Table of contents

The course is connected to the following study programs

Teaching language

Norwegian or English. Information about language will be available in Canvas at the start of the semester.

Recommended previous knowledge

IS-118 Programmering og prosjektarbeid (or other courses in foundational computer programming).

Learning outcomes

Upon completion of the course, the students will be able to:

  • Explain key concepts in data science, machine learning, and AI
  • Describe typical steps in a data science workflow
  • Use a programming environment (e.g., Python) to prepare, transform, analyse, and visualise data
  • Apply, train, and evaluate machine-learning models for simple tasks
  • Use pre-trained AI models (e.g., computer vision, LLMs) for applications to non-structured data sources (e.g., images, videos, audio)
  • Present analytical results in a clear and understandable way
  • Reflect critically on the possibilities and limitations of AI and data-driven methods

Contents

This course provides a practical, application-oriented introduction to artificial intelligence (AI) and data science. Students learn core concepts, workflows, and tools to understand and analyse data using AI. The course emphasises hands-on work in a programming environment using open datasets.

Main topics include:

  • Concepts, terminology, and real-world applications of AI and data science
  • Introduction to machine learning
  • Modern AI fundamentals, including neural networks
  • Data handling in Python (or equivalent tools): loading, cleaning, transforming, simple feature engineering
  • Exploratory data analysis: visualisation, pattern detection, and storytelling with data.
  • Data analytics with machine learning and generative AI

Basics of data quality, data governance, and ethics

Teaching and learning methods

  • Lectures and seminar sessions
  • Workshop and lab exercises
  • Group work and assignments

Estimated workload: 270 hours

Examination requirements

Mandatory assignments must be approved. Further information is provided in Canvas at the start of the semester.

Examinations

4-hour individual written exam. Graded assessment A-F.

Student evaluation

The course coordinator, in consultation with the student representative, determines the evaluation method and whether the course will have midterm or final evaluation in accordance with the quality system, Chapter 4.1. Information about the course evaluation method will be published in Canvas.
Last updated from FS (Felles studentsystem) Oct. 15, 2026 9:00:31 AM