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The Data Science specialisation focuses on the properties of the methods used in solving business problems, and how they can be improved. These methodological aspects play a more central role in the Data Science specialisation than in the Business Analytics Specialisation.

The programme

The academic year is divided into 2 semesters, each with 3 periods. You complete each period with an examination. In the first semester, you follow the course Impact Evaluation instead of Supply Chain Analytics, which is compulsory for the Business Analytics specialisation. In the second semester, you will follow at least one elective that centres around an application area.

COURSES SEM 1 SEM 2 SEMESTER 1 SEMESTER 2 EC
  • Advanced Analytics for a Better World
    Period 1
    5

    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.

  • Data Science Methods
    Period 1
    5

    Understand fundamental methods used in data science. We also help you develop the skills to apply these methods to economic and econometric data sets.

  • Machine Learning and Optimisation
    Period 1
    Period 2
    5

    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.

  • Impact Evaluation
    Period 2
    5

    Understand advanced econometric methods relevant for the empirical analysis of both predictive and causal relations in business. Interpret estimation and testing results, and know how to build a satisfactory empirical model. Practical experience is gained by applications to economic, marketing and financial data.

  • Privacy, AI, Law and Ethics
    Period 2
    5

    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.

  • Applied AI Research Seminar
    Period 3
    5

    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.

  • Reinforcement and Deep Learning
    Period 4
    Period 5
    5

    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.

  • Restricted-choice electives
    Period 4
    5

    You can choose one of the following electives: Economic and Financial Network Analysis, Machine Learning in Finance or Trustworthy AI for Business and Society.

  • Restricted-choice electives: additional course
    Period 4
    5

    You can choose one of the following electives: Advanced Marketing Analytics, Advanced People Analytics, Economic and Financial Network Analysis, Machine Learning in Finance or Trustworthy AI for Business and Society.

  • Master's Thesis
    Period 5
    Period 6
    15

    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.

Compulsory course
Elective
Specialisation

Honours programme

If you are a student of the Master's 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.

Copyright: UvA
We’ve designed this 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
Birds
Real-life case: predictions of neural networks

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.

Frequently asked questions
  • When do I need to select a specialisation track?

    A specialisation track must be chosen when applying for the Master’s programme. However, track modifications are still possible until late October. The criteria for all tracks are identical and do not impact the likelihood of being accepted into the programme.

  • How many students are in the programme?

    Our Master’s programme admits around  50 students per specialisation track. If you meet the entry requirements, you will be accepted. This Master’s does not have a numerus fixus.

  • What are the weekly contact hours?

    Most courses have one 2-3 hour lecture and one 2-hour tutorial per week. Generally students take 3 courses at a time, so count on about 12-15 contact hours per week.

  • Will all lectures be held in person, or will there be options for online attendance?

    Our preference is for in-person lectures. Certain sessions may be pre-recorded or follow a hybrid format. This entails preparing for Question and Answer (Q&A) sessions through video clips and readings, with subsequent discussions during meetings.

  • Is attendance compulsory for lectures, tutorials, and other sessions?

    Attendance is usually not compulsory for lectures, but commonly for tutorials and other sessions. Students greatly benefit from being present and engaging in discussions with both the instructor and their classmates.

  • What is the typical method of assessment for most courses?

    The majority of courses have a written on-site exam, which counts for a large percentage of the final grade. Most courses have additional assessment methods, including oral presentations, developing research proposals, conducting experiments and writing up results. Finally, some courses grade attendance, which is reflected by presence and activity in tutorials and online assignments.