The academic year is divided into 2 semesters, each with 3 periods. You complete each period with an examination.
Apply mathematical optimisation and machine learning for better decision making. Model relevant societal problems into the framework of optimisation and machine learning and solve them in small teams.
Understand fundamental methods used in data science. We also help you develop the skills to apply these methods to economic and econometric data sets.
Understand the interaction between machine learning and optimisation at an advanced level. Develop the skills to deal with large datasets in order to solve complex global business issues.
Understand EU personal data protection (privacy) legislation. Put your insights into practice through in-class assignments: an assessment of a hypothetical project that deals with issues like a new app or AI-system that involves the processing of personal (sensitive) data.
Apply deep learning techniques for computer vision, information retrieval and multimedia. Use the knowledge from this and previous courses to address a relevant business or societal challenge. The challenges are provided by our governmental and corporate partners in the Amsterdam Data Science network, such as the City of Amsterdam.
Understand the exploration/exploitation trade-off via the study of multi-armed bandits and of Markov Decision Processes. Implement various reinforcement algorithms (Monte Carlo & temporal-difference) for more complex problems.
Understand advanced econometric methods relevant for the empirical analysis of both predictive and causal relations in business. Interpret estimation and testing results and how to build a satisfactory empirical model. Practical experience is gained by applying regression analysis to economic, marketing and financial data.
You can choose from a selection of electives from both the Amsterdam School of Economics and the Amsterdam Business School to tailor the programme to your interests:
The Master’s thesis is the final requirement before you can graduate. It is often combined with an internship at a company, institution, public sector agency or semi-governmental organisation. This is your chance to dive deep into a new development or idea that you are enthusiastic about. When writing your thesis, you have the chance to explore it fully while simultaneously training your ability to independently conduct relevant research. You will be awarded the title Master of Science (MSc) upon graduation.
If you are a student of the Master Data Science and Business Analytics and you have a record of academic excellence, a critical mind and an enthusiasm for applied research, then our Honours programme is a great opportunity for you.
We’ve designed this new Master's with input from leading companies. These companies increasingly rely on professionals who can apply data science in various business fields to optimise operating results.Prof. Cees Diks
Programme Director Cees Diks and student Hania explain.
Neural networks are increasingly popular for predictive analytics, but they can sometimes fixate on unrelated data features. For example, when training a neural network to predict if a bird in a picture is a landbird or a waterbird, it may wrongly focus on background colour, leading to performance issues. This can be improved by identifying which parts of the feature spaces are truly associated with the main task. This is an important topic within current research in applied AI.