- Advanced R – application for statistics in agricultural sciences
- Basic Python for Data Science
for postgraduate students in West Africa (Benin, Burkina Faso, Niger, and Mali)
Funding source: Federal Foreign Office, Germany through the German Academic Exchange Service (DAAD)
DAAD Project ID: 57587014
Coordination: Pro-RUWA team at University of Kassel (Germany)
Deadline for application: 05 December 2025
Contract location: Witzenhausen/Kassel, Germany
Deadline for completion: April 30, 2026
Contact person: PD Dr. Martin Wiehle
Background
The German Academic Exchange Service (DAAD)-funded West African-German network „Promoting Academic Capacities for Sustainable Agricultural Resources Use in West Africa“ – Pro-RUWA was established to train a new generation of regionally and internationally educated young scientists, entrepreneurs, and administrators. Pro-RUWA builds on existing structures of three young universities in West Africa namely the University of Abomey-Calavi (UAC, Benin), University of Abdou Moumouni (UAM, Niger), University of Daniel-Ouezzin Coulibaly (UDOC, Burkina Faso) that have recently decided to internationalize their graduate education.
Within the network, several units exist or are about to be established which capacitate future decision-makers in francophone West Africa to jointly overcome bottlenecks in resource misuse and food insecurity at the national and regional level by fostering university education, international relations, and regional development. One of the Pro-RUWA pillars is to capacitate students and lecturers in up-to-date technologies which allow them to better understand the complex nature of socio-ecological systems by collecting, processing, analysing, and interpreting multidimensional data. To do so, we develop and provide open-source e-learning and e-teaching materials and courses with a particular focus on the realities and educational context of the three countries. In recognition of the need for a broader reform in research methods and data science teaching, we see these resources being used not just for the MSc and PhD level courses but for a number of other courses and contexts.
In this context, classical and up-to-date statistical algorithms and applications empowered through the open-source software R as well as Python are vital in agricultural research for its robust data analysis capabilities. Both applications handle diverse data types offering tools for exploratory analysis, hypothesis testing, and predictive modelling. Its comprehensive statistical functions enable an understanding of complex trends, essential for effective agricultural decision-making. This helps researchers optimize resource use and improve management strategies. Their open-source nature encourages innovation and adaptation to agricultural challenges, supporting efforts in food security and sustainability. We aim to produce materials licensed as open educational resources (Creative Commons license CC BY) and wherever possible all other materials including data shall be freely accessible.
Advanced R course: This advance course requires more advancing in automation, reporting & advanced R programming, writing custom functions and iteration, automated report generation with Quarto/R Markdown, creating professional tables and Word outputs, data validation and quality checks, as well as workflow automation. The topics should include the essentials of R/RStudio usage, understanding R syntax, and performing these tasks.
Python Data Science course: Basic course in Python for data course should include basic data analyses skill using Numpy, Matplotlib & Seaborn, file handling & coding practices, AI coding tools, Pandas & Scipy, and introduction to machine learning.
Both online courses should emphasize on practical applications and aim to use examples from agricultural sciences and experimental biology/ecology to equip students with the skills needed for handling real-world data, performing adequate analyses, and presenting findings professionally.
Intended output
Overall, we propose e-learning courses with about 12 learning hours fully online each. Materials should be made available online for self-paced learning. The online repository will be the in-house and Moodle-based African Excellence platform DIGI-FACE. Graduate students who want to use either/or programming languages application for statistics for their own research, or teachers who wish to include these in their teaching programs.
We request a three-step stage plan including the (i) development of the respective module, the (ii) implementation and also a later (iii) an adjustment phase based on feedbacks by participants, which should be reflected in the course development offer.
Applications for one or two courses courses (R and Python) are possible
How to apply?
The application must contain
- Letter of motivation (max. 1 page)
- CV (individual persons) or company profile including portfolios of already completed and conducted contracts (max 3. pages)
- Pricing by task (pre- and post-production, based on salaries usual in the higher education sector) including VAT (MwSt) – if applicable. Projected costs must include all course-related expenses (salaries, consumables, if applicable mobilities) – i.e. no additional costs will be covered.
Please submit your application as one pdf file with your full name and course, example: NameFamilyName_AdvanceRStatistic
Contact
www.uni-kassel.de/forschung/pro-ruwa/home
Tel: +49 (0) 5542 98 1372
email: pro-ruwa@uni-kassel.de
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