Topic models are a suite of algorithms that uncover the hidden thematic structure in document collections. These algorithms help us develop new ways to search, browse and summarize large archives of texts. This talk will introduce topic modeling and one of it's most widely used algorithms called LDA (Latent Dirichlet Allocation). Attendees will learn how to use Python to analyze the content of their text documents. The talk will go through the full topic modeling pipeline: from different ways of tokenizing your document, to using the Python library gensim, to visualizing your results and understanding how to evaluate them.