Across a wide range of fields and sectors, access to very large datasets is becoming commonplace. A new generation of analytical tools have emerged to explore and understand the meaning of patterns and relationships buried in these datasets. This course will develop the intuition behind the function of these tools and equip students to use them on real-world data. We begin with a review of basic concepts in statistics and regression, and use them as building blocks for a focus on classification and clustering based on non-regression techniques such as tree-based approaches, support vector machines, and unsupervised learning. This course is intended for first or second year Masters students. Ph.D. students interested in quantitative methods not based on regression may also find this course useful.