MEnvSci Environmental Science / Course details

In the Natural Scientist’s Toolkit you will increase your confidence in mathematics, programming and physical sciences. In Semester 1 there are two parts to the unit, covering: (1) maths and physics problem solving, to analyse real world problems like the burning of fossil fuels, and to learn how to apply statistics to geological and environmental data; (2) chemistry of the earth and environment, addressing basic chemical concepts and how they apply to topics such as water chemistry and geological processes. The Toolkit supplements the other first-year lecture and practical units, and equips you with the skills you will need for 2nd year and beyond. 

The aim of the Natural Scientist’s Toolkit is to increase your confidence in numerical problem solving, digital tools and the use of the physical sciences in the study of earth and environmental sciences. Part I of the Natural Scientists Toolkit, in Semester 1, includes Basic Mathematics and Physics, Statistics, and Chemistry of the Earth. 

1. Practice the application of key concepts in maths, physics and chemistry, including manipulating logarithms, rearranging equations, algebra, statistics, geometry, and atomic and molecular structure
2. Apply mathematical theory to describe real-world example problems
3. Interpret the geochemical behaviour of elements based on their place in the Periodic Table
4. Explain how radiogenic and stable isotopes are used to interpret the age of rocks and geochemical processes
5. Show how chemical reactions between the atmosphere, hydrosphere, biosphere and geosphere combine to form geochemical cycles
6. Explain how frequentist statistical thinking is applied in the context of hypothesis testing
7. Identify predictor and response variables and major data types (categorical and numerical, including continuous, discrete and binary), and with access to references select appropriate statistical tests for analysing each type of data
8. Apply basic statistical tests (t-tests, ANOVA, chi-squared tests, linear regression, logistic regression, mixed regression) to data
9. Synthesise results, visualise data and critically evaluate different methods for solving real-world problems