Data Analytics in the Public Sector with R

University of Michigan via Coursera Specialization

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Provider

Coursera Specialization
Pricing

Paid Course
Languages

English
Certificate

Certificate Available
Effort

8 weeks, 10 hours a week
Sessions

Self-Paced
Level

Intermediate

Found in

Overview

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Every government entity collects and stores millions of data points to perform administrative and legislative duties, allocate resources, and make decisions. Professionals in the public sector need the necessary skills to accurately interpret and inform administrators and policymakers about the meaning behind these data. This Specialization will equip you with fundamental technical skills using the R programming language to gather, manipulate, analyze, visualize, and interpret data to inform public policy and public administrative functions. Throughout four courses, you will gain new skills using the popular tidyverse packages, such as dplyr for data manipulation and ggplot for visualization. You will identify and address common political and ethical challenges in data analysis, and better understand public administration and public policy concepts using hands-on activities with real-world data sets. This course series is ideal for current or early-career professionals in the public sector looking to gain skills in analyzing public data effectively. There are no prerequisites, though programming experience, ideally with the R language, and basic applied statistics knowledge are recommended. The Google Data Analytics Professional Certificate offers such foundational skills. You will earn a dual badge when you complete the Google Career Certificate and this Specialization. All coursework is completed in RStudio in Coursera without the need to install additional software

Syllabus

Course 1: Fundamentals of Data Analytics in the Public Sector with R
Course 2: Exploratory Data Analysis for the Public Sector with ggplot
Course 3: Assisting Public Sector Decision Makers With Policy Analysis
Course 4: Politics and Ethics of Data Analytics in the Public Sector

Courses

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Learn about the core pillars of the public sector and the core functions of public administration through statistical Exploratory Data Analysis (EDA). Learn analytical and technical skills using the R programming language to explore, visualize, and present data, with a focus on equity and the administrative functions of planning and reporting. Technical skills in this course will focus on the ggplot2 library of the tidyverse, and include developing bar, line, and scatter charts, generating trend lines, and understanding histograms, kernel density estimations, violin plots, and ridgeplots. These skills are enhanced with lessons on best practices for good information visualization design. Upon completing this course, you will understand the layered grammar of graphics and its implementation in ggplot2, all while exploring a diverse set of authentic public datasets. All coursework is completed in RStudio in Coursera without the need to install additional software. This is the second of four courses within the Data Analytics in the Public Sector with R Specialization. The series is ideal for current or early-career professionals working in the public sector looking to gain skills in analyzing public data effectively. It is also ideal for current data analytics professionals or students looking to enter the public sector.
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Develop data analysis skills that support public sector decision-makers by performing policy analysis through all phases of the policymaking process. You will learn how to apply data analysis techniques to the core public sector principles of efficiency, effectiveness, and equity. Through authentic case studies and data sets, you 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. You 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, you 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 is the third of four courses within the Data Analytics in the Public Sector with R Specialization. The series is ideal for current or early career professionals working in the public sector looking to gain skills in analyzing public data effectively. It is also ideal for current data analytics professionals or students looking to enter the public sector.
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Gain a foundational understanding of key terms and concepts in public administration and public policy while learning foundational programming techniques using the R programming language. You will learn how to execute functions to load, select, filter, mutate, and summarize data frames using the tidyverse libraries with an emphasis on the dplyr package. By the end of the course, you will create custom functions and apply them to population data which is commonly found in public sector analytics. Throughout the course, you will work with authentic public datasets, and all programming can be completed in RStudio on the Coursera platform without additional software. This is the first of four courses within the Data Analytics in the Public Sector with R Specialization. The series is ideal for current or early career professionals working in the public sector looking to gain skills in analyzing public data effectively. It is also ideal for current data analytics professionals or students looking to enter the public sector.
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Deepen your understanding of the power and politics of data in the public sector, including how values — in addition to data and evidence — are always part of public sector decision-making. In this course, you will explore common ethical challenges associated with data, data analytics, and randomized controlled trials in the public sector. You will also navigate and understand the ethical issues related to data systems and data analysis by understanding frameworks, codes of ethics, and professional guidelines. Using two technical case studies, you will understand common ethical issues, including participation bias in populations and how slicing analysis is used to identify bias in predictive machine learning models. This course also serves as a capstone experience for the Data Analytics in the Public Sector with R Specialization, where you will conduct an applied policy options analysis using authentic data from a real-world case study. In this capstone exercise, you will review data as part of policy options analysis, create a visualization of the results, and make a recommendation. All coursework is completed in RStudio in Coursera without the need to install additional software. This is the fourth and final course within the Data Analytics in the Public Sector with R Specialization. The series is ideal for current or early-career professionals working in the public sector looking to gain skills in analyzing public data effectively. It is also ideal for current data analytics professionals or students looking to enter the public sector.