Earning Criteria

• After this lesson, students will be able to: 1) Describe how machine learning is used to identify diabetes risk factors. 2) Understand how the insights gained from research with large datasets have the potential to transform the way policymakers approach some of society's most important policy decisions. 3) Understand how robust evidence on previous policy outcomes and predictive modeling of future outcomes can guide policymakers to smarter policies with greater benefits at a lower cost.
• After this lesson, students will be able to: 4) Identify major types of data bias that can occur in predictive analysis. 5) Discuss the importance of Evidence-Based Policymaking using administrative and “big” data. 6) Understand moderately advanced statistical inference. 7) Identify ML techniques that can assist in public policy decision-making.
• Anticipated Outcomes: 1) Describe methods of exploratory data analysis used in research. 2) Analyze hypotheses for data related to risk for diabetes. 3) Evaluate a predictive model to inform decision-making for evidence-based assessment. 4) Correlate the data generated by the exploratory data analysis to identify a target for a public health campaign to prevent diabetes. 5) Use behavior-centered design principles to generate a public service announcement to support the diabetes health campaign
• To earn your badge, you will need to complete the DS 1001 (Data Science for All) course modules. If you have questions or are interested in additional information, please email rebecca.schroeder@utsa.edu.