BİS542 Data Processing

4 ECTS - 1-2 Duration (T+A)- . Semester- 2 National Credit
Information About Course

Information
Goal, Content & Resources
Course Learning Outcomes
Relation with Program Learning Outcome
Week Plan
Student Workload - ECTS

Information

Code BİS542
Name Data Processing
Semester . Semester
Duration (T+A) 1-2 (T-A) (17 Week)
ECTS 4 ECTS
National Credit 2 National Credit
Teaching Language Türkçe
Level Yüksek Lisans Dersi
Type Normal
Mode of study Yüz Yüze Öğretim
Catalog Information Coordinator


Course Goal

To be able to comprehend and apply the steps of data collection, data preparation, data entry, processing, data output/interpretation and data storage

Course Content

Data processing occurs when data is collected and translated into usable information. Usually performed by a data scientist or team of data scientists, it is important for data processing to be done correctly as not to negatively affect the end product, or data output. Data processing starts with data in its raw form and converts it into a more readable format (graphs, documents, etc.), giving it the form and context necessary to be interpreted by computers and utilized by employees throughout an organization.

Course Precondition

none

Resources

Computer Fundamental & Data Processing Paperback – 1 December 2010 by Satish Jain

Notes

Data Processing Handbook for Complex Biological Data Sources Edited by: Gauri Misra


Course Learning Outcomes

Order Course Learning Outcomes
LO01 decides in which database the data will be stored.
LO02 Can Collect data in the field of health.
LO03 Transfers data between databases.
LO04 visualizes data using graphs.


Relation with Program Learning Outcome

Order Type Program Learning Outcomes Level
PLO01 Bilgi - Kuramsal, Olgusal Students comprehends the fundamentals of statistical theory related to the field of health ( probability and bayesian biostatistics).
PLO02 Bilgi - Kuramsal, Olgusal Students explain demographic terminologies and statistical methods in the field of health sciences.
PLO03 Beceriler - Bilişsel, Uygulamalı Students understand and use medical terminology.
PLO04 Beceriler - Bilişsel, Uygulamalı Students collect data from research studies, analyze, and make inferences 4
PLO05 Beceriler - Bilişsel, Uygulamalı Students knows the system of international classification of diseases, obtain and analyze hospital statistics. 5
PLO06 Beceriler - Bilişsel, Uygulamalı Students design scientific research studies in order to give response to the problem arising from health and clinical sciences
PLO07 Beceriler - Bilişsel, Uygulamalı Students select the appropriate statistical procedure for analysis , apply and make inferences. 4
PLO08 Beceriler - Bilişsel, Uygulamalı Students use the necessary statistical packages for analysis, if necessary write and develop software. 3
PLO09 Beceriler - Bilişsel, Uygulamalı Students follow the latest development in medical informatics and employ frequently used tools and methods. 3
PLO10 Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği Students design health survey, determine the sampling method and conduct the survey
PLO11 Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği Students select and use proper statistical procedure for diagnosis and in making inferences for the data in health and clinical medicine and provide consultance to clinicians in the field.
PLO12 Yetkinlikler - Öğrenme Yetkinliği Students develop the ability of critical thinking, make a conclusion with a critical approach to the evidence
PLO13 Yetkinlikler - Öğrenme Yetkinliği Students apply analytical procedure to frequently used survival data, multivariate procedure and regression techniques.
PLO14 Yetkinlikler - İletişim ve Sosyal Yetkinlik Students provide consulting services by using effective communication skills; take part in research teamworks; defend the ethical rules.
PLO15 Yetkinlikler - Alana Özgü Yetkinlik Students explain the fundamental terminologies in epidemiology, guide researchers conducting field survey and clinical studies, develop methodologies in determining disease risk factor and disease burden and advise for choosing proper diagnostic test.


Week Plan

Week Topic Preparation Methods
1 Introduction to data processing reading Öğretim Yöntemleri:
Anlatım
2 Data collection reading Öğretim Yöntemleri:
Anlatım, Soru-Cevap
3 Data preparation reading Öğretim Yöntemleri:
Anlatım, Alıştırma ve Uygulama
4 Databases reading Öğretim Yöntemleri:
Anlatım, Tartışma
5 Databases II reading Öğretim Yöntemleri:
Anlatım, Alıştırma ve Uygulama, Soru-Cevap, Tartışma
6 Data inputation reading Öğretim Yöntemleri:
Alıştırma ve Uygulama, Tartışma
7 Transfer between databases reading Öğretim Yöntemleri:
Anlatım, Alıştırma ve Uygulama, Gösteri, Tartışma, Soru-Cevap
8 Mid-Term Exam none Ölçme Yöntemleri:
Ödev
9 Processing reading Öğretim Yöntemleri:
Anlatım
10 Processing II reading Öğretim Yöntemleri:
Anlatım, Alıştırma ve Uygulama, Soru-Cevap, Tartışma
11 Data output reading Öğretim Yöntemleri:
Anlatım, Soru-Cevap, Alıştırma ve Uygulama
12 Data interpretation reading Öğretim Yöntemleri:
Anlatım, Soru-Cevap, Alıştırma ve Uygulama
13 Data storage reading Öğretim Yöntemleri:
Anlatım, Alıştırma ve Uygulama, Soru-Cevap
14 Visualization of data -I reading Öğretim Yöntemleri:
Anlatım, Alıştırma ve Uygulama
15 Visualization of data -II reading Öğretim Yöntemleri:
Gösteri, Alıştırma ve Uygulama, Tartışma
16 Term Exams none Ölçme Yöntemleri:
Proje / Tasarım, Ödev
17 Term Exams none Ölçme Yöntemleri:
Ödev, Proje / Tasarım


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 0 0 0
Mid-term Exams (Written, Oral, etc.) 1 7 7
Final Exam 1 18 18
Total Workload (Hour) 109
Total Workload / 25 (h) 4,36
ECTS 4 ECTS