1. Rooster on Datanose
  2. Canvas: zie link op datnose
    1. wordt alleen gebruikt voor inleveren opdrachten en cijfers
  3. Docent, times
  4. Literature
    1. Lecturenotes
    2. NoteBooks
    3. Software
  5. Course Objectives/Leerdoelen
  6. Examination/toetsing
  7. Week by Week/Course Plan


  1. Lecturer: Maarten Marx,
  2. For assistants, times, see Datanose


All books are freely and legally available on the web:


Each lecture will be accompanied by lecture notes and/or slides. These notes are typically IPython Notebooks or MarkDown files. Lecture notes contain pointers to the literature and form the basic requirements of what you are supposed to know. They are excellent material for helping you master the course and know what you should study for the exam.


Lectures may be accompanied by IPython notebooks, as indicated in the lecturenotes and the courseplan. You can view notebooks using, but if you want to run them you should download them and run them on your own machine.


We make use of Python and NetworkX software. Networkx needs the numpy and matplotlib packages.

You must have all this software installed on your own laptop.

We strongly advice you to install the Anaconda Python Distribution. This distribution contains all the necessary modules and packages needed for this course. In particular, it contains networkx and IPython notebooks. It is available for all platforms and provides a simple installation procedure/ You can download it from: More detailed installation instructions.

We assume you know quite some Python and are able and willing to learn more. Check out this introductory class and see what is new for you, and get that knowledge.

This class is supported by DataCamp. All students have a free membership for all courses for 6 months.

“This class is supported by DataCamp, the most intuitive learning platform for data science. Learn R, Python and SQL the way you learn best through a combination of short expert videos and hands-on-the-keyboard exercises. Take over 100+ courses by expert instructors on topics such as importing data, data visualization or machine learning and learn faster through immediate and personalised feedback on every exercise.”

Course Objectives

See Course description in Course Catalogue (Studiegids)


A schedule of all assignments and exams and their weight can be found in the CoursePlan.

We assess progess in this course by weekly assignments which are graded, and by two exams. Assignments are made and handed in individually. Exams are individual.

For grading exams, we follow in this course the rules of the OER, which can be found at