Data Analysis: Tools for Environmental Scientists
The ability to summarize, visualize and analyze quantitative data is a foundational skill for natural scientists, and is crucial for problem solving across a wide range of fields.
In the emerging era of “big data”, it is more essential than ever for natural and social scientists to have high data literacy and the ability to rapidly interpret and interrogate data for answers to targeted questions.
Join this course and you will get an introduction, and basic skills in the modern, open-source programming languages R and Python, both of which are among the most-used programming languages in the world. Through targeted workshops and hands-on laboratory exercises, you will explore essential tools and libraries while learning practical data analysis workflows – from data input and cleaning to exploration, visualization and model-building.
The course covers descriptive statistics, hypothesis testing and predictive modeling. You will apply these data analysis techniques using real-world datasets relevant for environmental challenges including climate change, pollution, and biodiversity loss.
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Course structure
Teaching format
This is a course in applied mathematics and scientific programming. The best way to learn math and the best way to learn programming is to actually do it!
Following this philosophy, course meetings will feature short lectures where key concepts are introduced, followed directly by workshop sessions where students will work together to solve example problems. Students will also receive take-home problems to be solved independently. Solutions to all workshop and take-home problems will be provided, and discussed during question and answer sessions with the instructors.
Assessment
Assessment will be based on written laboratory reports for a series of data labs, and on a written exam.
Examiner
Ida Rahu, ida.rahu@aces.su.se
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Contact
Study counsellors
studeranu@aces.su.se
Course coordinatorIda Rahu, ida.rahu@aces.su.se
Department of Environmental Science, unit of Contaminant Chemistry
