BAN404 Statistical Learning (E)

• 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 contain applications on business data. The students will work on those using the software Python. Examples of applications are prediction of occurrence and size of insurance claims and use of statistical learning to allocate a marketing budget.

  Topics

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

• Learning outcome

  After completing the course, the students will be able to:

  Knowledge

  • Discuss the central ideas of statistical learning and predictive analytics.
  • Understand scientific publications using statistical learning methods.
  • Formulate a statistical learning problem and report its results.

  Skills

  • Implement the most common techniques to real-world prediction problems in Python.
  • Evaluate predictions and deal with the problem of overfitting.
  • Appropriately choose between a set of statistical learning methods for a given problem,

  General competence

  • Explain and discuss concepts in statistical learning.

• Teaching

  The teaching consist of lectures and tutorials where students work on exercises.

• Recommended prerequisites

  • Basic skills in statistical inference.
  • Skills in Python corresponding to BAN405 Python Programming for Data Science.

• Credit reduction due to overlap

  The course is 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 6 hour digital school exam. The students will have access to the statistical software Python, and the exam must be answered in English.

  Students retaking the assessment in spring 2026 may use R during the exam.

  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

  Python

• Literature

  James, Witten, Hastie, Tibshirani and Taylor (2023) "An introduction to statistical learning with applications in Python".

• Permitted Support Material

  All written support material permitted (category III)

  Calculator

  One bilingual dictionary (Category I)

  All in accordance with Supplementary provisions to the Regulations for Full-time Study Programmes at the Norwegian School of Economics Ch.4 Permitted support material https://www.nhh.no/en/for-students/regulations/https://www.nhh.no/en/for-students/regulations/ and https://www.nhh.no/en/for-students/examinations/examination-support-materials/https://www.nhh.no/en/for-students/examinations/examination-support-materials/