Explore data and algorithmic bias in recommender systems through this comprehensive tutorial from the UMAP'20 conference. Delve into real-world examples across various domains to understand the problem space and key concepts of bias investigation in recommendation. Engage with two practical use cases addressing biases that lead to disparate item exposure based on popularity and systematic discrimination against protected user classes. Learn a range of techniques for evaluating and mitigating bias impact on recommended lists, including pre-, in-, and post-processing procedures. Gain hands-on experience with accompanying Jupyter notebooks that apply core concepts to data from real-world platforms. This 2-hour 37-minute session, led by Ludovico Boratto and Mirko Marras, provides valuable insights for both researchers and practitioners interested in fairness and bias mitigation in recommender systems.