Data Analytics for Retail Management

Course Director: Asser Koskinen

This course covers probability theory, descriptive and inferential statistics, and basic machine learning techniques, applied to real-world scenarios to inform strategic business decisions.

Key concepts include probability, independence, Bayes' theorem, random variables, probability distributions, measures of central tendency and dispersion, the central limit theorem, random sampling, estimation, inference, hypothesis testing, regression, mediation, moderation, bootstrapping, supervised and unsupervised learning, classification, clustering, and prediction.

Format and Structure
The course consists of lectures, seminars and assignments, with and without the use of statistical software, and a written exam.

Course administrator: Karl.Strelis@hhs.se, alexandra.svahn.alberts@hhs.se

After successfully completing this course, students should be able to:

1. Describe the main features of a dataset.
2. Estimate population parameters to make statistical inferences.
3. Use basic machine learning techniques to classify data and make predictions.

General entry requirements.

• Newbold, P., Carlson, W., & Thorne, B. (2022). Statistics for Business and Economics, Tenth Global Edition. Pearson. Chapters 1 – 14. ISBN 9781292436845 (textbook) or ISBN 9781292436937 (textbook and online resources). 25% Discount Code ((login required)

All intended learning outcomes will be assessed through hand-in assignments (10%) and a written exam (90%).

Find the course schedule in TimeEdit

Have you been granted educational support because of a documented disability? If so, please contact the course director as soon as possible so that adequate accommodations can be made.

Contact specialneeds@hhs.se if you have questions about being granted educational support or need a copy of your educational support certificate.

For more information, visit the Specific Educational Needs page on the Student Hub (login required).