Preface

This text is an introduction to data sciences for Forestry and Environmental students. Understanding and responding to current environmental challenges requires strong quantitative and analytical skills. There is a pressing need for professionals with data science expertise in this data rich era. The McKinsey Global Institute predicts that “by 2018, the United States alone could face a shortage of 140,000 to 190,000 people with deep analytical skills as well as 1.5 million managers and analysts with the know-how to use the analysis of big data to make effective decisions”. The Harvard Business Review dubbed data scientist “The Sexiest Job of the 21st Century”. This need is not at all confined to the tech sector, as forestry professionals are increasingly asked to assume the role of data scientists and data analysts given the rapid accumulation and availability of environmental data (see, e.g. Schimel and Keller (2015)). Thomson Nguyen’s talk on the difference between a data scientist and a data analyst is very interesting and contains elements relevant to the aim of this text. This aim is to give you the opportunity to acquire the tools needed to become an environmental data analyst. Following Bravo et al. (2016) a data analyst has the ability to make appropriate calculations, convert data to graphical representation, interpret the information presented in graphical or mathematical forms, and make judgements or draw conclusions based on the quantitative analysis of data.

References

Bravo, Adriana, Ana Porzecanski, Eleanor Sterling, Nora Bynum, Michelle Cawthorn, Denny S. Fernandez, Laurie Freeman, et al. 2016. “Teaching for Higher Levels of Thinking: Developing Quantitative and Analytical Skills in Environmental Science Courses.” Ecosphere 7 (4): n/a–n/a. https://doi.org/10.1002/ecs2.1290.

Schimel, David, and Michael Keller. 2015. “Big Questions, Big Science: Meeting the Challenges of Global Ecology.” Oecologia 177 (4): 925–34. https://doi.org/10.1007/s00442-015-3236-3.