Environmental data analysis methods
| Title | Μέθοδοι Ανάλυσης Περιβαλλοντικών Δεδομένων / Environmental data analysis methods |
| Code | ΦΠΥ104 |
| Faculty | Sciences |
| School | Physics |
| Cycle / Level | 2nd / Postgraduate |
| Teaching Period | Winter/Spring |
| Coordinator | Maria-Elissavet Koukouli |
| Common | No |
| Status | Active |
| Course ID | 600022909 |
Programme of Study: PMS FYSIKĪ ATMOSFAIRIKOU PERIVALLONTOS KAI PAGKOSMIŌN METAVOLŌN
Registered students: 10
| Orientation | Attendance Type | Semester | Year | ECTS |
|---|---|---|---|---|
| KORMOS | Compulsory Course | 1 | 1 | 6 |
| Academic Year | 2025 – 2026 |
| Class Period | Winter |
| Faculty Instructors |
|
| Instructors from Other Categories |
|
| Weekly Hours | 3 |
| Total Hours | 39 |
| Class ID | 600286449
|
Course Type 2021
Specific Foundation
Course Type 2011-2015
Specific Foundation / Core
Mode of Delivery
- Face to face
Digital Course Content
- e-Study Guide https://qa.auth.gr/en/class/1/600286449
Language of Instruction
- Greek (Instruction, Examination)
Prerequisites
General Prerequisites
Very basic programming skills in one of the more typical programming languages would be beneficial, without being necessary.
Learning Outcomes
Upon succesful completion of the course, students
will have specialized in the use of environmental data analysis techniques and practiced on the analysis and presentation of environmental measurements.
will have learned statitisical processesing of environmental data, different types of environmental information files, metadata processing methods, satellite remote sensing data processing libraries, etc.
Upon successful completion of the course, students should be able to develop data visualization techniques and remote sensing data processing applications using either the Interactive Data Language, IDL, or Python. They should also be able to develop visualization and processing techniques for atmospheric model simulations, high volume data management techniques as well as data mining techniques with the programming language of their choice.
General Competences
- Apply knowledge in practice
- Retrieve, analyse and synthesise data and information, with the use of necessary technologies
- Adapt to new situations
- Work autonomously
- Advance free, creative and causative thinking
Course Content (Syllabus)
Introduction to statistics. Random variables. Normal distribution. Significance tests. Data sampling. Applied statistics for time series analysis. Techniques for mapping environmental variables
Specific aspects to be studied are: types of environmental information files, metadata, remote sensing data processing libraries, data visualization and remote sensing data development techniques using Python. Visual modelling and simulation techniques for atmospheric modelling purposes will also be taught, as well as large volume data management techniques and data mining techniques.
Educational Material Types
- Notes
- Slide presentations
- Interactive excersises
Use of Information and Communication Technologies
Use of ICT
- Use of ICT in Course Teaching
- Use of ICT in Laboratory Teaching
- Use of ICT in Communication with Students
- Use of ICT in Student Assessment
Course Organization
| Activities | Workload | ECTS | Individual | Teamwork | Erasmus |
|---|---|---|---|---|---|
| Lectures | 117 | 4.7 | |||
| Written assigments | 30.5 | 1.2 | |||
| Exams | 2.5 | 0.1 | |||
| Total | 150 | 6 |
Student Assessment
Description
Students are assessed throughout the semester through personal contact during the lectures and through their general interest and participation in class. At the same time, weekly assignments are given which will be discussed both online and in private with each student as well as analyzed anonymously in the classroom as part of the learning process. From personal contact and weekly assignments, the teacher receives a broader picture of the student.
Student Assessment methods
- Written Assignment (Formative)
- Written Exam with Problem Solving (Summative)
Bibliography
Additional bibliography for study
Statistics for Environmental Science and Management (Environmental Statistics)
Developing Statistical Software in Fortran 95
Camebridge University Press - Numerical Recipies in Fortran, Vol.1, 2nd Edition
Introduction to programming with fortran
Last Update
08-12-2023
