Online course in Science, Engineering & Maths

Data Science for Environmental Modelling and Renewables

Learn how data science can help us understand our environment and try the tools used by statisticians and data scientists.

Data Science for Environmental Modelling and Renewables

  • Duration 6 weeks
  • Weekly study 4 hours
  • Learn Free
  • Extra benefits From $54 Find out more

Discover how data science can help us understand environmental change

Environmental and climate change impact our lives, but what role does data play in informing us about such changes to our world? On this online course, we examine and explore the use of statistics and data science in better understanding the environment we live in.

You will develop data science skills learning from experts and completing hands-on modelling activities using real world environmental data and the powerful programming language R.

You will also consider how data can help plan the use of renewable energy resources such as wind power.

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Skip to 0 minutes and 7 secondsMARIAN SCOTT: We often hear in the news about endangered species, plastic pollution, poor air quality and its impact in health, but also the benefits of renewable energy, the green economy, and sustainability. With modern technology, many aspects of our lives and our environment are being routinely measured. How can we make sense of these data and use them to understand what impacts our actions have on our planet? In this course, you will learn more about the power of data science in the field of environmental monitoring and renewables.

Skip to 0 minutes and 37 secondsJETHRO BROWELL: Over six feet we'll introduce and explain some of the skills and tools needed when thinking about environmental data. Working with experts and professionals, we'll analyse real world data reflecting some of today's most pressing environmental issues, from climate change, floods and droughts, to energy, renewables, and making an increasing contribution to our energy systems that are heavy weather dependent. As a result, forecasting has become increasingly important to manage supply and demand which have to balance in real time. Data science allows us to produce accurate wind and solar power forecasts from numerical weather predictions and historic data.

Skip to 1 minute and 11 secondsMARIAN SCOTT: As recently as a few decades ago, many rivers suffered from industrial pollution. But now water quality has improved and fish have returned. Looking at data, we can identify and measure the effectiveness of regulatory interventions and inform future policy decisions. Using data science, we can better understand how our environment is changing, what might be driving those changes, how to manage the state of our environment better, and even how to consider what might happen in the future.

What topics will you cover?

  • Time series analysis
  • Quantile regression and extremes
  • Spatial Modelling
  • Open Data and citizen science
  • Forecasting and prediction

When would you like to start?

Most FutureLearn courses run multiple times. Every run of a course has a set start date but you can join it and work through it after it starts. Find out more

  • Available now
    This course started today

What will you achieve?

By the end of the course, you'll be able to...

  • Access and interpret open data which are increasingly available from citizen science
  • Ability to think critically about the nature of environmental evidence and how it is used
  • Appreciate the nature of statistical models and learn how to fit, understand and critique statistical models for temporal, spatial and spatio-temporal data
  • Understand and implement data science for environmental monitoring and renewables
  • Understand the nature of uncertainty and variability in environmental data

Who is the course for?

This course is for people with an interest in environment and/or renewable energy and who wish to gain new skills in data science. It will also be suitable for those with an interest in data science and who wish to learn more about applications in environment and renewable energy. You don’t need to be an expert in R to take this course.

Who will you learn with?

Marian Scott

Marian Scott

I am professor of Environmental Statistics in the University of Glasgow. I enjoy working with environmental scientists, and using statistical skills to learn about the environment from data.

Jethro Browell

Jethro Browell

I am a researcher working on topics in energy systems. I use statistics to describe uncertain processes, and to help people make better decisions in the presence of uncertainty.

Charalampos Chanialidis

Charalampos Chanialidis

Charis is a lecturer in Statistics at the University of Glasgow. He usually doesn't write about himself in the third person.

Ludger Evers

Ludger Evers

Ludger is a part-time lecturer in Statistics at the University of Glasgow.

Anna Sehn

Anna Sehn

Anna is currently doing a PhD in Environmental Statistics, performing statistical analysis of high dimensional remote sensing reflectances of lakes.

Who developed the course?

Founded in 1451, the University of Glasgow is the fourth oldest university in the English-speaking world. It is a member of the prestigious Russell Group of leading UK research universities.


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