You can download a pdf of the course syllabus here.

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


Bryan J. (2017) Excuse me, do you have a moment to talk about version control? PeerJ Preprints 5:e3159v2

What are the most important “technical” statistical mistakes in ecological history? And were they all THAT important?

Yes, statistical errors are slowing down scientific progress!

Week 2: Functional and object-oriented programming

Lecture: Programming in Ecology and Environmental Sciences Challenge theme: Using R to organise and visualise data

Tutorial: Intro to R, Troubleshooting and how to find help


R for Data Science Chapter 1

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.

The British Ecological Society Guide to Reproducible Code

Open Science Framework

R for Data Science Chapter 4 Workflow

R for Data Science Chapter 27 Markdown

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


R for Data Science Chapter 5 Data transformation

R for Data Science Part II Wrangle Chapters 9 - 16

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

Readings: R for Data Science Chapter 3 Data visualisation

R for Data Science Chapter 28 Graphics for communication

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.

R for Data Science Chapter 7 Exploratory Data Analysis

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

Readings: There’s Madness in our Methods: Improving inference in ecology and evolution

R for Data Science Part IV Model Chapters 22 - 25

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

Readings: Bayesian statistics: What’s it all about?

Week 9: Computing intensive research

Lecture: Earth observation and GIS Challenge theme: Conduct a classification using the Google Earth Engine

Tutorial: Intro to the Google Earth Engine and JavaScript

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.

Spatial data manipulation

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)

Tutorial: Transferring quantitative skills among scientists

Readings: Non-academic careers for ecologists: data science

Profile of Data Scientist: Jenny Bryan