NB: the /data folder was too big to pose on github. You can access the data using the following Dropbox link: https://www.dropbox.com/sh/fxcmtbz4o3tacz1/AABjQbeyg27zDh1chZxRDFcpa?dl=0
On successful completion of this module, students will be able to:
- Understand common data format and database structures specific to representative fields of environmental science
- Demonstrate technical competency in handling common data types routinely encountered in the environmental sciences and identify relevant open-source data repositories
- Identify and design suitable data analysis strategies that consider data types, data distribution constraints, strength, benefits and limitations of statistical and modelling tools and environmental dynamics.
- Understand the limitation of available data and data analysis products. Understand sources of errors and demonstrate ability to comprehensively characterize uncertainties and interpret results in the context of these uncertainties, including measurement errors, environmental uncertainties as well as errors stemming from the analytical procedure itself (e.g. calibration of analysis using synthetic data/models).
This module will deliver the core knowledge and skills required for processing and analysing data in the context of climate science. This week, we will focus on:
- understanding climate modelling, and learn how and where to access climate data
- basics of time-series analysis
- basics of geostatistics
We won't be able to go through these topics in detail, but it is hoped that the material covered will help you develop your own skills.
The key objective of the course is to equip the students with the information and technical skills needed to design comprehensive data analysis strategies and deliver thorough analytical results that best exploit the data available considering differences in data types, spatio-temporal coverage and associated uncertainties and errors.
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Data Science From Scartch: First principles with Python http://math.ecnu.edu.cn/~lfzhou/seminar/[Joel_Grus]_Data_Science_from_Scratch_First_Princ.pdf
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Deep Learning for the Earth Sciences: A comprehensive Approach to Remote sensing, climate science and geoscience https://www.goodreads.com/book/show/56733176-deep-learning-for-the-earth-sciences
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Multiple-point geostatistics: stochastic modeling with training images https://www.wiley.com/en-gb/Multiple+point+Geostatistics%3A+Stochastic+Modeling+with+Training+Images-p-9781118662953
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Time series data in python https://www.earthdatascience.org/courses/use-data-open-source-python/use-time-series-data-in-python/
| Date | Lecture | Instructor | Moderator |
|---|---|---|---|
| 2022-01-09 9:00-12:00 Mon | Intro to climate modelling | Y Plancherel | GTA |
| 2022-01-09 14:00-17:00 Mon | Intro to climate modelling (cont) | Y Plancherel | GTA |
| 2022-01-10 9:00-12:00 Tue | Working with climate data I | Y Plancherel | GTA |
| 2022-01-10 14:00-17:00 Tue | Working with climate data II | Y Plancherel | GTA |
| 2022-01-11 9:00-12:00 Wed | Temporal data; time series analysis | Y Plancherel | GTA |
| 2022-01-11 14:00-17:00 Wed | Free | Y Plancherel | GTA |
| 2022-01-12 9:00-12:00 Thu | Spatial data; geostatistics | Y Plancherel | GTA |
| 2022-01-12 14:00-17:00 Thu | Practical time-series/geostat | Y Plancherel | GTA |
| 2022-01-13 9:00-12:00 Fri | self-study, tutorial session | Y Plancherel |
Assessment will be 100% by coursework. It is all open book. Exercises will be distributed and submitted via GitHub Classroom on Friday.
| Release Date | Due Date | Topic |
|---|---|---|
| 2022-01-13 Fri 13:00 | 2022-01-13 17:00 Fri | Environmental data week 1 |