You can download a pdf of the course syllabus here.
Week 1 Introduction to data science
https://ourcodingclub.github.io https://ourcodingclub.github.io/course https://datascienceees.github.io/
Week 2 Version control and collaborative coding
Coding Club: Getting started with R and RStudio https://ourcodingclub.github.io/tutorials/intro-to-r/
Troubleshooting and how to find help https://ourcodingclub.github.io/troubleshooting
Readings: https://en.wikipedia.org/wiki/Data_science https://guides.github.com/activities/hello-world/ https://en.wikipedia.org/wiki/GitHub
Bryan J. (2017) Excuse me, do you have a moment to talk about version control? PeerJ Preprints 5:e3159v2 https://doi.org/10.7287/peerj.preprints.3159v2
R for Data Science Chapter 4 Workflow http://r4ds.had.co.nz/workflow-basics.html
R for Data Science Chapter 27 Markdown http://r4ds.had.co.nz/r-markdown.html
Tutorial: Introduction to Markdown https://ourcodingclub.github.io/rmarkdown
Week 3 Functional and object-oriented programming
Coding Club: Intro to git and version control https://ourcodingclub.github.io/tutorials/git/
Coding Etiquette https://ourcodingclub.github.io/etiquette
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)
R for Data Science Chapter 1 http://r4ds.had.co.nz/introduction.html
Week 4 Data manipulation and organisation
Coding Club: Basic data manipulation in R https://ourcodingclub.github.io/tutorials/data-manip-intro/
Efficient data manipulation in R https://ourcodingclub.github.io/data-manip-efficient
Readings: https://www.tidyverse.org/
R for Data Science Chapter 5 Data transformation http://r4ds.had.co.nz/transform.html
R for Data Science Part II Wrangle Chapters 9 - 16 http://r4ds.had.co.nz/wrangle-intro.html
Week 5 Data visualisation and graphics
Coding Club: Beautiful and informative data visualisation in R https://ourcodingclub.github.io/datavis
Customising your figures https://ourcodingclub.github.io/tutorials/data-vis-2/
Beautifying graphs and taking your visualisation to the next level https://ourcodingclub.github.io/tutorials/dataviz-beautification/
Readings: R for Data Science Chapter 3 Data visualisation http://r4ds.had.co.nz/data-visualisation.html
R for Data Science Chapter 28 Graphics for communication http://r4ds.had.co.nz/graphics-for-communication.html
Week 6 Linear models
Coding Club: Intro to model design https://ourcodingclub.github.io/model-design
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. https://www.sciencedirect.com/science/article/pii/S0169534716300957
Open Science Framework https://osf.io/
Tidy modelling in R https://www.tmwr.org
Week 7 Hierarchical models
Coding Club: Intro to mixed effects models https://ourcodingclub.github.io/mixed-models
Readings: There’s Madness in our Methods: Improving inference in ecology and evolution https://methodsblog.wordpress.com/2015/11/26/madness-in-our-methods/
R for Data Science Part IV Model Chapters 22 - 25 http://r4ds.had.co.nz/model-basics.html
Week 8 Intro to Bayesian statistics
Coding Club: Meta-analysis using MCMCglmm https://ourcodingclub.github.io/2018/01/22/mcmcglmm.html
Intro to Bayesian statistics https://ourcodingclub.github.io/stan-intro
Generalised linear models using Stan https://ourcodingclub.github.io/2018/04/30/stan-2.html
Readings: Bayesian statistics: What’s it all about? http://andrewgelman.com/2016/12/13/bayesian-statistics-whats/
Week 9 Big Data in Ecology and Environmental Sciences
Coding Club: Introduction to the Tidyverse https://ourcodingclub.github.io/tutorials/dataviz-beautification-synthesis/
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. http://onlinelibrary.wiley.com/doi/10.1890/120103/full
R for Data Science Chapter 7 Exploratory Data Analysis http://r4ds.had.co.nz/exploratory-data-analysis.html
Week 10 Computing intensive research
Coding Club: Intro to the Google Earth Engine https://ourcodingclub.github.io/earth-engine https://earthengine.google.com/
Tutorials: Intro to the Google Earth Engine and JavaScript https://developers.google.com/earth-engine/tutorial_js_01 https://developers.google.com/earth-engine/tutorial_forest_03
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. https://www.sciencedirect.com/science/article/pii/S0034425717302900
Spatial data manipulation http://rspatial.org/spatial/index.html http://rspatial.org/analysis/index.html
Week 11 Careers in Data Science
Coding Club: Intro to spatial analysis in R https://ourcodingclub.github.io/spatial
Readings: Non-academic careers for ecologists: data science https://dynamicecology.wordpress.com/2014/10/27/non-academic-careers-for-ecologists-data-science-guest-post/
Profile of Data Scientist Jenny Bryan https://ropensci.org/blog/2017/12/08/rprofile-jenny-bryan/
