COURSE UNIT TITLE

: HEALTH DATA ANALYSIS

DEGREE PROGRAMMES

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
OKE 6006 HEALTH DATA ANALYSIS ELECTIVE 1 2 0 8

Offered By

Canser Epidemiology Doctorate

Level of Course Unit

Third Cycle Programmes (Doctorate Degree)

Course Coordinator

PROFESSOR DOCTOR HÜLYA ELLIDOKUZ

Offered to

Canser Epidemiology Doctorate
Canser Epidemiology

Course Objective

Provides health science students with practical experience in preparing, analyzing and reporting findings. Epidemiological and other health-related data. To be able to make analysis using the existing epidemiological data sets. Data cleanup, data file creation and management, basic descriptive statistics, analytical strategies, biostatistical analysis and data interpretation, analysis and reporting of findings. Analysis of current health data.

Learning Outcomes of the Course Unit

1   Knows the basic concepts of cancer data.
2   Access to cancer databases.
3   will be able to understand mathematical and statistical methods used in the studies
4   Know the concept of big data in the field of cancer and apply analysis methods.
5   To be able to make statistical analysis and reporting in cancer researches

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description
1 Basic concepts of cancer data -1
2 Basic concepts of cancer data -2
3 Basic statistical methods-1
4 Basic statistical methods-2
5 Data cleaning and data file creation
6 Preparing, analyzing and interpreting the findings
7 Big Data Concept and Omics
8 Canceromic concepts (Genomics, Proteomics, Lipidomics, Metabolic, Interactomics)
9 Microarray data analysis
10 Rna sequence data analy
11 Term presentations-1
12 Presentations-2
13 An overview
14 Final exam

Recomended or Required Reading

High-Dimensional Data Analysis in Cancer Research, Editors: Xiaochun Li, Ronghui Xu, Springer-Verlag New York, 2009.

Planned Learning Activities and Teaching Methods

Making theoretical lessons, Preparing for exam, homework preparation, laboratory practice

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA
1 ASG ASSIGNMENT
2 FIN FINAL EXAM
3 FCG FINAL COURSE GRADE ASG * 0.50+ FIN* 0.50
4 RST RESIT
5 FCGR FINAL COURSE GRADE (RESIT) ASG * 0.50+ RST* 0.50


*** Resit Exam is Not Administered in Institutions Where Resit is not Applicable.

Further Notes About Assessment Methods

None

Assessment Criteria

To be announced.

Language of Instruction

Turkish

Course Policies and Rules

To be announced.

Contact Details for the Lecturer(s)

hulya.ellidokuz@deu.edu.tr 0 232 4125889/5801

Office Hours

Tuesday 12.30-13.30

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours)
Lectures 15 3 45
Preparations before/after weekly lectures 10 2 20
Preparation for final exam 1 30 30
Preparing assignments 5 10 50
Preparing presentations 5 10 50
Final 1 2 2
TOTAL WORKLOAD (hours) 197

Contribution of Learning Outcomes to Programme Outcomes

PO/LOPO.1PO.2PO.3PO.4PO.5PO.6PO.7PO.8PO.9PO.10PO.11PO.12
LO.155555
LO.255555
LO.355555
LO.455555
LO.555555

CONTACT INFORMATION
Dokuz Eylül Üniversitesi Cumhuriyet Bulvarı No: 144 35210 Alsancak / İZMİR
Phone: +90(232) 412 12 12 - Fax: +90 (232) 464 81 35

Copyright 2015 DEÜ. BİD

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