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Data Science


Admission Requirements

This course is an (extracurricular) Honours Class: an honours elective in the Honours College programme. There are limited spots available for second- and third-year non honours students. Admission will be based on motivation.

Because of substantial overlap, this class is only open to Computer Science students after permission of the course coordinator Esme Caubo:


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.


Mondays 17-19 hrs

4 February
11 February
18 February
25 February
4 March
11 March
18 March
25 March
1 April
8 April
27 May


Old Observatory, Leiden. Room c005.


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

Lectures 1 to 4: 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 5 to 9: data science in several fields of application:
5. Data Science & Social Sciences
6. Data Science & Life Sciences
7. (Social) network analysis
8. Recommender systems and Search Engines
9. Text Mining and Natural Language Processing
10. 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: 9 lectures of 2 hours

  • Seminars: 1 seminar of 4 hours

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

  • Final essay, reviews and presentation: 60 hours

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

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 and uSis

Blackboard will be used in this course. Students can register for the Blackboard site two weeks prior to the start of the course.

Please note: students are not required to register through uSis for the Honours Classes. Your registration will be done centrally.

Reading List



Enrolling in this course is possible from Tuesday November 6th until Thursday November 15th 23.59 hrs through the Honours Academy, via this link. It is not necessary to register in uSis.


Esme Caubo