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Data Analytics and Business Economics - Master's Programme

Master of Science, major in data analytics and business economics | 1 year | 60 credits

Graphs with lots of digits and letters

Transforming data into insights that can enhance decision-making is a key challenge for companies of every size, across all industries. In the Master's programme in Data Analytics and Business Economic, you will learn not only how to work the numbers and draw conclusions, but also how to clearly communicate your results to data technicians and business managers alike.


Have you noticed how Netflix and YouTube send you suggestions based on your previous views, or how Spotify makes suggestions based on what you have listened to in the past, or how Amazon shows similar products that you might be interested in based on previous purchases, or how the ads showing on your Facebook page are related to what you purchased or viewed online? These are companies that are known for their use of “big data” and analytics to predict and steer customer behaviour. But the truth is that today most organisations are heavily reliant on big data. And the more data businesses amass, the more important it becomes for organisations to be able to harness the information their data provides and use it strategically to improve their operations. This development has given rise to a huge demand for technically talented individuals who can leverage analytics to translate big data into valuable business insights.

In particular, organisations are searching for analytically talented individuals with statistical and programming skills that also understand the business-economic context in which they  will be working, as well as the relevant legal and ethical boundaries of  that work. However, most existing MSc programmes only meet one of these demands, and there are only a few that attempt to meet all. We know this because our Advisory Board, which consists of a number of private sector experts, tells us so. The aim of the MSc in Data Analytics and Business Economics is to fill this educational gap, thereby meeting the demands in the labor market.

The programme is multidisciplinary and is designed to solve business problems by integrating statistics, economics, business, informatics and law. As a student, you will learn how to write your own computer code, how to manage data, how to use statistical machine learning tools in order to explore and deduct hidden patterns from data, and how to incorporate the results obtained into strategic  decision-making. You will learn all this while at the same time developing an understanding for what matters in business and economics; you need to know the industry you will be working in and the problems companies are trying to solve. You will also learn about the relevant data legislation, and why it is important to ensure regulatory compliance when working with sensitive data. You will develop your communicative and collaborative skills. You will learn not only how to work the numbers and draw conclusions, but also how to clearly communicate your results to data technicians and business managers alike.

The program provides rigorous, hands-on training, and it does so through a careful blend of lectures, seminars, case assignments, computer labs and self-studies. The training is carried out under the supervision of a number of carefully selected researchers from across the departments of the School, which are working closely together with the experts of  the Advisory Board in order to maximize the programme’s relevance for employers.

International Master Class

Students with exceptional study results during the first semester at this programme, may compete for seats at the International Master Class programme. An International Master Class is a highly competitive opportunity to go on an exchange semester after finishing your Master’s studies at LUSEM.

International Master class for LUSEM master students


Master coordinator Economics
master [at] nek [dot] lu [dot] se (master[at]nek[dot]lu[dot]se)

Programme documents

Data Analytics and Business Economics – Master's Programme

The programme consists of several parts, each comprising distinct courses (in total 60 ECTS). Each part is presented below.

Autumn semester (September - October)

The following topics will be covered in the course:

  • basic programming concepts, control flow, data structures, conditional statements, loops, functions, scope, and classes, as well as basic syntax for these concepts,
  • using built-in functions,
  • creating own functions,
  • using basic data types, such as lists, vectors and matrices,
  • using an integrated development environment,
  • basic debugging procedures,
  • loading and using basic packages.

The course provides an introduction to theoretical and practical aspects of data visualisation. The following topics are covered in the course:

  • introduction and background
  • introduction to R and ggplot2
  • visualisation of data with few observations
  • choice of colour, symbols, scales, and perspective (2D, 3D)
  • summation and abstraction (many observations)
  • interactive visualisations
  • maps and spatial data
  • visualisation of statistical models
  • Basic programming concepts, data structures, conditional statements, functions, scope, and classes

Syllabus DABN19

Machine learning refers to statistical model predictions that that improve through experience; as new data arrive, the model learns and adapts. The price that the supermarket can charge for advertisements depends critically on its ability to learn from the data which customers that are likely prospects for a particular supplier’s product. Similarly, the price that Google can charge for space for sponsored links is directly tied to their ability to correctly identify people likely to follow the link. That is where machine learning comes in. This course teaches the basics of machine learning and it does so by focusing on those methods that build in one way or another on standard regression analysis. Some of the topics covered are classification based on logistic regression, model selection using information criteria and cross-validation, shrinkage methods such as lasso, ridge regression and elastic nets, dimension reduction methods such as principal components regression and partial least squares, and neural networks. Theoretical studies are interwoven with empirical applications to problems in business and economics.

Syllabus DABN13

Autumn semester (November – January)

The second period introduces elective courses, where you choose two out of three available courses that run parallel during the second half of the semester.

The course introduces legal thinking, and it provides an overview as well as a practical application of legal concepts and methods used to analyse the relevant legal rules and principles related to data analytics. The content of the course is focused on understanding the relevance of key legal rules and principles, related to data analytics, for informed decision-making. The main legal areas covered by the course are European law on intellectual property, data protection, competition law, and the law of contract, as applied to data analytics. An essential part of the course is exercises of an applied nature where legal rules and principles are applied from a strategic and informed decision-making perspective.

Syllabus DABN18

This course covers data, data management and databases from a practical perspective. The student will gain a basic understanding of what databases are and what they are used for, as well as a vocabulary to use when communicating with database administrators and IT technicians. The course also treats how to extract data from a database using techniques such as SQL (Structured Query Language) and how to analyse such data. Data are important in today's industry and society, and this course aims to make the student ready and able to use them to his or her advantage.

Syllabus DABN20

This course covers advanced machine learning methods that are relevant for applications in business and economics, and is intended as a continuation of Machine Learning from a Regression Perspective. Some of the topics covered include bootstrapping, ensemble methods such as boosting and random forests, unsupervised machine learning methods such as principal components analysis and clustering algorithms as well as applications of machine learning methods to problems that are relevant for business and economics, such as causal inference and text analysis.

Theoretical studies are interwoven with empirical applications to problems in business and economics.

Syllabus DABN14

Spring semester (January – March)

  • Elective course (7.5 ECTS)
  • Elective course (7.5 ECTS)

The selection of elective courses may vary between semesters depending on availability. Also, an announced elective course may be cancelled if too few students choose that course, and for some elective courses with high demand a selection will need to be made.

Analytics-based Strategic Management (7.5 ECTS)

Syllabus DABN17

The overall aim of the course is that the students will acquire a working method that will characterize them as action-oriented business analysts. The course will provide theory-based knowledge of strategic management, and an understanding of the connections between different theories in strategic management. Theoretical concepts and models will be related to real-world challenges in companies and applied accordingly in analysis and to present business solutions. The students will acquire abilities to argue in favor of their standpoints in both written and oral presentation.

Business and Artificial Intelligence (7.5 ECTS)

Syllabus INFN65

All organisations are affected by and dependent on processes, decisions and their digitalisation. Most of today’s managerial work requires knowledge and toolsets to manage business to be supported by and automated through Artificial Intelligence (AI). Moreover, to get real business value from AI, businesses must focus their efforts in AI on improving processes and decisions. This course aims to provide an insight into designing business and Artificial Intelligence supporting business. 

Applied Microeconometrics (7.5 ECTS)

Syllabus NEKN33

This course covers modern econometric tools and empirical strategies used by economists and demographers for the analysis of cross-sectional and panel micro- data. The course teaches the econometric theory behind these techniques but also requires reading of high-quality empirical articles and applications of the taught methods using real data sets. Topics covered in the course includes: The randomized experiment as a golden standard and the analysis of social experiments; fixed-effects methods, such as difference-in-differences techniques applied to panel data, but also applied to other data structures such as family-level data, (2) instrumental variables estimation; regression discontinuity design; matching estimators, such as propensity scores and kernel-matching; limited dependent variables.

Time Series Analysis (7.5 ECTS)

Syllabus NEKN34

The course gives an introduction to basic concepts within time series analysis. The univariate analysis of time series in this course is based upon ARMA/ARIMA and ARCH-/GARCH models. Multivariate time series analysis is based on VAR models. Nonstationary time series are analysed using unit root tests, cointegration methods and VEC models. Theoretical studies are interwoven with practical applications in financial economics and macroeconomics.

Deep Learning and Artificial Intelligence Methods (7.5 ECTS)

Syllabus STAN47

This course presents an application-focused and hands-on approach to learning neural networks and reinforcement learning. It can be viewed as first introduction to deep learning methods, presenting a wide range of connectionist models which represent the current state-of-the-art. It explores the most popular algorithms and architectures in a simple and intuitive style. The course covers the fundamentals of machine learning, and the mathematical and computational prerequisites for deep learning; feed-forward neural networks, convolutional neural networks, and the recurrent connections to a feed-forward neural network; a brief history of artificial intelligence and neural networks, and reviews interesting open research problems in deep learning and connectionism.

Analysis of Textual Data (7.5 ECTS)

Syllabus STAN49 

The course provides an introduction to statistical analysis of text. The following topics are covered:

  • Preprocessing of textual data
  • Text representation
  • Text classification
  • Text clustering
  • Topic modelling
  • Sentiment analysis
  • Text summarisation

During the course, both methods based on classic statistical approaches (including Bayesian models) and modern approaches such as deep learning and recurrent neural networks will be presented.

Spring semester (April – June)

The last study period will focus entirely on the degree project. We expect students to formulate a clear and researchable research question at the beginning of this final period. The topic will be selected from a list of broad themes specified by the available supervisors.

Magnus and Christoph share their thoughts after half a year in Lund at the DABE (Data Analytics and Business Economics) master's programme at LUSEM

“Choosing the master's programme was a decision driven by the programme's distinctive emphasis on data analytics and artificial intelligence – central domains that are undeniably likely to shape the future landscape in various areas of life. Recognizing the increasing importance of knowledge in these areas, I found this program combining theoretical foundations and practical application," says Magnus about why he chose the DABE programme.

In an interview we get insights from Magnus and his fellow student Christoph, exploring their Master's Programme experience and perspectives.

News article: LUSEM claims top spot among most applied-for Master's Programmes

Career opportunities

Transforming data into insights that can enhance decision-making is a key challenge for companies of every size, across all industries.

Be it the travel industry, technology, retail, healthcare, manufacturing, consulting, banking, finance, or insurance, data generated from market interaction is being used to determine and influence trends and gain a competitive edge over other players in the field. Companies are therefore looking for experts who have the capacity to use data to make informed strategic decisions. The same is true in government. Being able to minimize costs while at the same time deliver better services to citizens requires making the most of the information available.

Graduates from the MSc in Data Analytics and Business Economics are at a competitive advantage as organizations are looking for people who are not only fluent in the language of data but who also understand how to apply that data in the business-economic context. This layered skillset enables them to communicate effectively with clients, programmers, managers, data scientists, and policy makers to drive strategic decision-making.

Programme requirements

Advisory Board

Our Advisory Board consists of local experts who know how to gain the most from data analytics in business and economics. They advise us how to keep the programme relevant so that our students graduate with the skills in demand by employers. Many board members choose to also participate actively in the teaching through guest lectures and thesis supervision, for example. They are therefore a highly valued resource to the programme.

Alessandro Martinello
Principal Data Scientist
Danmarks Nationalbank

Andreas Keller Leth Laursen
Manager, DC Analytics and Cognitive
Deloitte Consulting

Andreas Olsson
CEO and Co-Founder

Anibal Martinez-Sistac
Senior Data Scientist
Tetra Pak

Anna Johansson
Employer Branding Coordinator
PwC Risk Advisory

Antonio Maranon
Senior Manager, Consumer Panel Methods and Quality
Growth from Knowledge

Babak Vahidi

Erik Dahlberg
CMO and Co-founder
Sanctify Financial Technologies

Fredrik Thuring
Head of Advanced Analytics
Codan Forsikring, Trygg-Hansa

Hampus Poppius
Data Scientist

Hugo Langéen
CIO and Co-Founder
Century Analytics

Isaiah Hull
Senior Economist
Sveriges Riksbank

Jimmy Carlsson
CTO and Co-Founder
Century Analytics

Jonas Ekblad
Head of Middle Office and Business Area Power

Johan Källstrand
Co-founder and analyst

Mattias Jönsson
CTO AI and Lead Data Scientist

Noah Schellenberg
Data Scientist Manager
Tetra Pak

Rikard Green
Quantitative Analyst
Energy Quant Solutions

Sara Moricz
Data Analyst
IKEA Group

Stavros Orfanoudakis
Software Engineer - Exploratory Tester

Oscar Dahlblom
CEO and Co-founder
Sanctify Financial Technologies

Valeri Voev
Lead Data Scientist
The LEGO Group

Thorbjörn Wallentin
CIO and Co-Founder