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1. Course title

Ilmakehätieteiden tilastolliset menetelmät

Statistical tools for climate and atmospheric science

Statistical tools for climate and atmospheric science

2. Course code


Aikaisemmat leikkaavat opintojaksot ATM308/530189 Kenttämittausten tilastollinen analyysi, 5 op.

3. Course status: compulsory/optional

-Which degree programme is responsible for the course?
Master's Programme in Atmospheric Sciences

-Which module does the course belong to?
ATM3001-6 Advanced Studies in Atmospheric Sciences

optional for

  • Study Track in Aerosol Physics
  • Study track in Biogeochemical Cycles
  • Study Track in Geophysics of the Hydrosphere
  • Study Track in Atmospheric Chemistry and Analysis
  • Study Track in Remote Sensing
  • Study Track in Meteorology

-Is the course available to students from other degree programmes?

4. Course level (first-, second-, third-cycle/EQF levels 6, 7 and 8)

Master’s level, degree programmes in medicine, dentistry and veterinary medicine = secondcycle
degree/EQF level 7
Doctoral level = third-cycle (doctoral) degree/EQF level 8

-Does the course belong to basic, intermediate or advanced studies (cf. Government Decree
on University Degrees)?
Advanced studies

5. Recommended time/stage of studies for completion

-The recommended time for completion may be, e.g., after certain relevant courses have
been completed.

The course is recommended to be taken on the first year of master's studies or beginning of doctoral studies.

6. Term/teaching period when the course will be offered

The course will be lectured every second year in the I-II period.

7. Scope of the course in credits

5 cr

8. Teacher coordinating the course

Katja Lauri (Santtu Mikkonen, Itä-Suomen yliopisto)

9. Course learning outcomes

-Description of the learning outcomes provided to students by the course
- See the competence map (

Upon completing the course, the student:

- Understands basic terminology of statistical analysis

  • variables
  • scales
  • distributions
  • measures of center and variation

- Is able to apply common methods of Descriptive and Inferential Statistics

- Remembers how to conduct more advanced statistical analyses to your data

  • Regression, variance and covariance analysis
  • linear and nonlinear models
  • time series analysis
  • multivariate methods

- Understands how to find the best analysis method for your data and prove the validity of the method

- Is able to report the results in a scientific article

10. Course completion methods

-Will the course be offered in the form of contact teaching, or can it be taken as a distance
learning course?
-Description of attendance requirements (e.g., X% attendance during the entire course or
during parts of it)
-Methods of completion

E-learning course, including

  • video lectures
  • online assignments  
  • Working in peer groups
  • interaction with teachers in Digicampus Moodle
  • Course work + seminar presenting the results
  • grading includes peer evaluation and self assessment

11. Prerequisites

-Description of the courses or modules that must be completed before taking this course or
what other prior learning is required

The student should have basic skills in some programming language/software capable in statistical analysis i.e. R, Matlab, Pyhton or SAS.

Knowing Concept of Probability is recommended, i.e. basic course of Probability theory, Basic course in statistics or similar basic level course.

12. Recommended optional studies

-What other courses are recommended to be taken in addition to this course?
-Which other courses support the further development of the competence provided by this

The competences can be further developed by taking the course

13. Course content

-Description of the course content

Recap of basic concepts of statistical analysis: mean, median, concepts of variance and deviation, distributions, longitudinal and panel data.

The most common explorative single- and multivariate data analysis methods, regression, variance- and covariance analysis and the basics of time series analysis and factor analysis.

Defining validity of a statistical model and scientific reporting of results.

  • 14. Recommended and required literature

-What kind of literature and other materials are read during the course (reading list)?
-Which works are set reading and which are recommended as supplementary reading?

The material will be provided during the course.

15. Activities and teaching methods in support of learning

-See the competence map (
-Student activities
-Description of how the teacher’s activities are documented

  • video lectures
  • online assignments  
  • Working in peer groups
  • interaction with teachers

16. Assessment practices and criteria, grading scale

-See the competence map (
-The assessment practices used are directly linked to the learning outcomes and teaching
methods of the course.

Final grade is based on exercises (1/4) Peer evaluation (1/4) and course work (1/2). 45% of total points needed to pass.

17. Teaching language


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