module 04: data analysis in Python

module 04: data analysis in Python

  • module 4: data analysis in Python
    07/09/2020 - 11/09/2020
    9:30 am - 11:00 am
    Mon 07/09: 9:30 am - 12:45 pm | online
    Tue 08/09: 9:30 am - 12:45 pm | online
    Wed 09/09: 9:30 am - 12:45 pm | online
    Thu 10/09: 9:30 am - 12:45 pm | online
    Fri 11/09: 9:30 am - 11:00 am | online

Course details

week 1 | register now


Python started off as a general-purpose programming language, but in the last decade it has become a popular environment for data science. The reason is that the community of Python users have recently created useful add-on packages which are suitable for data manipulation, preparation, visualization and analysis. This practical course introduces both base Python and the most important packages in a hands-on way with many exercises. The contents of the course are:

  • Introduction: Python and the Anaconda distribution
  • Data types: numbers, strings, lists, tuples, sets and dictionaries
  • Automation: control flow and self-defined functions
  • Importing data and exporting results
  • Managing data with NumPy and pandas
  • Graphs with matplotlib and seaborn
  • Statistical analysis with statsmodels

The objective of the course is that you are capable of doing data management, visualization and analysis in Python on your own. Python is an open-source programming language which you can freely download from (i.e. the Anaconda distribution). Python version 3 or higher is recommended.

Course structure

  • Target audience: This course targets professionals and investigators from diverse areas with little to no Python-programming experience who wish to start using Python for their data manipulation, data exploration or statistical analysis.
  • Exam: There is no exam connected to this module. Participants receive a certificate of attendance via e-mail at the end of the course.
  • Course materials: Copies of Python scripts and data files. Recommended but optional handbook: Haslwanter, Thomas (2016). An introduction to statistics with Python. Vienna: Springer. ISBN 978-3-319-28316-6.


The course is open to all interested persons. Knowledge of basic statistical concepts and experience with other programming languages are considered advantages, but not required for learning the Python language.


Koen Plevoets is a post-doctoral researcher at the Department of Translation, Interpreting and Communication of Ghent University. His research focusses on the cognitive load of interpreters. He obtained his PhD in linguistics in 2008 and he has specialized master’s degrees in Artificial Intelligence and in Statistics. He has over 15 years of experience in categorical data analysis, multivariate statistics and text mining. His interests are visualizations of complex data, for which he uses the open-source programming languages R and Python.

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