Big Data

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The text below is a pre-announcement. The information in it is placed here without prejudice.



Maarten de Rijke and Ana Varbanescu


After the course, the students

  1. are familiar with the fundamental concepts and technologies in data science, their uses as well as their foundations;
  2. will have some experience in working with data, with discovering knowledge from data and visualizing their discoveries;
  3. are familiar with some of the societal risks that data science entails.


Data has been called the new oil. Data science, the emerging research field centered around the scientific analysis of data, promises to extract knowledge from data. Data scientists make discoveries while swimming in data. They are able to recognize structure in large quantities of formless data and make analysis possible. Data science is about identifying rich data sources, joining them with other, potentially incomplete data sources, and cleaning the resulting set. In a landscape where data never stops flowing, data scientists help decision makers shift from ad hoc analysis to an ongoing conversation with data.

Data science has begun to alter many aspects of our lives. Commerce and research are being transformed by data-driven discovery and prediction. Finance, education, energy management, transportation, food production, retail, health, culture, intelligence, are just a few of the areas that have come to depend on data science. The potential is great but so are the risks. In relatively few years, we are transitioning to an algorithmic society and it is not immediately obvious that society as a whole is ready for this transition.

In the course we will tour the basic techniques of data science, including solutions for massive data management and the typical stages that make up data science projects (data selection, data integration, machine learning, visualization). Sessions on data science principles will be interleaved with presentations on real-world case studies, in domains as diverse as health, finance, intelligence, the creative industry. While the course will emphasize the principles and practices of data science, it will also reflect on its pitfalls.

Recommended prior knowledge

A basic understanding of statistics and probability theory will be helpful. A keen interest in algorithmic thinking will be helpful too.

Registration at

UvA students can enroll from December 2015 onwards using the course code 5512BIDA6Y in SIS.

Contract students and students from other institutions can enroll through a registration form which will be made available in the same period here below. 


Twelve weekly classes consisting of interactive lectures, discussions, and in-class exercises. We alternate between “principles” classes taught by leading academic researchers in data science and “practice” classes taught by practitioners from local data science industries and governmental organizations.


For times and locations see:[43406]

Study materials

Reader, slides plus excerpts from popular science publications on big data and data science.


See  here


Essays, participation in discussions, hands-on assignments.

6 ECTS, 14 weeks
Language of instruction
Starts in

Published by  Institute for Interdisciplinary Studies