Environmental Data Analysis
(1) General
| 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 | Credits | |
| 4 | |||
| Total 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: | Grreek | ||
| Is the course offered to Erasmus students: | No | ||
| Course Website (Url): | https://www.mar.aegean.gr/index.php?lang=en&lesson=4&pg=3.2.1 | ||
(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: |
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| 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:
- Zar, J.H., 2010. Biostatistical Analysis, 5th Edition. ISBN-13: 978-0321656865, Northern Illinois University.
- Sharma, S., 1996. Applied Multivariate Techniques. ISBN: 978-0-471-31064-8, 512 pages, Wiley.
- http://manuals.bioinformatics.ucr.edu/home/programming-in-r
- http://cran.r-project.org/doc/manuals/R-intro.html
- Related academic journals:
- Journal of Statistical Software
- https://www.jstatsoft.org/index