Agile organizations can gain a competitive advantage by making decisions that are driven by (past) performance data. This course introduces the techniques and tools to transform the raw historical data into actionable insights that can ultimately lead to informed decisions and operational excellence. The students will gain hands-on knowledge of data analysis and predictive modeling using supervised and unsupervised techniques, including classification, regression, and clustering. The students will be introduced to to probabilistic and fuzzy models for decision-making under uncertainty. They will also learn to assess and evaluate the interpretability and explainability of the predictive models. Students will learn to use Cross-Industry Standard Procedure for Data Mining (CRISP-DM) methodology for data-driven projects.