Several options are available for instructors seeking a textbook that introduces big data analysis in the context of fundamental questions in genetics and genomics. A Primer of Genome Science by Greg Gibson and Spencer Muse (whose third edition is soon to be replaced by a new edition from Oxford University Press) nicely juxtaposes problems in bioinformatic analysis with experimental approaches used to generate underlying data. Many of the experimental approaches presented will be familiar to biology majors, and the conceptual framing of questions will be familiar to computer science students. Practice exercises throughout the book open the opportunity for students from these two groups to work together, offering experience in the sorts of real-world, cross-disciplinary collaborations that they may eventually encounter in the field. In Concepts in Bioinformatics and Genomics, Jamil Momand and Alison McCurdy are similarly effective in embracing the experiences of both groups of students. Their book also gives specific attention to concepts in probability theory, illustrating its immediate relevance to the analysis of big data sets. In their 2004 work An Introduction to Bioinformatics Algorithms (soon to appear in a newly illustrated edition), Neil Jones and Pavel Pevzner offer a strong conceptual perspective that emphasizes mathematical approaches to analysis of DNA sequence data. This mathematical emphasis has potential to be either clear or overwhelming, depending on the strength of students’ mathematical preparation. Anna Tramontano’s Introduction to Bioinformatics addresses a largely overlapping set of concepts, but frames its examples around publicly available data sets rather than mathematical principles, making it potentially more accessible to students without a strong background in mathematics.