COURSE UNIT TITLE

: DATA ANALYSIS IN OCEANOGRAPHY

DEGREE PROGRAMMES

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
CDK 5021 DATA ANALYSIS IN OCEANOGRAPHY COMPULSORY 3 0 0 7

Offered By

Graduate School of Natural and Applied Sciences

Level of Course Unit

Second Cycle Programmes (Master's Degree)

Course Coordinator

ASSISTANT PROFESSOR EYÜP MÜMTAZ TIRAŞIN

Offered to

MARINE CHEMISTRY
MARINE LIVING RESOURCES
MARINE LIVING RESOURCES

Course Objective

The aim of the course is to introduce modern data analysis tools to oceanographers via oceanographic data. All branches of oceanography (physical, chemical, geological and biological) use data sets in research. These are as different in character as are the disciplinary sources of the oceanographers. The statistical theory underlying these basic and widely employed data analysis tools is covered conceptually and also explained by using students' own data sets and personal computer statistical packages.

Learning Outcomes of the Course Unit

1   Recognize the great diversity of oceanographic data.
2   Recognize the need for sampling design and statistical analysis in oceanographic investigations.
3   Demonstrate an ability to formulate statistical hypotheses and design basic experiments.
4   Be able to collect raw oceanographic data and make them ready for statistical analysis.
5   Compute all basic statistics to describe and summarize the collected data and prepare various graphics visualizing the information contained by the data.
6   Apply basic univariate statistical analysis methods to make inferences about the collected data and draw meaningful and valid conclusions.
7   Become able to use certain statistical software (R Project for Statistical Computing) to organize, input and analyse data, and interpret results.
8   Demonstrate critical thinking skills by evaluating the strengths and weaknesses of their own research work and/or that of other researchers from a statistical point of view.

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description
1 Introduction Introduction to the Course, Statistics and Scientific Method, Role of Statistics in Oceanographic Research.
2 Nature of Oceanographic Data and Data Collection Types of Data (Variables), Populations and Samples, Sampling Techniques in Oceanography, Oceanographic Sampling Designs, Data Presentation Methods, Preparing Data for Presentation (Tabular and Graphical Presentations).
3 Review of Descriptive Statistics Measures of Central Tendency, Measures of Variability, Random Variables, Expected Value.
4 Probability Events and Basic Probability, Bivariate Probabilities, Conditional Probability, Permutation, Combination.
5 Review of Basic Discrete and Continuous Probability Distributions Binomial Distribution, Poisson Distribution, Uniform Distribution, Normal Distribution, t Distribution, Chi Square Distribution, F Distribution.
6 Estimation Techniques in Oceanographic Data Analysis Point and Interval Estimates of Population Parameters, Small-Sample Estimation, Sample Size and Estimation Error for Oceanographic Data.
7 Review of Hypothesis Testing Hypothesis Testing Steps and Procedures, Developing Decision Rules, Hypothesis Tests about a Population Mean, Hypothesis Tests about a Population Proportion, Type I and II Errors, Choosing the Significance Level in Hypothesis Testing.
8 Comparisons of Means and Variances Hypothesis Tests about the Difference between Two Population Means (Independent t-tests), Hypothesis Testing for Means of Paired Samples (Dependent t-tests), F-test.
9 Midterm exam
10 Introduction to R Statistical Package Statistical Modules in the R Package, Data Import and Export, Graphics, t-tests, F-test, Case Study: Physical Oceanographic Data Summarization.
11 Analysis of Variance (I) ANOVA Basics, Oneway ANOVA, F-Test and ANOVA Table, Case Study: Biological Oceanographic Data (R Application).
12 Analysis of Variance (II) Model I ANOVA, Model II ANOVA, Two-way ANOVA, Case Study: Chemical Oceanographic Data (R Application).
13 Midterm exam
14 Linear Regression and Correlation Simple Linear Regression, Coefficient of Determination, Correlation Coefficient, Difference between Linear Regression and Correlation Analyses.

