Biology and Genetics

Assist. Prof. Arafat Salih Aydıner

School of Political Sciences, Department of Management Information Systems

  1. Curriculum

MASTER'S PROGRAM

1st SEMESTER

COURSE CODE

COURSE TITLE

THEORY

PRACTICE

CREDIT

ECTS

BVB 501

Biostatistics - I

3

0

3

7

BVB xxx

ELECTIVE COURSE I

3

0

3

7

BVB xxx

ELECTIVE COURSE II

3

0

3

7

BVB xxx

ELECTIVE COURSE III

3

0

3

7

TOTAL

12

0

12

28

2nd SEMESTER

COURSE CODE

COURSE TITLE

THEORY

PRACTICE

CREDIT

ECTS

BVB 502

Research Methods in Data Science

3

0

3

7

BVB 504

Bioethics

3

0

3

7

BVB xxx

ELECTIVE COURSE I

3

0

3

7

BVB xxx

ELECTIVE COURSE II

3

0

3

7

BVB 560

Seminar

1

2

12

4

TOTAL

12

0

24

32

GRAND TOTAL

24

2

24

60

1st SEMESTER ELECTIVE COURSES

COURSE CODE

COURSE TITLE

THEORY

PRACTICE

CREDIT

ECTS

BVB 503

Computer Programming

3

0

3

7

BVB 505

Differential Equations and Linear Algebra

3

0

3

7

BVB 507

Brain and Learning Mechanisms

3

0

3

7

BVB 509

Optimization Techniques

3

0

3

7

BVB 511

Epidemiology

3

0

3

7

BVB 513

Bioinformatics - I

3

0

3

7

BVB 515

Coding for Data Science

3

0

3

7

BVB 517

Clinical Research

3

0

3

7

BVB 519

Biostatistics - II

3

0

3

7

UMHB 522

Introduction to Algorithms and Data Structures

3

0

3

7

UMHB 529

Modeling and Simulation

3

0

3

7

2nd SEMESTER ELECTIVE COURSES

COURSE CODE

COURSE TITLE

THEORY

PRACTICE

CREDIT

ECTS

BVB 506

Multivariate Analysis - I

3

0

3

7

BVB 508

Strategic Management Information Systems

3

0

3

7

BVB 510

Social Network Analysis

3

0

3

7

BVB 512

Hospital Automation Systems and Cloud Computing

3

0

3

7

BVB 514

Molecular Biology and Genetics

3

0

3

7

BVB 516

Database Management Systems

3

0

3

7

BVB 518

Bioinformatics - II

3

0

3

7

BVB 520

Mathematical Methods in Data Analysis

3

0

3

7

UMHB 523

Principles of Artificial Intelligence

3

0

3

7

UMHB 520

Object Oriented Programming for Computational Sciences

3

0

3

7

UMHB 531

Data Mining and Analysis

3

0

3

7

EEM 554

Computer Vision

3

0

3

7

EEM 556

Pattern recognition

3

0

3

7

EEM 562

Biomedical Signal Processing

3

0

3

7

COURSE CODE

COURSE TITLE

THEORY

PRACTICE

CREDIT

ECTS

BVB 570

Field Course

4

0

0

15

BVB 580

Master Thesis

0

0

0

45

GRAND TOTAL

120

  1. Graduation Requirements

Students who are enrolled in the Master of Science (MSc) program in Biological Data Science are required to be successful by taking at least 24-credit courses and a seminar course and be successful in thesis defense exam. The student takes a minimum of 30 ECTS credits (Compulsory + Elective) in one semester. This number can be increased when necessary. Therefore, in order to graduate from this program, a total of 120 ECTS, 60 ECTS from the semester and 60 ECTS from the thesis, must be successfully completed.

  1. Employment Opportunities

The students who will graduate from the Biological Data Science Master Program can be employed as a Data Scientist in a wide range of strategic positions (Research Director, Expert Data Analyst, Data Manager, Data Science Consultant, Laboratory Researcher, Data Science Trainer, Hospital Statistics Unit Manager and Programmer) act as an effective element. In addition, students who graduate from this program, can apply for doctoral programs in Biostatistics, Data Science, Big Data Analytics, Cloud / Internet of Things or Medical Informatics and continue their academic studies.

  1. Contact information

Prof. Dr. Handan Ankaralı

Istanbul Medeniyet University, School of Medicine, Department of Biostatistics and Medical Informatics

Phone: 4018

E-mail: handanankarali@gmail.com