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COURSE INFORMATION
Course CodeCourse TitleL+P HourSemesterECTS
BIO 654ECOLOGICAL MODELLING3 + 01st Semester7,5

COURSE DESCRIPTION
Course Level Master's Degree
Course Type Elective
Course Objective The preparing the data matrices on the data, similarity and dissimilarity coefficients, clustering techniques, discriminant analyses, prenciple component analyses, eigen vectors, PC1 _ PC2 plotting interperatations and MDS are teaching.
Course Content
Prerequisites No the prerequisite of lesson.
Corequisite No the corequisite of lesson.
Mode of Delivery Face to Face

COURSE LEARNING OUTCOMES

COURSE'S CONTRIBUTION TO PROGRAM
Data not found.

ECTS ALLOCATED BASED ON STUDENT WORKLOAD BY THE COURSE DESCRIPTION
ActivitiesQuantityDuration (Hour)Total Work Load (Hour)
Course Duration (14 weeks/theoric+practical)14342
Hours for off-the-classroom study (Pre-study, practice)14798
Assignments155
Mid-terms11515
Final examination13535
Total Work Load

ECTS Credit of the Course






195

7,5
COURSE DETAILS
 Select Year   


 Course TermNoInstructors
Details 2021-2022 Spring1EYUP BAŞKALE
Details 2018-2019 Spring1EYUP BAŞKALE
Details 2017-2018 Spring1EYUP BAŞKALE
Details 2016-2017 Spring1EYUP BAŞKALE
Details 2014-2015 Spring1EYUP BAŞKALE
Details 2013-2014 Spring1EYUP BAŞKALE
Details 2012-2013 Spring1EYUP BAŞKALE


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Course Details
Course Code Course Title L+P Hour Course Code Language Of Instruction Course Semester
BIO 654 ECOLOGICAL MODELLING 3 + 0 1 Turkish 2021-2022 Spring
Course Coordinator  E-Mail  Phone Number  Course Location Attendance
Prof. Dr. EYUP BAŞKALE ebaskale@pau.edu.tr FEN B0307 %70
Goals The preparing the data matrices on the data, similarity and dissimilarity coefficients, clustering techniques, discriminant analyses, prenciple component analyses, eigen vectors, PC1 _ PC2 plotting interperatations and MDS are teaching.
Content
Topics
WeeksTopics
1 Data dizayn in clustering analyses
2 Similarity and Dissimilarity coefficients
3 Techniques of hierarchical gruping tecniques
4 Discriminant analyses
5 Discriminant analyses practise
6 Principle component analyses
7 Eigen vectors
8 PC1 and PC2 plots and interpretations
9 PC3 ……. PCn plots and interpretations
10 Principle component analyses and taxonomical data
11 Principle component analyses and ecological data
12 Multifactor analyses on ecological research data
13 Usage of Multifactor analyses on multi-discipliner researchs data
14 Term project presentations and interpretations
Materials
Materials are not specified.
Resources
ResourcesResources Language
SPSS (2007), SPSS base 16.0 User Guide, SPSS Inc., Chicago.English
Course Assessment
Assesment MethodsPercentage (%)Assesment Methods Title
Final Exam50Final Exam
Midterm Exam50Midterm Exam
L+P: Lecture and Practice
PQ: Program Learning Outcomes
LO: Course Learning Outcomes