Colleen M. Farrelly discusses the importance of interdisciplinary approaches in data science. She provides examples of data science problems involving health risk modeling, market forecasting, and predicting disease from genetic data. For each problem, multiple disciplines could provide relevant insights, such as sociology, nutrition, genetics, economics, and medicine. Farrelly argues that individuals with broad knowledge across disciplines are well-equipped for careers in data science, as interdisciplinary perspectives avoid bias and unreasonable assumptions when solving complex real-world problems.