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Week 1: Introduction to Data Science
Lecture: Intro to GitHub and version control Challenge theme: Set up your own GitHub repository (assessed as a part of the repository mark)
Tutorial: Intro to GitHub
Week 2: Functional and object-oriented programming
Lecture: Programming in Ecology and Environmental Sciences Challenge theme: Using R to organise and visualise data
Readings: https://en.wikipedia.org/wiki/Computer_programming https://en.wikipedia.org/wiki/Programming_language https://en.wikipedia.org/wiki/Functional_programming https://en.wikipedia.org/wiki/Object-oriented_programming https://en.wikipedia.org/wiki/R_(programming_language)
Week 3: Developing workflows
Lecture: Work flows and how to address quantitative challenges Challenge theme: Development of a reproducible workflow
Tutorial: Introduction to Markdown
Readings: Parker, T.H., Forstmeier, W., Koricheva, J., Fidler, F., Hadfield, J.D., Chee, Y.E., Kelly, C.D., Gurevitch, J. and Nakagawa, S., 2016. Transparency in ecology and evolution: real problems, real solutions. Trends in ecology & evolution, 31(9), pp.711-719.
Week 4: Data manipulation and organisation
Lecture: Data structures, hierarchy and organisation Challenge theme: Organising and summarising a large dataset
Tutorial: Introduction to the Tidyverse
Week 5: Data visualisation and graphics
Lecture: A figure tells a 1000 words (or more) Challenge theme: Visualise data to answer a research question
Tutorial: Intro to data visualisation
Week 6: Big Data in Ecology and Environmental Sciences
Lecture: How big data is changing the fields of Ecology and Environmental Science Challenge theme: Working with the Living Planet Index and the Global Biodiversity Information Facility Additional content: We will begin a revision of statistics including linear models in this week that will continue into the next session.
Tutorial: Working with large datasets
Readings: Hampton, S.E., Strasser, C.A., Tewksbury, J.J., Gram, W.K., Budden, A.E., Batcheller, A.L., Duke, C.S. and Porter, J.H., 2013. Big data and the future of ecology. Frontiers in Ecology and the Environment, 11(3), pp.156-162.
Week 7: Hierarchical models
Lecture: Hierarchical models in Ecology and Environmental Sciences Challenge theme: Fitting a hierarchical model Additional content: We will also explore the concept of simulation to understand how a linear model is fitting data using different random effect structures.
Tutorial: Introduction to linear mixed models
Week 8: Intro to Bayesian statistics
Lecture: Going Bayesian: How does Bayesian statistics differ from frequentist statistics? Challenge: Fitting a Bayesian linear model
Tutorial: Meta-analysis using MCMCglmm
Week 9: Computing intensive research
Lecture: Earth observation and GIS Challenge theme: Conduct a classification using the Google Earth Engine
Readings: Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D. and Moore, R., 2017. Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment, 202, pp.18-27.
Week 10: Careers in Data Science
Lecture: Trip to the DataLab and School of Informatics Challenge theme: Blog post on your experience on the course (assessed as a part of the repository mark)