Lund University

Overview

The Master of Science in Data Analytics and Business Economics at Lund University is designed to equip students with the skills necessary to transform data into actionable business strategies. This programme emphasizes the integration of statistics, machine learning, economics, and the legal and ethical considerations surrounding data usage. Students will gain practical experience through coding, working with real datasets, and applying analytics within a business-economic framework.

What you'll study

The curriculum encompasses a blend of core courses and specialized modules that provide a comprehensive understanding of data analytics and its applications in business contexts. Students will progress from foundational concepts to advanced analytical techniques.

• Programming in R
• Data Visualisation
• Machine Learning from a Regression Perspective
• Working with Databases
• Legal Aspects of Data Analytics
• Advanced Machine Learning

Capstone / thesis: The programme includes a significant independent project, typically culminating in a master’s thesis, where students apply analytical methods to a defined research question and present their findings in an academic format.

Entry requirements

To be eligible for the programme, applicants should have completed an undergraduate degree (BA/BSc) lasting a minimum of three years and comprising at least 180 credits. It is essential that the degree includes coursework in quantitative methods, with a focus on subjects such as statistics and linear algebra. Relevant academic backgrounds may include mathematics, statistics, economics, informatics, or related disciplines.

Career prospects

Graduates of the MSc in Data Analytics and Business Economics will be well-prepared for a variety of roles in the data analytics and business sectors. The skills acquired throughout the programme will enable them to pursue careers as data analysts, business intelligence specialists, and economic consultants, among other positions that require expertise in data-driven decision-making.