What is causal inference? Most simply, causal inference is a sub-discipline of statistics and computer science that aims to infer causes from data. I will go through some basic principles of causal inference, illustrate why many standard analyses conducted in the biological sciences (e.g. multiple regression, model selection) frequently violate causal inference principles, show you why experiments are not safe from thinking causally and, finally, provide some recommendations about how to improve causal inference in the biological sciences.

Luther Adams is a benthic ecologist with expertise in marine image annotation. Currently, he is the marine ecosystem scientist at the South African National Biodiversity Institute where he curates the invertebrate reference image and occurrence database. Since taking up this position he has been developing workflows and tutorials for image annotation for a number of image sampling protocols including but not limited to remotely operated vehicle, drop and towed camera images. Over the past few years he has given in-person image annotation training workshops to students and interns from DFFE, CPUT and UCT.

The purpose of this workshop is to equip attendees with skills to make their presentations look good and professional, give tips and tricks to present with confidence, highlight the do’s and don’ts when it comes to presenting and the content of PPTs.

This workshop will cover some basics of using Python in conjunction with environmental data. Specifically focusing on where and how to download environmental data, basic data analysis such as calculating climatologies and visualizing data through maps, time-series and animations. The seminar will use ‘google colab’ which is a convenient platform for Python coding for anyone with a Google account. The seminar is intended for anyone interested in starting out with Python or would like a refresher on the basics.

Join us for a comprehensive (but quick!) walkthrough of HTTP, learn valuable techniques for programmatically accessing data, and explore the potential of leveraging tools like ChatGPT to generate code tailored to your needs. Whether you are a data enthusiast, developer, or researcher, this workshop equips you with practical knowledge and some useful code snippets as a future reference.