Earth and Environmental Data Science (EART60702)

Class Overview | Class Schedule | Labs and Projects Info

Class Overview

Course Information: HERE
Course Staff Info:

Instructor: Dr Zhonghua Zheng
Graduate Teaching Assistant (GTA): Junjie Yu, Fei Jiang, Jiyang Xia, Zhiyi Song

Class Schedule

Week	Academic Calendar	Date	Assessment	Topics
W1	Week 20	2026-02-03		Introduction to Earth and Environmental Data Science Introduction to JupyterLab and Git Fundamentals Review of Python: NumPy, Pandas, and Basic Operation of Environmental Datasets Lecture Slides (view) Lab: HERE for notebook
W2	Week 21	2026-02-10		Data Analytics I: Dataframe Computation, Time and Date Functionality (e.g., NumPy and Pandas) High level multidimensional gridded data using xarray Lecture Slides (view) Lab: HERE for notebook
W3	Week 22	2026-02-17		Data Analytics II: Review of Statistics Data Visualization: Principle and Tools (e.g., Matplotlib, Seaborn) Lecture Slides (view) Lab: HERE for notebook
W4	Week 23	2026-02-24	Project 1	Reproducible Research (e.g., Binder, Markdown) Lecture Slides (view) Project 1 (data visualization + data analytics) Presentation Project 1 Peer Review Form
W5	Week 24	2026-03-03		Introduction to Unix Managing Python Environments Git Advanced Lecture Slides (view) Lab: HERE for notebook
W6	Week 25	2026-03-10		Online Repository of Earth and Environmental Science Data Remote Sensing Data: Google Earth Engine and geemap Climate Data: Climate Change Service Lecture Slides (view) Lab: HERE for notebook
W7	Week 26	2026-03-17		Supervised Learning and Automated Machine Learning Unsupervised Learning Applications of Machine Learning in Earth and Environmental Sciences Lecture Slides (view) Lab: HERE for notebook
W8	Week 27	2026-03-24		Organization and Packaging of Python Projects Advanced Data Visualization (e.g., Cartopy) Lecture Slides (view) Lab: HERE for notebook
	Week 28	2026-03-31		Easter break
	Week 29	2026-04-07		Easter break
	Week 30	2026-04-14		Non-teaching period
W9	Week 31	2026-04-21 (Tuesday) AND 2026-04-22 (Wednesday)	Project 2	A Taste of Deep Learning Lecture Slides Project 2 (reproducible research) Presentation Project 2 Peer Review Form
W10	Week 32	2026-04-28 (Tuesday) AND 2026-04-29 (Wednesday)	Literature Review	High-Performance Computing and Dask for Parallel Computing Lecture Slides Scientific Literature Presentation (Machine Learning for Earth and Environmental Science) Scientific Literature Presentation Peer Review Form
W11	Week 33	2026-05-05		Environmental Data Science: a case study Lecture Slides (view) Lab: HERE for HPC notebook
W12	Week 34	2026-05-12	Summary	Summary Lecture Slides Lab: HERE (TensorFlow) and HERE (PyTorch) for notebooks

Labs and Projects Info

Please click HERE to launch JupyterLab
All lab notebooks are available at HERE
Project 1 (35%): code (45%), 10-min presentation (35%), peer-review (10%), project reflection write-up (10%)
Project 2 (35%): code (45%), 15-min presentation (35%), peer-review (10%), project reflection write-up (10%). You are required to demonstrate the performance of your supervised learning model in predicting TREFMXAV_U in Manchester from January 1, 2050, to December 31, 2080. Furthermore, you need to articulate a clear research question and ensure that you provide a reproducible environment (e.g., a environment.yml file) for your audience.
Literature Review (30%): Please find an example from the EDS book and give a 5-min presentation. Your presentation should include: (1) A brief overview of what the example is about. (2) Evidence that you have run the notebook. (3) Your evaluation of the notebook — for example, what are its strengths and weaknesses? What could be improved? If you happen to choose a notebook that requires significant computational resources, please consider selecting a different one. Please upload your presentation to HERE by XXX