This tutorial provides a common ground for both researchers and practitioners interested in data and algorithmic bias in recommender systems. Guided by real-world examples in various domains, we introduce problem space and concepts underlying bias investigation in recommendation, and show two of the most frequently investigated use cases, addressing popularity bias and fairness. Then, we cover a range of techniques for evaluating and mitigating the impact of these biases in recommended lists, including pre-, in-, and post-processing procedures. This tutorial is accompanied by Jupyter notebooks putting into practice core concepts in data from real-world online platforms.