Master’s programme in Data Analytics and Business Economics

Academic requirements

The programme is developed for students who have recently finished an undergraduate degree (BA/BSc) of at least three years, 180 credits, in a subject matter including quantitative methods. This opens up for a broad spectrum of backgrounds from mathematics, statistics, economics, informatics and elsewhere. It is of special importance that the undergraduate studies include courses in quantitative methods (statistics and linear algebra are of particular importance).

It is recommended that students have at least 10 credits in economics and 10 credits in business administration. More specifically, it is required that the students have one of the following backgrounds:

• an undergraduate degree including at least 30 credits in statistics and mathematics with at least one course in statistics that includes regression analysis and one course in mathematics;
• an undergraduate degree including at least 60 credits in economics, informatics or business administration with at least one course in econometrics or regression analysis and one course in statistics or mathematics;
• an undergraduate degree including at least 60 credits in statistics with at least one course in regression analysis.

For students not having a course named regression analysis or econometrics, it is important to include a course syllabus (or  something similar) in the application, to demonstrate the fulfilment of this specific requirement.

Profiles of students that perform well within the programme

The content and the degree of difficulty of the MSc in Data Analytics and Business Economics have been worked out in collaboration with the Advisory Board, because it knows firsthand what employers want. Some of the must-have skills when leaving the programme are; database management, programming, data visualization, machine learning, and legal and business-economic know-how. These are skills that students develop during the programme.

However, since most must-have skills are technical in nature, it is important that students have a solid background in statistics and/or econometrics when entering the programme. In particular, it is important for students to know regression analysis, as many machine learning techniques build on it. Math is also important, since much of the machine learning exposition will be based on linear algebra.

The extent to which students have a solid background in statistics, econometrics and/or math is reflected in their performance within the programme. Here are a few illustrative examples of course combinations at Lund University that have led to good performance:

• Students with a background in business, economics or informatics who have taken Basic Course 1 in statistics (STAA31), Mathematical Methods for Economics (NEKG33) and Econometrics (NEKG31);
• Students with a background in statistics who have taken Regression Analysis (STAG23);
• Students with an engineering background who have taken Linear Algebra 1 (MATA22), Mathematical Statistics (MASB11) and/or Mathematical Statistics: Basic Course (MASA02) together with other math and statistics courses to meet the formal 30 credit requirement.

Students are not expected to have taken any programming courses prior to entering the programme. However, programming will be introduced at a fast pace. Some experience working with a script-based program such as R, Python, Matlab or Javascript is therefore useful.

Selection to the programme

We normally look for undergraduates with excellent results from an internationally recognised university. Selection is based on academic merits from university studies (100%). This implies that an assessment will be made of the grades from previous studies at the undergraduate level. In the assessment, special weight will be given to grades on courses that prepare students for the curriculum of this study programme. We also take into account the standing of the institution where you studied your qualification.