Prescriptive Analytics

Welcome to Thayer School of Engineering at Dartmouth’s Prescriptive Analytics for Digital Transformation. This comprehensive course is designed to equip you with the tools and methodologies needed to transform raw data into actionable strategies for decision-making in complex, real-world scenarios. By the end of this course, you will be able to design and implement optimization models that solve intricate business problems and align with digital transformation initiatives. This course provides a deep dive into optimization principles and practical applications, beginning with foundational concepts such as decision variables, objective functions, and constraints. You’ll learn to differentiate between linear and non-linear optimization problems, gaining insight into when and how to transform non-linear models into linear ones for more efficient problem-solving. Through hands-on activities and Python-based exercises, you will implement linear optimization models to address challenges like inventory management, resource allocation, and advertising optimization. As you progress, the course introduces more complex scenarios that require mixed-integer linear optimization. By incorporating integer variables into your models, you’ll unlock the ability to tackle discrete decision-making problems, such as determining warehouse locations, project selection, and resource distribution. These advanced techniques will help you formulate and solve optimization problems that mirror the complexities of modern business environments. The course also covers practical tools like Pyomo and cloud-based platforms, ensuring you gain scalable, real-world skills. You’ll explore advanced methods such as branch-and-bound for binary integer optimization, enabling efficient solutions for large-scale problems. Applying these techniques to examples like portfolio optimization and logistics planning lets you see how prescriptive analytics drives operational efficiency and strategic decision-making across industries. You'll consolidate your learning by applying prescriptive analytics to a capstone project. You’ll develop optimization models, analyze results, and prepare a professional report with actionable recommendations tailored to stakeholders. This hands-on experience will prepare you to lead data-driven innovations and effectively communicate the value of prescriptive analytics in decision-making. Guided by Professors Vikrant Vaze and Reed Harder, this course blends rigorous academic instruction with practical, real-world applications. Whether a seasoned professional or new to analytics, you’ll leave this course with the skills and confidence to tackle complex decisions and contribute to your organization’s digital transformation. 3b:T58a,

• ● 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.● Transform Non-Linear Problems : Apply linearization techniques to convert complex non-linear constraints into linear forms for efficient and scalable solutions.● Model Complex Decisions : Incorporate integer variables and logical rules into optimization models to handle discrete decisions, such as project selection or facility placement.● Evaluate and Refine Models : Use sensitivity analysis, branching, bounding, and pruning techniques to ensure robust and effective solutions that adapt to changing conditions.● Leverage Prescriptive Analytics for Strategy : Apply optimization and prescriptive analytics to develop actionable recommendations, enhancing efficiency and decision-making in digital transformation contexts.

• Course: Fundamentals of Digital TransformationSkills: Basic knowledge of Python