Recomended or Required Reading

1. Text Books: (Appropriate parts of below listed books will constitute basic teaching material)

Dalgaard, P., 2002. Introductory Statistics with R. Springer, New York, USA.
Manly, Bryan F. J., 2000. Statistics for Environmental Science and Management. Chapman and Hall, New York, USA.
Snedecor, G. W. and Cochran, W. G., 1989. Statistical Methods (8th edition). Iowa State University Press. Ames, Iowa, USA.
Sokal, R. R. and Rohlf, F. J., 2012. Biometry (4th edition). W. H. Freeman Co., New York, USA.
Zar, J. H., 2010. Biostatistical Analysis (5th edition). Pearson Prentice-Hall, New Jersey, USA.

2. Lecture slides and handouts.

3. R software environment for statistical computing and graphics (R Project for Statistical Computing / http://www.r-project.org/).

Planned Learning Activities and Teaching Methods

1. Lectures
Class lectures are carried out in a highly interactive format. The instructor prompts students for response to questions posed and solicits their thoughts on issues discussed. Lectures will focus on the transfer of basic statistical concepts and techniques rather than rigorous mathematics. The emphasis will be put on the real world applications, and additional elaboration and illustration will be provided for better comprehension.

2. Class Discussions
In-class assignments and homework assignments are the basis of problems to be solved in classroom discussions. Individual participation by students in classroom discussions will be strongly encouraged.

3. Computer Applications
In the laboratory component, the R - free software environment for statistical computing and graphics (R Project for Statistical Computing) will be introduced to perform analyses of data and to produce graphics.

4. Calculator:
Students will need a scientific calculator (preferably one that can perform basic statistical functions for both one and two variable analyses) for various calculation problems in and out of class, and during exams.

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA
1 ASG ASSIGNMENT
2 MTE 1 MIDTERM EXAM 1
3 MTE 2 MIDTERM EXAM 2
4 FIN FINAL EXAM
5 PAR PARTICIPATION
6 FCG FINAL COURSE GRADE ASG * 0.15 + MTE 1 * 0.175 + MTE 2 * 0.175 + FIN * 0.40 + PAR * 0.10
7 RST RESIT
8 FCGR FINAL COURSE GRADE (RESIT) ASG * 0.15 + MTE 1 * 0.175 +MAKRMTE 2 * 0.175 + RST * 0.40 + PAR * 0.10


*** 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

English

Course Policies and Rules

1. Regular attendance is essential for satisfactory completion of this course. Statistics is a cumulative subject and each lesson day builds on the previous lessons' material. If you have excessive absences, you cannot develop to your fullest potential in the course.
2. The student is responsible for all homework assignments, changes in assignments or other verbal information given in the class, whether in attendance or not.
3. Homework assignments must be delivered at the beginning of the lesson on the date they are due.
4. Students are required to have their own calculator for this course.

Contact Details for the Lecturer(s)

Dr. E. Mümtaz TIRAŞIN
Dokuz Eylül University, Institute of Marine Sciences and Technology,
Inciraltı 35340, Balçova - Izmir.
Phone:(+90) 232 2785565 /165
Fax: (+90) 232 2785082
E-mail: mumtaz.tirasin@deu.edu.tr

Office Hours

To be announced.

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours)
Lectures 12 3 36
Preparing assignments 10 4 40
Preparation for midterm exam 2 7 14
Preparations before/after weekly lectures 11 5 55
Preparation for final exam 1 12 12
Midterm 2 3 6
Final 1 4 4
TOTAL WORKLOAD (hours) 167

Contribution of Learning Outcomes to Programme Outcomes

PO/LOPO.1PO.2PO.3PO.4PO.5PO.6PO.7PO.8PO.9PO.10PO.11
LO.154423313323
LO.233134232234
LO.345312144242
LO.422112225221
LO.523153242322
LO.643424321131
LO.735241112321
LO.832532133413

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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