Predictive Analytics with R

BAN404 Predictive Analytics with R

Autumn 2020

  • Topics

    This course is an introduction to statistical learning and its application to help making economic decisions. Statistical learning is the science of extracting important patterns from data, patterns that can inform a decision maker. Particularly, the course focuses on predictive analytics, the activity of predicting future events or predicting an unobserved property of an individual, a company or some other unit. The course will contain applications on business data. The students will work on those using the statistical software R. Examples of applications are statistical fraud detection and market basket analysis.


    • Linear regression and classification
    • Resampling methods
    • Model selection
    • Non-linear models
    • Tree-based methods
    • Unsupervised learning

  • Learning outcome

    After completing the course the students will know the central ideas of statistical learning and predictive analytics and have: 

    the practical skills to

    • implement the most common techniques to real-world prediction problems in R
    • evaluate predictions and deal with the problem of overfitting
    • appropriately choose between a set of statistical learning methods for a given problem

    and the knowledge to

    • formulate a research problem for a master thesis
    • structure a master thesis
    • understand the ethical issues in collecting and interpreting research data

  • Teaching

    Lectures and, in addition, tutorials where students work in groups on theory- and data exercises.

  • Recommended prerequisites

    Basic skills in statistical inference. Basic skills in R.

  • Credit reduction due to overlap

    Course identical to BUS459.

  • Requirements for course approval

    Passed on a written project report.

  • Assessment

    Due to the ongoing Corona pandemic, the assessment for the spring semester 2020 has been changed:

    Individual home exam, 6 hours.


    Original assessment form spring 2020 – cancelled:

    The final grade will be based on a 5 hour written school exam.

    This course is a continuation of BUS459 and the total number of attempts applies to the course (not the course code).

  • Grading Scale

    Grading scale spring 2020: Pass/Fail.

    (Originally planned A - F)

  • Computer tools


  • Literature

    James, Witten, Hastie and Tibshirani (2013) "An introduction to statistical learning with applications in R".


ECTS Credits
Teaching language

Spring. Offered Spring 2020.

Course responsible

Professor Jonas Andersson, Department of Business and Management Science