Prescriptive Analytics

Learn to transform data into actionable strategies in Prescriptive Analytics for Digital Transformation. Use Python to build and solve optimization models, tackle complex decisions, and leverage prescriptive tools to drive efficient, data-driven innovations with Dartmouth Thayer School of Engineering faculty Vikrant Vaze and Reed Harder. What you'll learn: 1. Optimize Decision-Making Using Python: Build and solve linear and mixed-integer optimization models with Python tools like Pyomo, tackling real-world challenges in logistics, resource allocation, and planning. 2. Transform Non-Linear Problems: Apply linearization techniques to convert complex non-linear constraints into linear forms for efficient and scalable solutions. 3. Model Complex Decisions: Incorporate integer variables and logical rules into optimization models to handle discrete decisions, such as project selection or facility placement. 4. Evaluate and Refine Models: Use sensitivity analysis, branching, bounding, and pruning techniques to ensure robust and effective solutions that adapt to changing conditions. 5. Leverage Prescriptive Analytics for Strategy: Apply optimization and prescriptive analytics to develop actionable recommendations, enhancing efficiency and decision-making in digital transformation contexts

• Transform data into actionable strategies using prescriptive analytics
• Use Python to build and solve optimization models
• Tackle complex decision-making problems with prescriptive tools
• Drive efficient, data-driven innovations

• Basic familiarity with the topic and its common terminology
• Readiness to practice through applied exercises or case-based work