| date | week | topic | module |
|---|---|---|---|
| 11 September 2025 | 1 | Welcome & get ready for the course | module 1 |
| 18 September 2025 | 2 | Data science lifecycle & Exploratory data analysis using visualization | module 2 |
| 25 September 2025 | 3 | Data transformation with dplyr | module 3 |
| 02 October 2025 | 4 | Data import & Data organization in spreadsheets | module 4 |
| 09 October 2025 | 5 | No class | NA |
| 16 October 2025 | 6 | No class | NA |
| 23 October 2025 | 7 | Conditions & Dates & Tables | module 5 |
| 30 October 2025 | 8 | Data types & Vectors & For Loops | module 6 |
| 06 November 2025 | 9 | Pivoting & joining data | module 7 |
| 13 November 2025 | 10 | Creating and publishing scholarly articles with Quarto and GitHub pages | module 8 |
| 20 November 2025 | 11 | Bonus module: Use of AI for coding support | module 9 |
| 27 November 2025 | 12 | Work on Capstone project | NA |
| 04 December 2025 | 13 | Work on Capstone project | NA |
| 11 December 2025 | 14 | Final submission date of Capstone project | NA |
| 18 December 2025 | 15 | Graduation of openwashdata academy | module 10 |
Course Overview
Thank you for your interest in this course. Your course instructors: Lars Schöbitz & Adriana Clavijo are looking forward to meeting you.
We will meet on Zoom for 9 modules over 12 weeks (see Course Calendar) at the following times:
- Start: 11th September 2025 - 2 pm to 4:30 pm CET
- End: 4th December 2025 - 2 pm to 4:30 pm CET
We will use Posit Cloud infrastructure, so you do not need to install any software. You will hear from us about a week before the course starts.
Sign up here: https://ee-eu.kobotoolbox.org/single/7V3qeDYD (it will take about 15 minutes)
Q&A Session Recording Available
The pre-course Q&A session from August 26th, 2025 is now available. Watch the recording to learn about course content, logistics, and see answers to questions from prospective students.
Access the recording and shared notes here: Pre-Course Q&A Session
Who can participate?
To participate in this course, you need to:
- be somewhat connected to the greater Water, Sanitation and Hygiene (WASH) sector (yes, public health, solid waste management, global health engineering, and related topics also count)
- commit 9 × 2.5 hours to participate in Zoom calls
- commit another 3 hours/week for readings and additional exercises for practice
- identify a dataset of your own or your organization that you’re interested in sharing with the public
- have an openness to new ideas and workflows that disrupt current practice
Course Information
This course provides learners with skills in using the collection of R tidyverse packages as a tool for data analysis, reproducible research, and communication. Lectures will be delivered through participatory live coding for students to learn how to write code in code-along exercises. We will use publicly available data related to waste management, air quality, and sanitation.
Project-Based Learning Approach
This course emphasizes hands-on, project-based learning where you will:
- Work with real-world WASH data throughout the course
- Build your data science portfolio with practical examples
- Complete a capstone project using your own dataset
- Learn by doing through interactive coding sessions
- Receive personalized feedback on your work
Open Data & Reproducible Workflow
We are committed to open science principles and will teach you how to:
- Share your data and code openly on GitHub
- Create reproducible analyses that others can verify and build upon
- Document your work for transparency and collaboration
- Contribute to the openwashdata ecosystem
- Follow FAIR (Findable, Accessible, Interoperable, Reusable) data principles
By the end of this course, you’ll not only have technical skills but also understand how to work openly and collaboratively in the data science community.
Learning Goals
Master data science tools - Use R, RStudio IDE, Git, GitHub, tidyverse, and Quarto to analyze and communicate data effectively.
Create reproducible documents - Produce professional reports with Quarto, including citations, figures, and tables.
Practice open science - Share your data and code openly, following best practices for reproducibility and collaboration.
Build a portfolio - Complete real-world projects that demonstrate your skills to future employers or collaborators.
Textbooks and Materials
We will rely entirely on open source and open access material for this course. We will use “R for Data Science” by Hadley Wickham and “Tidyverse Skills for Data Science” by Carrie Wright, Shannon E. Ellis, Stephanie C. Hicks and Roger D. Peng, as complementary reading and learning material for this course. Additional readings will consist of blog posts, journal articles, and reports. All required readings and class material will be provided through this website.
Course Calendar
Weekly Structure
| Monday | |
| Tuesday | Office hours on Zoom from 2 pm to 3 pm CET |
| Wednesday | |
| Thursday | Module on Zoom from 2 pm to 4:30 CET |
| Friday |
Assignments
Quiz: A weekly quiz will be provided to test students’ understanding of the material covered in the previous week. We expect the quiz to be completed within two weeks after the lecture. Successful completion of the course will require students to complete all quizzes.
Homework assignments: Each week will have at least one homework assignment. All assignments, but those for Week 1, are delivered as Quarto documents with instructions and some sample code. Students are required to submit their work through GitHub.
Readings: Every week, additional readings will be provided that support students in learning the underlying concepts that are taught during the class.
Capstone Project: A final capstone project provides students with an opportunity to apply their skills and techniques to real-world data sets. Detailed instructions for the capstone project will be provided. The project will be delivered as Quarto documents and students are asked to submit their work through GitHub. Successful completion of the capstone project is required for successful completion of the course.
Attendance
We hope you can participate in the live lectures, as class participation improves your learning experience. This year, we will record the Zoom sessions and make them available to all students. We will expect you to complete a homework quiz within two weeks after the lecture.
Code of Conduct
This course and the openwashdata community follow a code of conduct. Please ensure that you have read it.