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Charalampos Chanialidis,
Ludger Evers,
Jethro Browell,
Anna Sehn,
and
Marian Scott
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 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.
Topics Covered
Read more
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 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.
Topics Covered
Read more
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 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.
Topics Covered
- Time series analysis
- Quantile regression and extremes
- Spatial Modelling
- Open Data and citizen science
- Forecasting and prediction
Register for this course and see more details by visiting:
OpenCourser.com/course/32yc21/data
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Reviews summary
Data science for environmental and renewables application
According to learners, this course offers a largely positive experience, particularly for those looking to apply data science to environmental modeling and renewable energy. Many highlight its relevance to real-world applications and the clear explanation of theoretical concepts. The modules on time series analysis, forecasting, and spatial modeling are frequently cited as strong points, alongside the inclusion of open data and citizen science. However, some students note an inconsistent pace and express a desire for more in-depth coding examples and practical implementation, suggesting that while the course is accessible to those new to R, additional self-study may be beneficial for truly mastering the techniques.
Deeper R skills may be needed for full benefit despite accessibility.
"The 'no R expertise needed' claim is true for getting started, but to truly benefit, I felt I needed to look up additional resources for R coding."
"If you're entirely new to data science, this is a good start, but be prepared to do a lot of self-study for the programming part."
Covers unique and relevant topics like Open Data and citizen science.
"The citizen science module was interesting but felt a bit detached from the core data science topics."
"The modules on extreme values and quantile regression were quite enlightening."
"I especially liked how it integrated real open data sets."
Offers a strong conceptual understanding of data science methods.
"This course provided a fantastic overview of data science applications in environmental modeling."
"Good course for understanding the concepts of environmental data science."
"The course managed to simplify complex topics without losing rigor."
Directly relevant for environmental and renewable energy professionals.
"As someone working in renewable energy, the spatial modeling and open data topics were directly applicable to my work."
"The focus on real-world environmental data was a major plus."
"The forecasting techniques taught were immediately useful in my work in renewable energy."
The pace can be uneven, too basic for some and too fast for others.
"The pace was inconsistent. Some parts were too basic for me (I have a background in statistics), while others jumped ahead..."
"I found this course somewhat disappointing... explanations often lacked the depth required for complex data science methods."
"It's more conceptual than practical. I would have preferred more challenging coding exercises."
Could benefit from more hands-on coding and practical assignments.
"I wished for more in-depth coding examples beyond the basics presented."
"The practical implementation side (coding challenges, projects) could be expanded to provide more hands-on experience..."
"I struggled with the assignments as they weren't sufficiently aligned with the lecture material."
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Data Science for Environmental Modelling and Renewables.
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Learners who complete Data Science for Environmental Modelling and Renewables will develop knowledge and skills
that may be useful to these careers:
For more career information including salaries, visit:
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Reading list
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Established as a classic in the field, this book focuses on the fundamentals of time series analysis and forecasting, with an emphasis on understanding the underlying principles. It provides a solid foundation for students, researchers, and practitioners seeking a deeper understanding of time series analysis.
Offering a comprehensive overview of time series analysis, this book covers both theoretical foundations and practical applications. It explores various techniques for analyzing time series data, addressing topics such as stationarity, seasonality, forecasting, and more.
Provides a rigorous and in-depth treatment of the theory and methods used in time series analysis. It covers advanced topics such as stochastic processes, spectral analysis, state space models, and more. Suitable for advanced students and researchers.
This introductory-level book provides a clear and accessible overview of time series analysis. It covers basic concepts, techniques, and applications, making it suitable for beginners or those seeking a refresher in the subject.
Explores the application of machine learning techniques to time series forecasting. It covers various machine learning algorithms and provides practical guidance on implementing them for forecasting purposes.
Focuses on time series analysis using state space methods. It covers advanced topics such as Kalman filtering, smoothing, and forecasting, and is suitable for researchers and practitioners in fields where state space models are commonly used.
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Combines theoretical foundations and practical applications of time series analysis using the R programming language. It provides hands-on examples and exercises, making it suitable for students and practitioners seeking to implement time series analysis in R.
This introductory book provides a clear and concise overview of time series analysis and forecasting. It covers fundamental concepts, methods, and applications, making it accessible to students and practitioners with limited prior knowledge in the field.
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This comprehensive book covers a wide range of forecasting methods, from traditional statistical models to machine learning techniques. It provides a practical guide for practitioners and researchers seeking to implement effective forecasting solutions.
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Provides a comprehensive and rigorous treatment of time series analysis. It covers advanced topics such as non-stationary time series, cointegration, and multivariate time series models.
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This comprehensive textbook provides a broad overview of forecasting techniques, from basic to advanced methods. It is suitable for both undergraduate and graduate students, as well as practitioners in various fields.
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This classic textbook comprehensive reference on statistical methods for forecasting. It covers a wide range of topics, from time series analysis to Bayesian forecasting.
Provides a clear and concise introduction to time series analysis and forecasting. It is suitable for undergraduate and graduate students, as well as practitioners who need to analyze and forecast time series data.
Focuses on forecasting economic and financial time series. It covers a wide range of topics, from model selection to forecasting evaluation.
Provides a clear and concise introduction to time series analysis and forecasting. It is suitable for undergraduate and graduate students, as well as practitioners who need to analyze and forecast time series data.
Provides a comprehensive overview of machine learning and deep learning methods for time series forecasting. It is suitable for both undergraduate and graduate students, as well as practitioners who need to develop accurate and reliable forecasts.
Focuses on forecasting in supply chains. It covers a wide range of topics, from demand forecasting to inventory management.
Provides a clear and concise introduction to statistical forecasting. It is suitable for undergraduate and graduate students, as well as practitioners who need to develop accurate and reliable forecasts.
Provides a comprehensive introduction to forecasting methods, suitable for both undergraduate students and professionals new to the field. It covers essential concepts and algorithms with practical examples in R, making it a valuable resource for gaining a broad understanding and applying methods. The online version is continuously updated, making it a current reference as well.
Emphasizing practical applications, this book covers various time series analysis techniques and demonstrates their use through R examples. It provides a valuable resource for practitioners and students seeking to apply time series analysis in their own work.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/32yc21/data
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Time
24 hours
Via
FutureLearn
Institutions
University of Glasgow
The University of Glasgow
Instructors
Charalampos Chanialidis
Ludger Evers
Jethro Browell
Anna Sehn
Marian Scott
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