BAN404 Predictive Analytics with R

• 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.

  Topics

  • 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 on exercises.

• Recommended prerequisites

  Basic skills in statistical inference. Basic skills in R.

• Credit reduction due to overlap

  Course identical to BUS459.

• Compulsory Activity

  Written project report (approved / not approved). The report must be written in English.

• Assessment

  The final grade is based on an individual 8 hour take home exam. The exam must be answered in English.

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

• Grading Scale

  A-F

• Computer tools

  R

• Literature

  James, Witten, Hastie and Tibshirani (2021) "An introduction to statistical learning with applications in R". Second Edition.