Table of contents

The course is connected to the following study programs

Teaching language

English

Recommended previous knowledge

Bachelor level course in Applied Data Science

Learning outcomes

Upon completion of the course, students should:

  • have knowledge about statistical concepts such as probability and modeling and how to apply them in practice
  • be able to promote data-analytic thinking and explain how to extract knowledge from different types of big data
  • be able to identify the potential for creating business and societal value out of big data
  • be able to discuss why and how the change in the digital era and data availability can transform business and society
  • have thorough knowledge of how big data and analytics can foster successful digital transformations
  • be able to use advanced business intelligence platforms, including data visualisation, communication, forecasting, and prediction
  • be able to run machine learning algorithms in a no-coding environment and interpret the results
  • understand the role of data, information, and data analytics in managerial decision making
  • be able to evaluate and assess business problems, propose, and develop data-driven business models, strategies, and solutions
  • be able to use advanced business intelligence and machine learning to organisational decision making
  • have achieved a common understanding that will lead to more efficient communication between management, technical/development, and data science teams
  • be able to understand the basic principles of data governance, and apply it to the lifecycle of data and information availability and presentation
  • be able to understand and apply basic principles of privacy regulations, and ethical data management, ensuring responsible use of data, compliance with legal frameworks and stakeholders' interests
  • be able to communicate analytical results through effective storytelling, tailoring dashboards and presentations to both technical and managerial audiences, facilitating the collaboration between management, technical/development, and data science teams

Contents

The course builds on concepts and techniques from multiple fields, including business, management, economics, sociology and computer science. The students will be able to take a broad perspective on real-life problems, view a challenge from different perspectives, and see how different pieces fit together, leading them to propose, design, or develop data-driven solutions. Throughout the course, you will be using powerful Business Intelligence (BI) platforms (eg, PowerBI platform environment) and ML tools and platforms.

You will learn statistical concepts and data analysis techniques along with skills on BI platforms. You will understand the technical and managerial aspects of data technology and data analytics, and be better able to provide insights from data supporting managerial decision-making. Students may be asked, without being mandatory, to link their in-class assignments with actual cases from their own experience (eg, an existing dataset or a business problem). Focus will be given on exploring a domain space and finding a relevant problem to solve while fostering the ideation of creative solutions and suggesting managerial actions using advanced data science and analytics techniques on existing datasets. The course gives students a systematic basis for addressing change in the digital business and bridging digital transformation with digital sustainability for shared value that impacts society. We will be discussing big data analytics ecosystems and strategies for digital transformation as paths to business and societal change.

Teaching and learning methods

The teaching consists of a combination of lectures, group work and project assignments. There is a compulsory attendance in parts of the teaching, this will be specified at the start of the semester. Expected working hours are 270 hours.

Examination requirements

Mandatory assignments must be passed. Participation in class discussions is required in order to complete and obtain certain mandatory assignments. Information about these terms is described in Canvas. 

Examinations

Group project report. Letter grades, A-F

Student evaluation

The person responsible for the course, in consultation with the student representative, decides the method of evaluation and whether the courses will have a midterm or end of term evaluation, see also the Quality System, section 4.1. Information about the evaluation method for the course will be posted on Canvas.
Last updated from FS (Felles studentsystem) Oct. 15, 2026 2:29:02 PM