BİS669

5 ECTS - 2-3 Duration (T+A)- . Semester- 2 National Credit

Information

Code BİS669
Name
Term 2022-2023 Academic Year
Term Spring
Duration (T+A) 2-3 (T-A) (17 Week)
ECTS 5 ECTS
National Credit 2 National Credit
Teaching Language Türkçe
Level Doktora Dersi
Type Normal
Mode of study Yüz Yüze Öğretim
Catalog Information Coordinator
Course Instructor
1


Course Goal / Objective

With this course, it is aimed that the student will do high-performance coding with R, simulate data with resampling and Monte Carlo methods, and simulate complex data.

Course Content

In this course, advanced simulation techniques will be explained with applications. The course content will cover the use of R for simulation, random number generation, Monte Carlo approach, resampling methods and complex data simulations.

Course Precondition

None

Resources

Matthias Templ, Simulation for Data Science with R, Plackt Publishing, 2016.

Notes

Greasley, Andrew. Simulation Modelling Concepts, Tools and Practical Business Applications. Routledge, 2023.


Course Learning Outcomes

Order Course Learning Outcomes
LO01 Performs high-performance coding with R
LO02 Knows random numbers generation mechanisms and generate random numbers.
LO03 Does Monte Carlo applications
LO04 Counts and applies resampling techniques.
LO05 Simulates complex data.


Relation with Program Learning Outcome

Order Type Program Learning Outcomes Level
PLO01 Bilgi - Kuramsal, Olgusal Comprehends the original definitions, concepts and theorems that will bring innovation to the field based on the qualifications gained in the biostatistics master's program. 3
PLO02 Bilgi - Kuramsal, Olgusal Using knowledge that requires expertise, analyzes, evaluates and interprets new and complex ideas in the field and related fields. 3
PLO03 Bilgi - Kuramsal, Olgusal He/She has advanced knowledge about technological tools and software that are frequently used in the field of biostatistics. 5
PLO04 Bilgi - Kuramsal, Olgusal Knows the importance of ethical principles and ethical committees for the individual and society. Comprehends the importance of Biostatistician in ethics committees.
PLO05 Bilgi - Kuramsal, Olgusal He/She has advanced knowledge about statistical methods that are frequently used in studies in the field of health. 3
PLO06 Beceriler - Bilişsel, Uygulamalı Evaluates the knowledge in the field of biostatistics with a systematic approach
PLO07 Beceriler - Bilişsel, Uygulamalı Develops a new idea, method, design or application that brings innovation to the field of biostatistics, develops a known idea, method, design or application and applies it to a different field.
PLO08 Beceriler - Bilişsel, Uygulamalı Design, analyzes critically, interprets and reports observational and clinical researchs for new and complex problems in medicine and health sciences.
PLO09 Beceriler - Bilişsel, Uygulamalı He/She uses advanced statistical methods in the decision-making process in diagnosis and treatment in health sciences, and consults to researchers working in this field. 3
PLO10 Beceriler - Bilişsel, Uygulamalı Uses research and analysis methods that require high-level skills in studies related to the field of biostatistics. 3
PLO11 Beceriler - Bilişsel, Uygulamalı Develops and applies advanced statistical methods and techniques frequently used in health sciences at the level of expertise with original thought, research.
PLO12 Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği Performs independently an original work that brings innovation to the field of biostatistics 4
PLO13 Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği Performs advanced statistical analysis that can evaluate a scientific article. 4
PLO14 Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği Develops the ability to read and write articles related to the field of biostatistics and apply for articles to national and/or international refereed journals.
PLO15 Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği Takes an active role in solving original and interdisciplinary problems
PLO16 Yetkinlikler - Öğrenme Yetkinliği Develops new ideas and methods in the field of Biostatistics by using high-level mental processes such as creative and critical thinking, problem solving and decision making.
PLO17 Yetkinlikler - Öğrenme Yetkinliği Comprehends the ways to reach the evidence and evaluates the evidence critically.
PLO18 Yetkinlikler - Öğrenme Yetkinliği He/She determines the principles of lifelong learning and professional development as an attitude and displays this attitude in his/her works.
PLO19 Yetkinlikler - İletişim ve Sosyal Yetkinlik Understands the dynamics of social relations required by the health profession and critically evaluates and develops the norms that guide these relations.
PLO20 Yetkinlikler - İletişim ve Sosyal Yetkinlik Discusses the issues in the field with other experts in interdisciplinary studies, using effective communication skills, and provides academic consultancy by defending his/her original views.
PLO21 Yetkinlikler - İletişim ve Sosyal Yetkinlik Communicates written, verbal and visual with foreign language knowledge in international scientific environments
PLO22 Yetkinlikler - Alana Özgü Yetkinlik By using the knowledge of biostatistics and medical informatics, he/she contributes to the society's becoming an information society by presenting his/her knowledge and skills to his/her society.
PLO23 Yetkinlikler - Alana Özgü Yetkinlik Establishes functional interaction by defending original views in solving problems related to biostatistics
PLO24 Yetkinlikler - Alana Özgü Yetkinlik Consults using effective communication skills, takes part in teamwork in research, defends scientific ethical rules
PLO25 Yetkinlikler - Alana Özgü Yetkinlik He/She has the experience of working with other health disciplines as a requirement of the field.
PLO26 Yetkinlikler - Alana Özgü Yetkinlik He/she chooses and applies the correct statistical methods in his/her studies in the field of health and interprets them correctly. Performs advanced analysis and synthesis. 3
PLO27 Yetkinlikler - Alana Özgü Yetkinlik Uses current developments and information in the field of health for the benefit of society in line with the realities of the country.


