Contrary to other reviews, I found this unit both easy to do well in and easy to follow. Some content is a bit clunky, and there is definitely room for improving both the explanations and level of support provided to students, but all said, this unit effectively teaches the basics of scientific programming. This unit uses R (common in earth and environmental sciences), but aims to teach transferable skills that can be used in other languages common to those and related fields (notably atmospheric science), such as Python and MATLAB. This means the code taught is simple and, at times, inefficient - you are not assessed on your ability to improve instructors' examples, you are assessed on your ability to demonstrate you have or are developing those transferable skills and techniques. You will probably find this unit frustrating if you have an existing background in scientific programming - I certainly did occasionally - but within the tasks set out there is still room to develop additional skills alongside those being assessed, or places to implement techniques not covered by this unit. This unit does not exist to teach the cutting edge of R techniques. It exists to develop basic skills necessary in several common languages, and to demonstrate how those skills are applicable to basic case studies.

Anonymous, Semester 1, 2025

Since the last review in 2018, not much has changed. The TL;DR: AVOID THIS UNIT(!!!!!). R is the second-most popular scientific programming language, and almost all statistical analyses in the life & environmental sciences that can be run on a local computer are run in R(Studio). BUT, don't think having some/any programming experience will help you: In this unit, you'll be pushed to use their own copy-and-pasted, clunky R-base code. Coding your own way, even if you reach the same answer, will NOT be marked. This seems to all be in spite of the step changes over the last decade in R scientific programming practices--including way more efficient C# implementations of the statistical & numerical models taught in the unit, code/statistical reproducibility (e.g., this unit doesn't teach 'set.seed()'), and transparent use of AI. In an increasingly uncertain & changing climate and environment, there's simply no place for quarter-of-a-century-old code on old case-studies and resistance to change and new case-studies. There's a recent Conference Abstract online that preaches the teaching successes of this unit--I don't buy it. My advice: take other units if you're after 1) more up-to-date R scientific programming skills and 2) a safe/accomodating/encouraging space for solving environmental & earth science problems

Anonymous, Semester 2, 2026

This unit was disorganised and difficult. The lecturers don't talk to one another and aren't overly helpful. The unit does not require maths or coding experience in theory, but without some, you're totally lost. The computer lab demonstraters can be hit or miss, some are fantastic but some refuse to help you or can't help you because the program used (r-studio) is not generally used by scientists in the field.

Anonymous, Semester 1, 2018