For specific tasks, topological data analysis can be a more rigid, straightforward and interpretable alternative to complicated machine learning pipelines. However, it is not so widely known and can be intimidating to get into when starting from zero. The goal of this talk is to introduce persistent homology, the main tool of topological data analysis, show concrete examples of how to apply it using available Python libraries, and reveal more details about what is going on "under the hood", which is important to correctly utilize the methods. I will start with several examples showcasing the possible uses of persistent homology and how to establish an analysis pipeline in Python. Then I will describe more about different variants within such a pipeline, like a choice of a filtered complex or vectorization, and their advantages and disadvantages.