Research School for Socio-Economic and
Natural Sciences of the Environment
Research School for Socio-Economic and
Natural Sciences of the Environment

Workshop Introduction to data handling and modelling in Python

Date: 02 March 2020
Location: Wageningen University Campus

General information

In this course the mathematical and modelling expertise of prof. Karel Keesman will be combined with the python skills of Caspar Geelen to teach you how to use python to analyse your data as well as fit a model through your data using (linear) regression or custom model equations.

The course will consist of short lectures of theory, python applications of said theory, hands-on exercises and coffee breaks, interchangeably. 

Target groupFor people starting with data handling and modelling in Python, with already some basic understanding of Python. The focus is on people working in the field of Environmental Sciences. For a course on bioinformatics in Python, see upcoming course (16-20 March 2020).
Intended credits0.3 ECTS
Course fee

• WIMEK/WUR PhDs with TSP and WUR postdocs: €90 (early bird) / €140 (regular)
• SENSE PhDs with TSP: €180 (early bird) / €230 (regular)
• All other PhD candidates: €260 (early bird) / €310 (regular)
• Staff WUR graduate schools: €260 (early bird) / €310 (regular)
1. The course fee includes coffee/tea and lunche. It does not include accommodation.
2. The Early-Bird Fee applies to anyone who REGISTERS ON OR BEFORE 2 February; Regular deadline 16 February.

Cancellation conditions

• Up to 16 February, cancellation is free of charge
• Up to 23 February, a fee of 25% of the full costs will be charged.
• In case of cancellation after 28 February, a fee of 50% of the full costs will be charged.
• If you do not show at all, a fee of 100% of the full costs and a fine of 40 euro will be charged.


prof. Karel Keesman

More informationPeter Vermeulen
tel: +31 (0) 317 48 5090
Registration form Register here
Registration deadline   Early bird: 2 February 2020
Final deadline: 16 February 2020
Group size15-30 participants

Learning goals

  • explain relevance of matrices and basic statistics;
  • apply basic data analyses in Python;
  • write linear estimation problems in vector-matrix form
  • numerically calculate unknown linear regression parameters in Python;
  • critically reflect upon the numerical results using prior knowledge and/or practical data;
  • numerically solve differential equations 
    Course programme

Course Programme

- short introduction to the program and its learning goals (15 min)
- short refresher of Python programming (15 min)
- data handling in Python

- coffee break

- hands-on training data handling in Python

- lunch

- scientific modelling ( (non)linear regression/differential eqns) in Python

- coffee/tea break

- hands-on training scientific modelling in Python

- wrap-up/closure 


1) Python
a. At least familiar with most variable types (int, float, string, bool, list) and basic syntax (calculations, range, string formatting)
b. Numpy: arrays, some basic functions

2) Mathematics
a. Basic statistics
b. Differential equation calculus
c. Matrix calculus: matrix transpose, multiplication and inverse (will be recapped briefly)

Participation will require use of a laptop with internet access, on which python 3.7 (preferably) anaconda's python installation, is present (, as the Jupyter interface will be used to illustrate the python code. Lecture slides, code and other course material will also be made available before the lecture. Make sure you install the following packages: Numpy, Pandas
(type conda install numpy pandas scikit-learn scipy matplotlib seaborn statsmodels or have a look at conda basics at