COURSE UNIT TITLE

: NUMERICAL TAXONOMY

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
BYL 5075 NUMERICAL TAXONOMY ELECTIVE 3 0 0 6

Offered By

Graduate School of Natural and Applied Sciences

Level of Course Unit

Second Cycle Programmes (Master's Degree)

Course Coordinator

PROFESSOR DOCTOR FERHAT MATUR

Offered to

Biology

Course Objective

To semtinize Evolutionary theory at molecular aspect. To choose suitable marker by learning molecular techniques. To applicate for working organism by learning Phylogeny concepts.

Learning Outcomes of the Course Unit

1   Can explain principles of numeric taxonomy
2   Can summarize historical evolution process of numeric taxonomy
3   Can identifies features using in numeric taxonomy and explain their importance with examples.
4   Can identifies features using in numeric taxonomy and solve the examples.
5   Knows Taxonomical Characters and using areas in taxonomy
6   Knows data matrices and construction of its
7   Knows clustering analysis
8   Knows the determination of Relatedness and Distance measurements
9   Knows the construction of clustering algorithms
10   Knows Ordination and Discrimination Analysis

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description
1 Purpose and principles of numeric taxonomy.
2 Some terms used in numeric taxonomy
3 Advantages and disadvantages over other methods of numerical taxonomy
4 Principles of phenetic and phylogenetic taxonomy
5 Operational taxonomic unit (OTU) and the taxonomic level.
6 Data matrix types and their creation
7 Taksonomic features
8 Midterm exam
9 Data types and standardization
10 Taxonomic similarity and the similarity coefficients
11 Problems encountered in the determination of the relations Phenetic
12 Clustering analysis: UPGMA, WPGMC, UPGMC
13 Clustering analysis: SINGLE LINKAGE, COMPLE LINKAGE
14 Final exam

Recomended or Required Reading

Peter H. A. Sneath, Numerical Taxonomy: The Principles and Practice of Numerical Classification, W H Freeman & Co (Sd), 1973

Planned Learning Activities and Teaching Methods

The course is taught in a lecture, class presentation and discussion format. All class members are expected to attend and both the lecture and take part in the discussion sessions. Besides the taught lecture, group presentations are to be prepared by the groups assigned for that week and presented to open a discussion session.

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA
1 PRS PRESENTATION
2 ASG ASSIGNMENT
3 RAS RESEARCH ASSIGNMENT
4 FCG FINAL COURSE GRADE PRS * 0.30 +ASG * 0.20 + RAS * 0.50


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

Further Notes About Assessment Methods

None

Assessment Criteria

Student will be evaluated with midterm exams, homework presentation and final exam.

Language of Instruction

Turkish

Course Policies and Rules

Attendance to at least 70% for the lectures is an essential requirement of this course and is the responsibility of the student. It is necessary that attendance to the lecture and homework delivery must be on time. Any unethical behavior that occurs either in presentations or in exams will be dealt with as outlined in school policy

Contact Details for the Lecturer(s)

ferhat.matur@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 2 28
Preparation for midterm exam 1 14 14
Preparation for final exam 1 14 14
Preparation for quiz etc. 4 3 12
Preparing assignments 14 2 28
Preparing presentations 6 2 12
Final 1 2 2
Midterm 1 2 2
Quiz etc. 4 1 4
TOTAL WORKLOAD (hours) 158

Contribution of Learning Outcomes to Programme Outcomes

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LO.255555555
LO.35555555
LO.455555555
LO.5555555555
LO.655555555
LO.755555555
LO.8555555555
LO.9555555
LO.1055555555