Analytics for Social Good
** available as of 06/15/2026
** available as of 06/15/2026
Prerequisites: Junior Standing; minimum grade of "C-" in SCMG 332 or SCMG 332H or SCMG 232 or SCMG 232H; INFS 247 or INFS 247H; ISSCM 241 or ISSCM 241H or STAT 103.
This course introduces students to the application of analytics methods in the context of humanitarian, nonprofit, public health, and public service supply chains. Students will develop foundational skills in probability, descriptive analytics, data visualization, simulation, and predictive modeling using Excel and Python. Through cases and projects, students will apply these tools to analyze and address challenges in socially focused supply chain and operations settings.
Outcomes: Students will be able to use data-driven methods to evaluate, interpret, and communicate solutions to operational problems in social-good contexts; Develop technical proficiency in key analytics methods, including descriptive analysis, data visualization, probability, simulation, predictive modeling, and clustering techniques, using Excel and Python; Understand the unique goals and challenges of humanitarian, nonprofit, public health, and public service supply chains, and apply analytics to address real-world problems in these contexts; Analyze, interpret, and solve operational problems by applying quantitative methods and evaluating the trade-offs between various objectives in social-good contexts; Communicate findings effectively through concise briefs, reports, and presentations tailored to both technical and non-technical stakeholders; Work collaboratively to design and evaluate data-driven solutions for case-based and project-based problems; Reflect on the broader ethical and societal implications of applying analytics in social-good contexts.
This course introduces students to the application of analytics methods in the context of humanitarian, nonprofit, public health, and public service supply chains. Students will develop foundational skills in probability, descriptive analytics, data visualization, simulation, and predictive modeling using Excel and Python. Through cases and projects, students will apply these tools to analyze and address challenges in socially focused supply chain and operations settings.
Outcomes: Students will be able to use data-driven methods to evaluate, interpret, and communicate solutions to operational problems in social-good contexts; Develop technical proficiency in key analytics methods, including descriptive analysis, data visualization, probability, simulation, predictive modeling, and clustering techniques, using Excel and Python; Understand the unique goals and challenges of humanitarian, nonprofit, public health, and public service supply chains, and apply analytics to address real-world problems in these contexts; Analyze, interpret, and solve operational problems by applying quantitative methods and evaluating the trade-offs between various objectives in social-good contexts; Communicate findings effectively through concise briefs, reports, and presentations tailored to both technical and non-technical stakeholders; Work collaboratively to design and evaluate data-driven solutions for case-based and project-based problems; Reflect on the broader ethical and societal implications of applying analytics in social-good contexts.