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Data Science


Deze informatie is alleen in het Engels beschikbaar.

Disclaimer: due to the coronavirus pandemic, this course description might be subject to changes.

Topics: Data Science
Disciplines: Data science, Life Sciences, Social Sciences
Skills: Research, presenting, academic writing and reviewing

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 (generally 10-15%) available for second- and third-year regular students. Admission will be based on motivation.


Data Science deals with handling, processing, analysing, 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, sports, 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. Parallel to the lectures, students will learn to apply data science tools during the practical sessions by performing data analyses using Python.

Course objectives:

Upon successful completion of this course, students:

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

  • have knowledge of different types of data;

  • are 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.

Programme and timetable:

This course will take place on Mondays from 17:00-19:00 hrs.

Lecture 1: 8 February 2021
Introduction to data science - Python practical: set-up

Lecture 2: 15 February 2021
Supervised learning models: classification - Python practical: introduction to Python and import data

Lecture 3: 22 February 2021
Supervised learning models: regression - Python practical: exploratory data analysis

Lecture 4: 1 March 2021
Unsupervised learning models - Python practical: exploratory data analysis

Lecture 5: 8 March 2021
Tools and model evaluation - Python practical: Model fitting

Lecture 6: 15 March 2021
Data science in society - Python practical: Model fitting

Lecture 7: 22 March 2021
Guest lectures on specific data science applications

Lecture 8: 29 March 2021
Guest lectures on specific data science applications

Lecture 9: 12 April 2021
Guest lectures on specific data science applications

Lecture 10: 19 April 2021
Guest lectures on specific data science applications

Lecture 11: 31 May 2021 (presentations)
Final seminar with student presentations and discussions.


Reading list:


Course load and teaching method:

This course is worth 5 ECTS, which means the total course load equals 140 hours:

  • Lectures: 6 of 1 hour, 4 of 2 hours (participation mandatory

  • Practical sessions: 6 of 1 hour (participation mandatory

  • Preparation lectures: 1 hour/week

  • Preparation practical and practical assignment: 5 hours/week

  • Final group assignment and seminar: 60 hours

Assessment methods:

The assessment methods will look as follows:

20% presentation (5 minutes) during final seminar;
50% paper (3000 words);
20% practical assignment;
10% participation (active).

Brightspace and uSis:

Brightspace will be used in this course. Students can register for the Brightspace module one week prior to the start of the course.

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

Registration process:

UPDATE 29-10 Registration will be possible from Monday 9 November 2020 up to and including Thursday 19 November 2020. The registration link will be posted on the student website of the Honours Academy.