Assisting Public Sector Decision Makers With Policy Analysis

Course Overview

This course, Assisting Public Sector Decision Makers With Policy Analysis, provides learners with the opportunity to develop data analysis skills that support public sector decision-makers by performing policy analysis through all phases of the policymaking process. Learners will apply data analysis techniques to the core public sector principles of efficiency, effectiveness, and equity. Through authentic case studies and data sets, learners will develop analytical skills commonly used to analyze and assess policies and programs, including policy options analysis, microsimulation modeling, and research designs for program and policy evaluation. Learners will also learn intermediate technical skills, such as Chi-squared tests and contingency tables, comparing samples through t-tests and ANOVA, applying Tukey's honest significant difference to correct for multiple tests, understanding p-values, and visualizing simulations of statistical functions to help answer questions policymakers ask such as “What should we do?” and “Did it work?” In addition, learners will practice statistical testing and create ggplot visuals for two real-world datasets using the R programming language. All coursework is completed in RStudio in Coursera without the need to install additional software.

This course is the third of four courses within the Data Analytics in the Public Sector with R Specialization. It is ideal for current or early career professionals working in the public sector looking to gain skills in analyzing public data effectively, as well as current data analytics professionals or students looking to enter the public sector.

[Applications]
Upon completion of this course, participants will be able to apply data analysis techniques to the core public sector principles of efficiency, effectiveness, and equity. They will have developed analytical skills commonly used to analyze and assess policies and programs, including policy options analysis, microsimulation modeling, and research designs for program and policy evaluation. Participants will also have learned intermediate technical skills, such as Chi-squared tests and contingency tables, comparing samples through t-tests and ANOVA, applying Tukey's honest significant difference to correct for multiple tests, understanding p-values, and visualizing simulations of statistical functions. Additionally, they will have practiced statistical testing and created ggplot visuals for two real-world datasets using the R programming language.

[Career Paths]
1. Policy Analyst: Policy analysts are responsible for researching, analyzing, and evaluating public policies and programs. They use data analysis techniques to assess the effectiveness of policies and programs, and to recommend changes or improvements. They must be able to interpret data and present their findings in a clear and concise manner. As the public sector continues to rely more heavily on data-driven decision-making, the demand for policy analysts is expected to increase.

2. Program Evaluator: Program evaluators are responsible for assessing the effectiveness of public programs and services. They use data analysis techniques to measure the impact of programs and services, and to recommend changes or improvements. They must be able to interpret data and present their findings in a clear and concise manner. As the public sector continues to rely more heavily on data-driven decision-making, the demand for program evaluators is expected to increase.

3. Data Scientist: Data scientists are responsible for analyzing large datasets to uncover patterns and insights. They use data analysis techniques to identify trends and correlations, and to develop predictive models. They must be able to interpret data and present their findings in a clear and concise manner. As the public sector continues to rely more heavily on data-driven decision-making, the demand for data scientists is expected to increase.

4. Data Visualization Specialist: Data visualization specialists are responsible for creating visual representations of data. They use data analysis techniques to create charts, graphs, and other visuals that help to communicate complex data in an easy-to-understand format. They must be able to interpret data and present their findings in a clear and concise manner. As the public sector continues to rely more heavily on data-driven decision-making, the demand for data visualization specialists is expected to increase.

[Education Paths]
1. Master of Public Policy: A Master of Public Policy (MPP) degree is designed to provide students with the skills and knowledge necessary to analyze and evaluate public policy. This degree focuses on the development of policy analysis skills, including the ability to identify and analyze policy issues, develop policy options, and evaluate the effectiveness of policy solutions. It also covers topics such as public finance, public management, and public law. This degree is ideal for those looking to pursue a career in public policy analysis, as well as those looking to gain a deeper understanding of the public sector.

2. Master of Public Administration: A Master of Public Administration (MPA) degree is designed to provide students with the skills and knowledge necessary to manage public organizations. This degree focuses on the development of management skills, including the ability to develop and implement effective policies, manage personnel, and evaluate the performance of public organizations. It also covers topics such as public finance, public management, and public law. This degree is ideal for those looking to pursue a career in public administration, as well as those looking to gain a deeper understanding of the public sector.

3. Master of Science in Data Science: A Master of Science in Data Science (MSDS) degree is designed to provide students with the skills and knowledge necessary to analyze and interpret data. This degree focuses on the development of data analysis skills, including the ability to identify and analyze data, develop data-driven solutions, and evaluate the effectiveness of data-driven solutions. It also covers topics such as machine learning, data visualization, and data mining. This degree is ideal for those looking to pursue a career in data science, as well as those looking to gain a deeper understanding of data analysis.

4. Master of Science in Applied Statistics: A Master of Science in Applied Statistics (MSAS) degree is designed to provide students with the skills and knowledge necessary to analyze and interpret data. This degree focuses on the development of statistical analysis skills, including the ability to identify and analyze data, develop statistical models, and evaluate the effectiveness of statistical models. It also covers topics such as probability theory, statistical inference, and data mining. This degree is ideal for those looking to pursue a career in applied statistics, as well as those looking to gain a deeper understanding of data analysis.