Introduction to Programming (3.5 ECTS)
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.
Data Visualisation (4 ECTS)
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 ECTS)
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.
Legal Aspects of Data Analytics (4 ECTS)
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 ECTS)
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.
Advanced Machine Learning (7.5 ECTS)
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.