Week Plan

Week Topic Preparation Methods
1 R and High-Performance Computing 1 Studying the appropriate chapter in the given resource book Öğretim Yöntemleri:
Anlatım, Alıştırma ve Uygulama, Tartışma
2 R and High-Performance Computing 2 Studying the appropriate chapter in the given resource book Öğretim Yöntemleri:
Anlatım, Alıştırma ve Uygulama, Tartışma
3 The Discrepancy between Pencil-Driven Theory and Data-Driven Computational Solutions Studying the appropriate chapter in the given resource book Öğretim Yöntemleri:
Anlatım, Soru-Cevap
4 Simulation of Random Numbers 1 Studying the appropriate chapter in the given resource book Öğretim Yöntemleri:
Anlatım, Gösterip Yaptırma
5 Simulation of Random Numbers 2 Studying the appropriate chapter in the given resource book Öğretim Yöntemleri:
Anlatım, Alıştırma ve Uygulama, Tartışma
6 Monte Carlo Methods for Optimization Problems Studying the appropriate chapter in the given resource book Öğretim Yöntemleri:
Anlatım, Alıştırma ve Uygulama
7 Probability Theory Shown by Simulation Studying the appropriate chapter in the given resource book Öğretim Yöntemleri:
Anlatım, Soru-Cevap
8 Mid-Term Exam Ölçme Yöntemleri:
Ödev
9 Resampling Methods 1 Studying the appropriate chapter in the given resource book Öğretim Yöntemleri:
Anlatım, Gösterip Yaptırma
10 Resampling Methods 2 Studying the appropriate chapter in the given resource book Öğretim Yöntemleri:
Anlatım, Alıştırma ve Uygulama, Tartışma
11 Applications of Resampling Methods and Monte Carlo Tests 1 Studying the appropriate chapter in the given resource book Öğretim Yöntemleri:
Anlatım, Gösterip Yaptırma
12 Applications of Resampling Methods and Monte Carlo Tests 2 Studying the appropriate chapter in the given resource book Öğretim Yöntemleri:
Anlatım, Alıştırma ve Uygulama, Tartışma
13 The EM Algorithm Studying the appropriate chapter in the given resource book Öğretim Yöntemleri:
Anlatım, Soru-Cevap, Gösterip Yaptırma
14 Simulation with Complex Data 1 Studying the appropriate chapter in the given resource book Öğretim Yöntemleri:
Anlatım, Gösterip Yaptırma
15 Simulation with Complex Data 2 Studying the appropriate chapter in the given resource book Öğretim Yöntemleri:
Alıştırma ve Uygulama, Tartışma
16 Term Exams Ölçme Yöntemleri:
Yazılı Sınav
17 Term Exams Ölçme Yöntemleri:
Yazılı Sınav


Student Workload - ECTS

Works Number Time (Hour) Workload (Hour)
Course Related Works
Class Time (Exam weeks are excluded) 14 5 70
Out of Class Study (Preliminary Work, Practice) 14 1 14
Assesment Related Works
Homeworks, Projects, Others 5 5 25
Mid-term Exams (Written, Oral, etc.) 0 0 0
Final Exam 1 10 10
Total Workload (Hour) 119
Total Workload / 25 (h) 4,76
ECTS 5 ECTS

Update Time: 17.11.2022 04:13