Course Information


Course Information
Course Title Code Semester L+U Hour Credits ECTS
DATA VISUALIZATION IN GEOPHYSICS 801100715400 3 + 0 3.0 8.0

Prerequisites None

Language of Instruction Turkish
Course Level Graduate Degree
Course Type Compulsory
Mode of delivery Visual material, lecturing, question-answering, discussion, interacting, computer application, homework
Course Coordinator
Instructors Bülent KAYPAK
Assistants
Goals Visualization of digital data obtained in Geophysics by using various methods and techniques.
Course Content Data concept, type, format and classification. Data description and types in Geophysics. Data visualization on computing systems. Digitizing of analogue data and resampling. Coordinate systems in data visualization. Mapping and projection types. 1-D, 2-D, and 3-D data and their visualization techniques. Gridding methods for multi-dimensional data. Visualization of time depended data variations. Visualization techniques of special data in Geophysics. Graphical analysis methods.
Learning Outcomes 1) Defines the data type, their charactesr and properties obtained in geophysics.
2) Converts analog data to digital data
3) Designs the digital data in what size and how to visualize
4) Draws digital maps
5) Applies modern techniques and methods used in data visualization

Weekly Topics (Content)
Week Topics Teaching and Learning Methods and Techniques Study Materials
1. Week Definition of Data and Terminology Lecture; Question Answer; Discussion; Case Study
Brainstorming; Opinion Pool
Problem Based Learning; Brain Based Learning 		Presentation (Including Preparation Time)
2. Week Geophysical Data Definition , Types and Characteristics Lecture; Question Answer; Discussion; Case Study
Brainstorming; Opinion Pool
Problem Based Learning; Brain Based Learning 		Presentation (Including Preparation Time)
3. Week Various Softwares Used in Data Visualization Lecture; Question Answer; Discussion; Case Study
Brainstorming; Opinion Pool
Problem Based Learning; Brain Based Learning 		Presentation (Including Preparation Time)
4. Week Digitizing and Re-sampling of Analog Data Lecture; Question Answer; Problem Solving; Discussion; Case Study
Brainstorming; Opinion Pool
Problem Based Learning; Brain Based Learning 		Presentation (Including Preparation Time)
5. Week Coordinate Systems Lecture; Question Answer; Problem Solving; Discussion; Case Study
Brainstorming; Opinion Pool
Problem Based Learning; Brain Based Learning 		Presentation (Including Preparation Time)
6. Week Drawing Map and Projection Types Lecture; Question Answer; Problem Solving; Discussion; Case Study
Brainstorming; Opinion Pool
Problem Based Learning; Brain Based Learning 		Presentation (Including Preparation Time)
7. Week Graphical Features in Data Visualization Lecture; Question Answer; Problem Solving; Discussion; Case Study
Brainstorming; Opinion Pool
Problem Based Learning; Brain Based Learning 		Presentation (Including Preparation Time)
8. Week 1-Dimensional Data and Visualization Lecture; Question Answer; Problem Solving; Discussion; Case Study
Brainstorming; Opinion Pool
Problem Based Learning; Brain Based Learning 		Presentation (Including Preparation Time)
9. Week 2-Dimensional Data and Visualization Lecture; Question Answer; Problem Solving; Discussion; Case Study
Brainstorming; Opinion Pool
Problem Based Learning; Brain Based Learning 		Presentation (Including Preparation Time)
10. Week 3-Dimensional Data and Visualization Lecture; Question Answer; Problem Solving; Discussion; Case Study
Brainstorming; Opinion Pool
Problem Based Learning; Brain Based Learning 		Presentation (Including Preparation Time)
11. Week Time Variant Data and Visualization Lecture; Question Answer; Problem Solving; Discussion; Case Study
Brainstorming; Opinion Pool
Problem Based Learning; Brain Based Learning 		Presentation (Including Preparation Time)
12. Week Specific Data in Geophysics and Visualization Lecture; Question Answer; Problem Solving; Discussion; Case Study
Brainstorming; Opinion Pool
Problem Based Learning; Brain Based Learning 		Presentation (Including Preparation Time)
13. Week Graphical Data Analysis Lecture; Question Answer; Problem Solving; Discussion; Case Study
Brainstorming; Opinion Pool
Problem Based Learning; Brain Based Learning 		Presentation (Including Preparation Time)
14. Week General Evaluation Question Answer; Discussion; Case Study
Brainstorming; Opinion Pool; Speech Loop; Colloquium
Brain Based Learning 		Presentation (Including Preparation Time)
15. Week Final Exam Question Answer

 Homework

Sources Used in This Course
Recommended Sources
Fry, B., 2007. Visualizing Data: Exploring and Explaining Data with the Processing Environment, 384 p., ISBN 0596514557.
Ware, C., 2008. Visual Thinking: For Design (Morgan Kaufmann Series in Interactive Technologies). 197 p. ISBN0123708966.
Ware, C., 2012. Information Visualization: Perception for Design, 3rd Edition, Morgan Kaufmann, 536 p. ISBN0123814642.
Wessel, P. and W. H. F. Smith, 1991. Free software helps map and display data, EOS Trans. AGU, 72, 441.
Wessel, P. and W. H. F. Smith, 1995. New version of the Generic Mapping Tools released, EOS Trans. AGU, 76, 329.
Wessel, P. and W. H. F. Smith, 1998. New, improvedversion of the GenericMapping Tools released, EOS Trans. AGU, 79, 579.
Wessel, P., W. H. F. Smith, R. Scharroo, J. F. Luis, and F. Wobbe, 2013. Generic Mapping Tools: Improved version released, EOS Trans. AGU, 94, 409-410.

Relations with Education Attainment Program Course Competencies
Program RequirementsContribution LevelDK1DK2DK3DK4DK5
PY1500000
PY2500000
PY3500000
PY4500000

*DK = Course's Contrubution.
0 1 2 3 4 5
Level of contribution None Very Low Low Fair High Very High
.

ECTS credits and course workload
Event Quantity Duration (Hour) Total Workload (Hour)
Course Duration (Total weeks*Hours per week) 14 3
Work Hour outside Classroom (Preparation, strengthening) 14 3
Homework 5 10
Practice (Teaching Practice, Music/Musical Instrument Practice , Statistics, Laboratory, Field Work, Clinic and Polyclinic Practice) 10 5
Midterm Exam 1 3
Time to prepare for Midterm Exam 5 5
Final Exam 1 3
Time to prepare for Final Exam 5 5
Total Workload
Total Workload / 30 (s)
ECTS Credit of the Course
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Course Information
  • Course Information
  • Weekly Topics (Content)
  • Sources Used in This Course
  • Relations with Education Attainment Program Course Competencies
  • ECTS credits and course workload