MSc in Information Studies: Data Science Track 

at University of Amsterdam, Amsterdam

• Literature research
• Academic writing
• The ability to make abstractions from different contexts based on analytical thinking
• Research skills, scientific reasoning
• Reflection: critical assessment on general accepted theories
• Ability to framing: the use of different interpretation schemes (frames) to reason about reality
• Statistics (research methodology)

• Students must be able to:
  • Collect and read in datasets in various formats and write scripts to do basic data pre-processing
  • Take an algorithm of moderate complexity described in pseudo-code and programme it in one or more programming languages
  • Write scripts for applying basic machine learning, statistics, and visualisation on the basis of a library with tools

• Good understanding of the following topics:
  • Random variables, basic distributions such as the normal and binomial distributions, expectations, mean and variance;
  •   Multiple regression, correlation, and hypothesis testing.

Students that enter our Master's programme need to possess the knowledge and skills based on formal courses on the topics that are listed below.

Programming skills (12 EC)
Good programming skills in any language, preferably Python.
Good understanding of the basics of computing, algorithms, and data structures.

Students must be able to:

• Collect and read in datasets in various formats and write scripts to do basic data pre-processing
• Take an algorithm of moderate complexity described in pseudo-code and programme it in one or more programming languages
• Write scripts for applying basic machine learning, statistics, and visualisation on the basis of a library with tools
   

Statistics (6 EC)
Good understanding of the following topics:

• Random variables, basic distributions such as the normal and binomial distributions, expectations, mean and variance;
• Multiple regression, correlation, and hypothesis testing.

Students must be able to:

• Calculate a confidence interval for the expectation on the basis of outcomes of an experiment  do multiple regression in an appropriate tool and interpret the outcomes such as R^2
• Do calculations with the distribution function of for example binomial random variable

Data and Information Modelling (12 EC)

• Knowledge and data: Entity – Relationship Diagrams, Ontologies, Knowledge Representation fundamentals, Semantic Web technologies (RDF, OWL)
• Databases and data structures: SQL, Relational Model, Relational Database use.

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