Models, Mathematics and Data Science: How to Make Sure We're Answering the Right Questions
Mathematical models underlie the interpretation of data at all levels, whether we are fitting a simple linear regression or taking advantage of the most sophisticated machine learning techniques. Ultimately the purpose of data science is not to describe data but to make use of data to inform real-world actions, so in order to understand the degree to which our models can help inform decisions, we need to understand how good they are. Dr. Erica Thompson from the LSE Data Science Institute has recently published a book called Escape From Model Land, an accessible introduction to the pitfalls of constructing, calibrating and interpreting models.
This seminar will present a nuanced introduction to the use of models in data science, including how to make the most of the scenarios where we can justifiably have high confidence in model output as well as how to identify and work with those more speculative scenarios where we should expect to have lower confidence.
This is part of the CIVICA Data Science Seminar Series. You can find out more about the series here: https://socialdatascience.network/