This course will help you take control of data and familiarize yourself with the tools to use it in the planning, management, and evaluation of public policies.
This course offers comprehensive insight into data analysis and statistics, empowering participants like Dario, who used the knowledge to create DATASIMUS—a portal for urban mobility data. The course is delivered through videos and real-life cases, enabling learners to master data use, understanding, and interpretation to aid decision-making in public management.
No specific prerequisites are needed. However, participants will require:
Individuals interested in the intersection of data analysis and public policy, such as public administrators, NGO workers, and policymakers. This course is also suitable for beginners in data analysis with no prior exposure.
The acquired skills can be leveraged to improve decision-making processes within governmental or non-governmental organizations by utilizing data to formulate effective public policies. It may also enhance personal capabilities in managing projects, interpreting data, and aligning organizational goals with empirical research.
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How can we know if the policies and interventions implemented in low- and middle-income countries are making a difference, if they are really improving outcomes? This course provides the experimental and statistical tools needed to rigorously measure the effects of policies and programs, with a particular focus on questions of causal attribution, the design and conduct of randomized control trials and quasi-experimental methods, and estimation of impacts using regression techniques.
Economic development is about making a difference in the lives of the poor, through interventions in the health, education, microfinance, transport, agriculture, and other sectors. This course will provide you with the experimental and statistical tools you need to measure the impacts you are hoping to see. How do you design and conduct a randomized control trial, and how do you evaluate the data using regression techniques? What other quasi-experimental methods can be used? How do you know you’re making a difference?
