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Table of Contents

1. Course title

Kenttämittausten tilastollinen analyysi
Statistical Analysis of Environmental Field Measurements
Statistical Analysis of Environmental Field Measurements

2. Course code


Aikaisemmat leikkaavat opintojaksot 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?
ATM300 Advanced Studies in Atmospheric Sciences

compulsory for

  • Study Track in Aerosol Physics

optional for

  • 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.

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

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

7. Scope of the course in credits

5 cr

8. Teacher coordinating the course

Anton Rusanen, Otso Peräkylä, Martha A Zaidan

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 the concept and applicability of statistical tests and p-values and is able to apply them to a given dataset;
  • is able to perform fundamental statistical analysis of a given dataset;
  • is able to produce clear programming code.

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

Weekly exercises and final exam, which can be arranged as a small project work or home exam.

11. Prerequisites

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

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

  • programming basics: functions, code organization & style, simple optimization, debugging, different ways to do IO
  • basics of statistics: distributions, cont. & disc., parameters, histograms
  • tests and their related concepts, hypothesis tests & p-value
  • fitting: linear fits, polynomial & other non-linear fits, interpolation & extrapolation
  • PCA, factor analysis & clustering
  • time series: data models

    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

Weekly exercises and lectures.

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 (1/2) and final exam (1/2), and 45% of total points needed to pass.

17. Teaching language