Logic and
Theory of
Discrete Systems

Informatik 7


Foundations of Data Science


In the age of "big data" and "advanced analytics", data processing faces new challenges. Queries become more complex and often involve data mining and machine learning tasks, and the scale of the datasets requires new algorithmic approaches.

This course will cover the theoretical foundations of modern data processing and analytics. This includes topics from database theory, such as data models, the analysis of query languages, and basic algorithmic and complexity theoretic questions related to query processing. It also includes topics from algorithmic learning theory, such as basic machine learning algorithms, support vector machines, the PAC model, and VC-Dimension. Furthermore, it includes new models of computation on massive datasets, such as the streaming model and the map-reduce paradigm, and algorithms for these models.

We will focus on computational aspects of the theory. Statistics, though undoubtedly one of the foundations of data science, will not play a central role in this course.

Lectures and exercises will be in English.


Time and Place

Tuesday, 8:30 - 10:00 am in 2350|111 (AH II)

Thursday, 3:15 - 4:45 pm in 2350|314.1 (AH III)


Martin Grohe


Monday, 4:15 - 5:45 pm in 2356|056 (5056), held by Marlin Frickenschmidt


There will be weekly exercise sets. Completing these successfully (at least 50% of possible points) is necessary for admittance to the examination.

A new exercise sheet will be released every Thursday in our L2P room. Each sheet has to be handed in before the Thursday lecture a week later, or in our box in E1, first floor before 15:15.


The modalities will be announced later. The planned exam dates are:

25.02.2016, 11:30 am, 2350|111 (AH II)

31.03.2016, 11.30 am, 2350|009 (AH I)


S. Abiteboul, R. Hull, V. Vianu. Foundations of Databases. Addison Wesley 1995.

J. Hopcroft, R. Kannan. Foundations of Data Science. Unpublished, draft available online.

M. Kearns, U. Vazirani. An Introduction to Computational Learning Theory. MIT Press 1994.

J. Leskovec, A. Rajaraman, J. Ullman. Mining of Massive Datasets. Cambridge University Press 2014.

S.J. Russell, P. Norvig. Artificial Intelligence: A Modern Approach. 3rd Edition, Pearson 2014.

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