Advance your knowledge of operations research and social
good!
Recent technological developments allow data analytics practitioners
to solve large problems better and faster with state-of-the-art artificial
intelligence (AI) tools. At the same time, humanity faces overarching
challenges such as the climate crisis, child malnutrition, systemic racism,
and global pandemics, among others. Operations Research for Social
Good: A Practitioner's Introduction Using SAS and Python showcases
operations research (OR) methodologies typically required in engineering
curricula to applications targeted to make this world a better place.
Designed for data scientists, analytics and operations research
practitioners, and graduate-level students interested in learning
optimization modeling with applied use cases, this book provides the
skills to model and solve OR problems with both SAS and Python as well
as practical tools and tips to bridge the gap between academic learning
and real-world implementations based on Data4Good initiatives.
Natalia Summerville is the Director of Applied Data Science in the Strategy and Innovation Division at Memorial Sloan Kettering Cancer Center. Her team develops data analytics products to support hospital strategy and innovations in care delivery, as well as cutting-edge cancer research. Previously, she led a team of Operations Research and Machine Learning experts at SAS, building analytical engines for customers across industries such as Health Care, Life Sciences, Retail, and Manufacturing. Natalia has been teaching undergrad and grad-level classes in Operations Research, Data Analytics, and Machine Learning since 2005, and she is currently an Adjunct Professor at Duke University. She is deeply passionate about the Data4Good movement and has been collaborating with many non-profit and mission-driven organizations to implement data analytics for social good. She is a board member within the "Pro-Bono Analytics" committee and is part of the "Franz Edelman Award" committee at INFORMS.
