Programming in Python (beginners)

Note: all our courses are taught online this year.

Python code is relatively easy to read and understand, with a large spectrum of applicability (sciences, industries, governance, arts). Python is in high-demand as the programming language with the fastest growing popularity amongst professionals. Even so, skills taught in this course will be transferable to other programming languages.

Course days11-15 January 2021
Course levelMaster, PhD candidates and professionals from all disciplines 
Coordinating lecturersDr. Nick Schutgens
Other lecturersTba
Forms of tuitionOnline Lectures, practicals and assignments
Forms of assessmentApplicable assignments
Credits3 ECTS
Contact hours30 hours
Tuition feeRead all information about our tuition fees and what's included here  
How to applyFind our application form here 

This course is suitable for anybody with an interest in learning to program a computer using Python. Master students and PhD candidates of diverse backgrounds (natural or social sciences, humanities) can benefit from this course. If you have doubts about your eligibility for the course, please contact us: [email protected]

None. However, students will benefit from and are expected to work through CodeAcademy’s first 8 lectures on Python 2. This is a free online course. You do not have to sign up for the PRO membership. 

Through (supervised) practicals and (evaluated) assignments, students will learn how to program primarily by actively coding, thus creating an environment of independence where students can feel secure in their own comprehension and application of Python.

Python code is relatively easy to read and understand, with a large spectrum of applicability (sciences, industries, governance, arts). Python is in high-demand as the programming language with the fastest growing popularity amongst professionals. Even so, skills taught in this course will be transferable to other programming languages.

Lectures will be minimal and highly interactive with many small exercises to engage student’s abilities under professional guidance.

Course topics include, but are not limited to:
•    Concepts in computer programming;
•    Programming as a problem solving tool
•    The Python language;
•    Use of Jupyter notebook
•    Good coding practices;
•    Debugging code; and
•    Visualization.
By the end of this course, students will be able to:
•    Utilize Python to write small computer programs;
•    Have confidence in their ability to increase their programming knowledge through self-study;

Nick Schutgens is an atmospheric scientist with broad experience in programming computers for simulation and data analysis.