Τμήμα Ωκεανογραφίας και Θαλασσίων Βιοεπιστημών

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Environmental Data Analysis
School:
Of the Environment
Academic Unit:
Department of Marine Sciences
Level of studies:
Postgraduate
Course Code:
Semester:
Α
Course Title:
Environmental Data Analysis
Independent Teaching Activities
Weekly Teaching Hours
4
Credits
6
Course Type:
General knowledge
Prerequisite Courses:
There are no prerequisite courses. Basic knowledge on computer use and mathematics is essential.
Language of Instruction and Examinations:
Greek
Is the course offered to Erasmus students:
No
  • (2) Learning Outcomes
  • Learning Outcomes

The aimed learning outcomes regarding knowledge, skills and abilities, are the following:

  • Knowledge of descriptive statistics methods.
  • Knowledge of the underlying theory of univariate tests.
  • Knowledge of the basic univariate tests.
  • Knowledge of the basic multivariate tests.
  • Ability to apply univariate and multivariate methods for environmental data analysis with R and RStudio software.
  • General Competences
  • Search for, analysis and synthesis of data and information, with the use of the necessary technology
  • Adapting to new situations
  • Decision-making
  • Working independently
  • Production of new research ideas
  •  
  • (3) Syllabus

Week 1: Descriptive statistics methods

Week 2: Statistical distributions-Central limit theorem

Week 3: Hypotheis testing

Week 4: One-sample t-test

Week 5: Independent and Paired two-sample t-tests

Week 6: Non-parametric methods for two independent and paired samples

Week 7: One-way ANOVA and Kruskal-Wallis test

Week 8: Post-hoc tests

Week 9: Simple and Multiple Linear Regression

Week 10: Parametric and non-parametric correlation

Week 11: Introduction to multivariate statistics

Week 12: Cluster analysis

Week 13: Principal Component Analysis

Week 14: Applications in coastal management with R and RStudio

  • (4) Teaching and Learning Methods - Evaluation
  • Delivery:

Face-to-face – Distance learning methods

  • Use of Information and Communication Technology:

Oral presentations-Distance learning methods

The course is supported for registered students by the e-class platform at https://eclass.aegean.gr/courses/MAR188

Students practice with R and RStudio

  • Teaching Methods:
Activity Semester workload
Lectures
39
Practicals on R and RStudio
15
Independent study
93
Final exam
3
Course total
150
  • Student Performance Evaluation:

Evaluation of student essays on selected subjects.

 

Students are able to check their written documents and ask for further information about their evaluation.

  • (5) Attached Bibliography

 -Suggested bibliography:

  • Students’ notes:
  • Instructors’ presentations (https://eclass.aegean.gr/courses/MAR188/)

– Additional bibliography:

– Related academic journals: