Master’s programme in Data Analytics and Business Economics

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

Courses

This one-year Master’s degree comprises of 60 credits. The year is divided into two 30 credits semesters.

Semester 1, autumn

Period 1, September–October

  • Programming in R (3.5 cr)
  • Data Visualisation (4 cr)
  • Machine Learning from a Regression Perspective (7.5 cr)

Period 2, November–December

  • Legal Aspects of Data Analytics (4 cr)
  • Working with Databases, (3.5 cr)
  • Advanced Machine Learning (7.5 cr)

Programming in R (3.5 cr)

The following topics will be covered in the course:

  • basic programming concepts, data structures, conditional statements, functions, scope, and classes, as well as the basic R-syntax for these concepts
  • using built-in functions in R, such as “lm”, “max” and “apply”
  • creating own functions in R
  • using basic R data types, such as lists, vectors and matrices
  • using an integrated development environment, such as R-studio
  • basic debugging procedures
  • loading and using basic R-packages
  • using modern data manipulation packages, such as “dplyr”

Data Visualisation (4 cr)

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

Machine Learning from a Regression Perspective (7.5 cr)

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, which are carried out using the R software.


Legal Aspects of Data Analytics (4 cr)

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.


Working with Databases, (3.5 cr)

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 in R. 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.


Advanced Machine Learning (7.5 cr)

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, which are carried out using the R software.

Semester 2, spring

Period 3, January–March

  • Analytics-based Strategic Management (7.5 cr)
  • Elective (7.5 cr)

Period 4, April–June

  • Master’s thesis (15 cr)

Analytics-based Strategic Management (7.5 cr)

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.

The course includes the following four parts:

  • economic organization and the boundaries of the firm
  • markets and competition between firms
  • strategic positioning and competitive advantage of firms
  • strategic organization of firms

An objective of the course is to provide students with specific business cases, which can serve both as empirical illustration and as bases for theoretical analysis. The particular theoretical perspective should thereby prepare student for analyzing and evaluating actual strategy decisions in companies.


Master’s thesis I (15 cr)

The course consists of writing an essay that is publicly defended at a seminar with a discussant and to discuss another essay at a seminar. The essay shall be written individually or by two students writing together. In the process of writing, the student is advised by one or more tutors. The course starts with some gatherings for general information on writing essays and seeking information through the library.


Example of elective courses in Economics, Statistics and Informatics

The selection of elective courses may vary between semesters depending on availability.

Business and Artificial Intelligence (7.5 cr)
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 cr)
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 cr)
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 cr)
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.