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Data Science


Disclaimer: due to the coronavirus pandemic, this course description might be subject to changes. For the latest updates regarding corona virus, please check this link.

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

Admission requirements:

This course is an (extracurricular) Honours Class: an elective course within the Honours College programme. Third year students who don’t participate in the Honours College have the opportunity to apply for a Bachelor Honours Class. Students will be selected based on i.a. their motivation and average grade.

There are no official prerequisites to this course, but a general knowledge about managing and installing programs and files on a personal computer is assumed. The course is easier if students have any prior coding experience, but it is not required. Those who would like to prepare in advance (which again, is not required) can have a look at the following resources: • • • •


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 characterised 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 and tools of Data Science, an introduction into Python programming for data analysis and then continues with overviews of specific applications.

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 using Python;

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

  • Have developed skills for several tools for analysing data;

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

Programme and timetable:

This course will take place on Tuesdays from 17:15-19:00.

Lecture 1: February 7 (Old Observatory, room C0.03)
Introduction to data science - Python practical: set-up.

Lecture 2: February 14 (Old Observatory, room C1.03)
Supervised learning models: classification - Python practical: introduction to Python and import data.

Lecture 3: February 21 (Old Observatory, room C1.03)
Supervised learning models: regression - Python practical: exploratory data analysis.

Lecture 4: February 28 (Old Observatory, room C1.03)
Unsupervised learning models - Python practical: exploratory data analysis.

Lecture 5: March 7 (Old Observatory, room C1.03)
Tools and model evaluation - Python practical: Model fitting.

Lecture 6: March 14 (Old Observatory, room C1.03)
Data science in society - Python practical: Model fitting.

Lecture 7: March 21 (Old Observatory, room C1.03)
Guest lectures on specific data science applications.

Lecture 8: March 28 (Old Observatory, room C1.03)
Guest lectures on specific data science applications.

Lecture 9: April 4 (Old Observatory, room C1.03)
Guest lectures on specific data science applications.

Lecture 10: April 11 (Old Observatory, room C1.03)
Guest lectures on specific data science applications.

Lecture 11: May 23 (17.15 - 20.15) in Kamerlingh Onnes building, room A0.14
Final seminar with student presentations and discussions.

Old Observatory, room C0.03, C1.03 and end session in Kamerlingh Onnes building room A0.14

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:

10% presentation (10+5 minutes) during final seminar;
40% paper (3000 words);
10% peer review of another group’s work;
30% practical assignment;
10% participation (active).

Brightspace and uSis:

Brightspace will be used in this course. Upon admission students will be enrolled in Brightspace by the teaching administration.

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

Registration process:

Submitting an application for this course is possible from Monday 31 October 2022 up to and including Sunday 20 November 2022 23:59 through the link on the Honours Academy student website.

Note: students don’t have to register for the Bachelor Honours Classes in uSis. The registration is done centrally before the start of the class.

For questions about course content: Marieke Vinkenoog
For administrative questions: Honours Academy