• Description:

    Most quantitative research projects involve both case-specific programming tasks as well as data analysis of a more standard nature. When working with quantitative data it is moreover essential to be able to do so in a systematic and reproducible manner with trustworthy software. This course aims at introducing the free program R as a computational environment for integrating these tasks. R has for two decades been a leading tool for computing with data and it is the preferred language for implementation of new statistical methodology.

    The course will cover the practice of scientific computing, programming, and quantitative analyses as well as the essential theoretical underpinnings.

    The topics of the course will include.

    • Introducing R as a statistical programming environment for data analysis.
    • Efficient data management using R.
    • High-level graphics in R.
    • Statistical models in R
    • Aspects of scientific computing in theory and practice.
    • Reproducible research in practice
    • Programming in R and vectorized computations.
    • Optional (if time permits) Matrix factorizations and other numerical methods in R. Easy integration of C++ code in R.

    Participants must have a working knowledge of elementary statistical methods such as regression models and analysis of variance.

    Learning objectives:
    After completing the course the participants will
    1. be able to solve programming tasks with R.
    2. know how to manage data, visualize data and fit models to data using R.
    3. have learned enough about R to continue learning on their own.

    Teaching methods:
    A combination of instructive videos, computer practicals and lectures.

    Criteria for assessment:
    Active participation in the practicals + approval of major exercise (to be handed in after the course).

    Frequently asked questions:

  • Q: If I participate in the course, can you then help me analyze a dataset that I work with as part of my ph.d. project.
  • A: No, I am afraid that this is not possible 
  • Q: I would like to participate in the course, but during a part of the course period I can not be present. Is it possible to follow to course via Skype or similar?
  • A: No, I am afraid that this is not possible
  • Q: I am not a ph.d. student, but I would like to participate in the course anyway. Is that possible?
  • A: You will have to ask the doctoral school: doctoral.school@adm.aau.dk
  • Q: I realize that I am late for enrollment, but I would really like to participate. Is it possible.
  • A: You will have to ask the doctoral school: doctoral.school@adm.aau.dk

  • Organizer:  Søren Højsgaard,Department of Mathematical Sciences.

  • Lecturers: Søren Højsgaard who is associate professor at the Department of Mathematical Sciences. He has 20 years of practical experience with R. He has authored several R packages and he has written a book about qraphical models with R

  • ECTS: 4

  • Time: 30 November + 1,2,7,8 and 9 December, 2016. 9:00-15:30

  • Place: Frederik Bajers Vej 7B/2-107

  • Zip code: 9220

  • City: Aalborg

  • Number of seats: 40

  • Deadline: 9 November, 2016

  • Important information concerning PhD courses 
    We have over some time experienced problems with no-show for both project and general courses. It has now reached a point where we are forced to take action. Therefore, the Doctoral School has decided to introduce a no-show fee of DKK 5,000 for each course where the student does not show up. Cancellations are accepted no later than 2 weeks before start of the course. Registered illness is of course an acceptable reason for not showing up on those days. Furthermore, all courses open for registration approximately three months before start. This can hopefully also provide new students a chance to register for courses during the year. We look forward to your registrations.