COURSE UNIT TITLE

: COMPUTER AIDED DATA ANALYSIS

DEGREE PROGRAMMES

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
AFY 7043 COMPUTER AIDED DATA ANALYSIS ELECTIVE 3 0 0 6

Offered By

DISASTER ADMINISTRATION, NON-THESIS (Evening)

Level of Course Unit

Second Cycle Programmes (Master's Degree)

Course Coordinator

ASSOCIATE PROFESSOR ISTEM KÖYMEN

Offered to

DISASTER ADMINISTRATION, NON-THESIS (Evening)

Course Objective

To be able to collect data in the field of disaster management with appropriate methods, to make statistical evaluations, to make comments, to give the skills to use statistical results, to develop statistical reasoning and to support them with statistical programming languages.

Learning Outcomes of the Course Unit

1   To be able to recognize the definition of statistics, its subject and its relationship with disaster management,
2   To be able to explain measures of central tendency and measures of change,
3   To be able to classify variables according to their properties,
4   To be able to display and interpret the data set graphically,
5   To be able to perform and interpret basic statistical analysis,
6   To be able to define the basic concepts of statistical science,
7   To be able to solve and interpret problems with the support of programming languages.

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description
1 Introduction to Statistics, Basic Concepts: Statistics, Population, Parameter, Variable, Data
2 Collection of Data, Classification, Graphical Representations: Frequency Distributions, Pie Charts, Bar Charts, Histogram, Frequency Polygon and Demonstration of the use of basic graphical representations with programming languages
3 Central Tendency Measures: Demonstration of the use of Arithmetic Mean, Mode, Median, Geometric Mean, Harmonic Mean, Quartiles and Central Tendency Measures with programming languages
4 Measures of Variability: Range, Standard Deviation, Variance, Absolute Mean Deviation, Coefficient of Variation and demonstration of the use of Variability measures with programming languages
5 Bowley Ve Pearson Skewness Measures . Demonstration of the use of asymmetry measures with programming languages
6 Confidence Intervals, Interpretation and demonstration of their use with programming languages
7 Hypothesis Testing, Interpretation and demonstration of its use with programming languages
8 Nonparametric tests, interpretation and demonstration of their use with programming langua
9 Correlation Analysis and demonstration of its use with programming languages
10 Regression anad logistic regression Analysis and demonstration of its use with programming languages
11 Survey resaerch, sample collection
12 Survey research,, scale development, scale adaptation
13 Analysis of survey research data with basic methods and sample application with package programs
14 Scientific article review on the use of basic statistical methods in disaster management

Recomended or Required Reading

1- R ile Istatistiksel Analiz ve Programlama, Nobel Akademik Yayıncılık, Hasan Bulut
2- R ile Veri Analizi, Istatistik, Modelleme,Uygulama, Sentez Yayıncılık, Hakan Emekçi,Suat Altan, Seçkin Yayıncılık.

Planned Learning Activities and Teaching Methods

Lecture Method, Question and Answer Method, Discussion Method and Problem Solving Method

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA
1 MTE MIDTERM EXAM
2 STT TERM WORK (SEMESTER)
3 FCGR FINAL COURSE GRADE
4 FCGR FINAL COURSE GRADE (RESIT) MTE * 0.20 + STT * 0.20 + FN* 0.60
5 RST RESIT
6 FCGR FINAL COURSE GRADE (RESIT) MTE * 0.20 + STT * 0.20 + RST* 0.60


Further Notes About Assessment Methods

None

Assessment Criteria

To be announced.

Language of Instruction

Turkish

Course Policies and Rules

1-Have current, theoretical and practical knowledge in the field of statistics

2-Define and solve problems in the field of disaster management by statistical methods

3-Uses abstract and analytical thinking skills

4- Uses computer software and programming knowledge at a level that can effectively apply statistical science

5-Collect, analyze and interpret data and determine appropriate statistical methods

6-Define statistical problems and develop solutions based on evidence and research.

Contact Details for the Lecturer(s)

Doç. Dr, Istem KÖYMEN: istem.koymen@deu.edu.tr

Office Hours

To be announced.

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours)
Lectures 14 3 42
Preparations before/after weekly lectures 14 3 42
Preparation for midterm exam 1 24 24
Preparation for final exam 1 30 30
Midterm 1 3 3
Final 1 3 3
TOTAL WORKLOAD (hours) 144

Contribution of Learning Outcomes to Programme Outcomes

PO/LOPO.1PO.2PO.3PO.4PO.5PO.6PO.7
LO.15
LO.25
LO.35
LO.45
LO.55
LO.65
LO.75

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

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