This course will introduce students to fundamentals of data science with a specific focus on the application of these methods to biological data. The topics covered will include: 1) structures for data representation; 2) data visualization; 3) methods for diagnosing quality of data; 4) detection, interpretation, and removal of outlier data; 5) preprocessing methods including variable transformations, feature selection, and feature detection; 6) methods for supervised learning, including classification and regression; 7) methods for unsupervised learning, including clustering.