Translation of the Swedish course syllabus 
 
Statistical analyses and visualisation in R: I, 15 credits
(Statistisk analys och visualisering i R: I, 15 högskolepoäng)
 
Course code: 1040MA
Subject area: Mathematic Statistics
Main field of study: No main subject
Academic school: School of Natural Sciences, Technology and Environmental Studies
Disciplinary domain: Natural Sciences 100%
Grading scale: AF
Education cycle: First-cycle (Bachelor)
Course level: A (introductory)
Progressive specialisation: G1N (First cycle. Entry requirements: completed upper secondary education)
Language of instruction: English
Valid from: HT 2019 (autumn semester)


1. Validation

This course syllabus was validated by the Faculty Board for Natural Sciences, Technology and Environmental Studies at Södertörn University on 2019-01-30 according to the stipulations in the Higher Education Ordinance.

2. Entry requirements

General entry requirements and English B (intermediate level) (Specific entry requirements 2, exemption provided for modern languages).
Or
General entry requirements (Specific entry requirements A2, exemption provided for modern languages).

3. Learning outcomes

Upon completion of the course, the student is able to:
  • describe basic statistical concepts
  • define and identify different types of data
  • describe different data distributions and distribution functions and their importance in statistical analysis
  • use R to perform statistical analyses using parametric and non-parametric frequentistic methods and basic Bayesian methods
  • use R for the visualisation and presentation of results from statistical analyses
  • complete an individual study including statistical analysis and visualisation in R

  • 4. Course content, modules and examinations

    This course introduces basic statistical methodology, with the focus on applying statistical models to analyse the results of studies and scientific experiments. Students learn how to use a statistical platform, R, to manage data, statistical analysis and the visualisation of results from statistical models. Students will work with R, RStudio and RMarkdown. The course introduces correlation, linear and non-linear regression, t-test, and ANOVA, as well as non-parametric alternative models for data with other distribution forms. Students are taught to develop general linear models using different information criteria. The course also introduces basic Bayesian analysis of statistical models. Students are taught to produce graphical illustrations of results from statistical models using different plotting functions in R, as well as how to use RMarkdown to create reports and presentations directly from R.

    • Statistical analyses and visualisation in R: I, 15 credits
      (Statistisk analys och visualisering i R: I, 15 högskolepoäng)


    • 1001, Statistical analyses and visualization in R: I, Practicals, 10 credits
      (Statistisk analys och visualisering i R: I, övningar, 10 högskolepoäng)
      Grades permitted: A/B/C/D/E/F

      1002, Statistical analyses and visualization in R: I, Project, 5 credits
      (Statistisk analys och visualisering i R: I, projektarbete, 5 högskolepoäng)
      Grades permitted: A/B/C/D/E/F

    5. Course design

    If the course is a distance course: All teaching is in English in the form of written instructions and demonstrations, as well as video lectures.

    If the course is taught on campus: All teaching is in English in the form of written instructions and demonstrations, as well as seminars, lectures and video lectures.

    6. Examination format

    1001, Statistical analyses and visualisation in R: I, Practicals, 10 credits
    (Statistisk analys och visualisering i R: I, övningar, 10 högskolepoäng)
    Completed exercises with individual written reports

    1002, Statistical analyses and visualisation in R: I, Project, 5 credits
    (Statistisk analys och visualisering i R: I, projektarbete, 5 högskolepoäng)
    Individual written report

    All examinations can be written in English or Swedish.
     The grading criteria are distributed prior to the start of a course or module.

    7. Reading list

    If there are changes in the reading list between semesters, a new list for the next semester is to be validated no later than July 15 and December 15 respectively.
     
    Valid from Valid to Decision date Decision board
    HT 20192019-05-24Programrådet för Miljö och utveckling, ordförandebeslut

    8. Restrictions on accreditation

    The course may not be accredited as part of a degree if the contents are partly or wholly the same as a course previously taken in Sweden or elsewhere.