Table of contents

The course is connected to the following study programs

Teaching language

English.

Prerequisites

ME-201 Quantitative Data Analysis or other econometric courses that covers basic regression models.

Recommended previous knowledge

Knowledge of Stata (or other analytical tools) is an advantage.

Learning outcomes

Upon completion of the course, students will

  • have knowledge of core concepts in artificial intelligence and how different AI approaches are applied in business contexts
  • have knowledge of principles of data quality and model evaluation and their impact on business outcomes and decision-making
  • be able to apply programming tools to analyze business data and develop predictive models using appropriate methods
  • be able to assess and communicate AI results, including model limitations, data quality issues, and ethical considerations, to support business decisions
  • be able to design effective interactions with AI systems for business tasks while understanding their capabilities and limitations
  • be able to demonstrate responsible and ethically grounded use of AI tools, including the ability to critically assess the accuracy, reliability, and potential biases in AIgenerated outputs.
  • be capable of evaluating the implications of AI-supported decisions, reflecting on risks and limitations, and applying appropriate strategies to ensure transparency, accountability, and sound professional judgement in contexts where AI is employed.

Contents

The course provides students with a practical introduction to artificial intelligence (AI) and its applications in business contexts. As AI tools become increasingly central to professional work, the ability to understand, apply, and critically evaluate them is a valuable skill for aspiring business professionals. Students are introduced to core AI concepts and terminology, including the distinction between analytical/predictive AI and generative AI. The course covers a typical workflow for AI-driven analysis, from data handling and model building to interpreting and communicating results. Ethical considerations, limitations of AI systems, and relevant regulatory frameworks are addressed in the course. A central element of the course is hands-on work with Python, where students develop basic programming skills applied to data analysis and prediction tasks. Students also gain practical experience with generative AI tools, including how to interact with them effectively for professional purposes. Through an ongoing project, students apply these skills to a realistic business problem, culminating in a group submission and oral presentation.

Teaching and learning methods

The course consists of lectures, coding labs and group work. Students will throughout the course work on a project which will be presented at the end of the course. The expected work load is approximately 200 hours.

Examination requirements

Participating in an oral group presentation is required to be eligible for the exam.

Examinations

Portfolio assessment. The portfolio consists of group project deliverables and an individual reflection note. Assessment expression: pass/ fail. It will not be possible to take the exam as a new or postponed exam in the semester in which the course is not offered.

Student evaluation

The person responsible for the course decides, in cooperation with the student representative, the form of student evaluation and whether the course is to have a midway or end of course evaluation in accordance with the quality system for education, chapter 4.1.
Last updated from FS (Felles studentsystem) Oct. 15, 2026 2:33:10 PM