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Data Science


Admission requirements

This course is an Honours Class and therefore in principle only available to students of the Honours College. There are a few places available for regular students.


Data Science deals with handling, processing, analyzing, interpreting, and extracting knowledge from data, ultimately to derive optimal decisions. Often, the term is associated with the concept of big data, i.e., data that is characterized by large volume, high velocity of generation, and data variety, meaning many different types of information.

Today, data science is of paramount importance in just about any domain, ranging from the life sciences, including e.g. health and biosciences, to banking, insurances, retail, and heavy industries.

The possibilities for generating new insights and decisions based on data are considerable. This Honours Class first introduces students to some of the fundamental concepts of Data Science and then continues with overviews of specific application domains.

Course objectives

Upon successful completion of this course, students will:

  • Have a general overview of the possibilities in the field of data science;

  • Have knowledge of different types of data;

  • Be able to understand and perform basic data analysis tasks;

  • Have basic knowledge of some of the tools used in data science;

  • Have developed skills for several tools for analyzing data;

  • Have experience with performing basic analyses for real-world applications.


Monday 13, 20, 27 February, 6, 13, 20, 27 March; 3 April 16.00-18.00hrs. Final session: Monday 15 May: 16.00-20.00hrs


Old Observatory, Leiden.

Monday 13 (room C102), 20 (room C102), 27 February (room C102), 6 (room C102), 13 (room C005), 20 (room C005), 27 March (room C102), 3 April (room C102); 16.00-18.00hrs.
Final session: Monday 15 May: 16.00-20.00hrs (room C104)


The lectures are a combination of (guest) lectures and practice.

Lectures 1 to 3: General Introduction
What is data science? The importance of data science. Techniques used in data science. Different kinds of data. Relation to statistics. These lectures are a combination of (guest) lectures and hands-on sessions with different tools.

Lectures 4 to 8: data science in several fields of application:
4. Data Science & Social Sciences
5. Data Science & finances and forensics
6. (Social) network analysis
7. Recommender systems and information retrieval
8. Data Science & Life Sciences
9. Final seminar with student presentations, discussions and dinner

Course Load

This course is worth 5 EC, which means the total course load equals 140 hours.

  • Lectures: 8 lectures of 2 hours

  • Seminars: 1 seminars of 4 hours

  • Literature reading & practical work: on average 8 hours p/week

  • Final essay, reviews and presentation: 60 hours

Assessment method

The final grade will be composed from the following aspects:

  • 20% presentation (5 minutes) during final seminar

  • 50% paper (3000 words) about one of the subareas

  • 20% double open peer review of two student papers from other subareas

  • 10% participation (active)


Blackboard will not be used in this course.

Reading list

Recommended literature: to be announced.
Obligatory: the papers specified during the lectures.


Enrolling in this course is possible from Monday November 7th until Sunday November 20th through the Honours Academy, via this link


Judith Havelaar