Associate Professor of Information
Fundamentals of Data Analytics in the Public Sector with R
What You'll Learn
- Understand key terms and concepts in public administration, public policy
- Learn foundational techniques using the R programming language
- Create custom functions to analyze population data commonly found in public sector analytics
5 Modules
15 Hours
3 hrs per module (approx.)
Rating
About Fundamentals of Data Analytics in the Public Sector with R
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.
Skills You'll Gain
- Data Analysis
- Policy Analysis
- Public Administration
- RStudio
- Tidyverse
What You'll Earn
- Certificate of Completion:
- Certificates of completion acknowledge knowledge acquired upon completion of a non-credit course or program.
Welcome Message
Fundamentals of Data Analytics in the Public Sector with R is the first course in a professional certificate focused on applying data analytics to public policy and administration. Learners strengthen analytical reasoning while developing hands-on R skills using authentic public-sector datasets. The course is the first course in the Data Analytics in the Public Sector series and integrates policy concepts with technical practice.
This abbreviated syllabus description was created with the help of AI tools and reviewed by staff. The full syllabus is available to those who enroll in the course.
Course Schedule
Module 1: Introduction to Data Analytics in the Public Sector with R
- Video: Welcome from the University of Michigan!
- Reading: Certificate Road Map
- Reading: Course Syllabus
- Discussion Prompt: Meet Your Fellow Learners
- Reading: Help Us Learn About You
- Video: What is the Public Sector & What are Public Goods/Services?
- Video: Data Science and Data Analytics: An Introduction
- Video: What Does Public Administration & Public Policy Mean?
- Reading: Course Glossary of Terms
- Reading: Meaning and Significance of Data Analytics for the Public Sector
- Reading: 7 Reasons for Policy Professionals to Get into R Programming
- Video: Introduction to RStudio
- Ungraded Lab: RStudio
- Reading: Week 1 Optional Readings & Resources
Module 2: Core Functions of Public Administration and R Basics
- Video: General Organization of Government Bureaucracies
- Video: Examples of Government Bureaucracies
- Discussion Prompt: Public Administration Functions: Point of Reflection
- Reading: POSDCORB: Important 7 Steps, Functions, Strength, Criticism
- Video: Core Functions of Public Administration: Planning, Organizing, Staffing, & Directing
- Video: Core Functions of Public Administration: Coordinating, Reporting, & Budgeting
- Video: Applications of Data Analytics to Public Administration Functions
- Video: Getting Started with R: The Basics
- Video: Getting started with R: The Basics of Vectorized Language
- Reading: RStudio Cheat Sheet
- Video: Getting started with R: Attributes of Data
- Video: Exploring R Dataframes: Columns as Variables
- Graded Assignment: Getting Started With R: Practice & Knowledge Check
- Video: Using Vectors to Select and Analyze Data
- Video: Exploring R Dataframes: Dealing with Missing Values Through Boolean Masking
- Video: Exploring R Dataframes: Creating Statistics from Cleaned Dataframe
- Reading: Week 2 Optional Readings & Resources
- Video: One More Thing for This Week!
Module 3: Survey Data Analysis with the Tidyverse
- Video: Survey Data Analytics: Importance, Types and Examples of Survey Data Analytics in the Public Sector
- Video: What Analysts Need to Know About Survey Designs & Types
- Graded Assignment: Survey Data Analytics Knowledge Check
- Video: What Analysts Need to Know About Survey Deployment
- Video: What Analysts Need to Know in Understanding Survey Data | Part 1
- Video: What Analysts Need to Know in Understanding Survey Data | Part 2
- Video: Example: Reading a Codebook
- Video: Example: Dashboards
- Graded Assignment: What Analysts Need to Know: Knowledge Check
- Reading: Required Resources Needed Ahead
- Video: Example of a Codebook: The Behavioral Risk Factor Surveillance System
- Video: Data Manipulation with dplyr: Tidyverse & Exploring the Data
- Video: Data Manipulation with dplyr: Pipe Operator & Adding Variables
- Video: Data Manipulation with dplyr: Formulas in R
- Graded Assignment: Data Manipulation: Practice & Knowledge Check
- Video: Data Analysis: Grouping by Variable
- Video: Data Analysis: Grouping by Multiple Variables
- Reading: Week 3 Optional Readings & Resources
- Video: One More Thing for This Week!
Module 4: Population Data Analysis with Custom R Functions
- Video: Importance of Population Data Analytics & the Three Main Demographic Processes
- Video: Population Data Analytics: Key Population Data Sources
- Video: Population Data Analytics: The Answer to a Basic Population Question
- Graded Assignment: Generating & Interpreting Population Data
- Reading: Resources Needed for Week 4
- Video: Creating Custom Analytics
- Video: Applying Custom Analytics to Groups
- Graded Assignment: Custom Analytics: Practice & Knowledge Check
- Video: Writing Functions
- Video: Alternative Analytics for TDR
- Graded Assignment: RStudio Function for Data Analysis: Practice & Knowledge Check
- Reading: Week 4 Optional Readings & Resources
Module 5: Public Sector Data Analytics in Practice
- Video: Day-to-Day Role of Public Health Data: Interview with Joneigh Khaldun
- Discussion Prompt: Point of Reflection: Day to Day Role of Public Health Data
- Video: Interview with Javed Ali (Part 1): National Security & Government Bureaucracy
- Video: Interview with Javed Ali (Part 2): The Role of Data in National Security as a Public Good
- Discussion Prompt: Point of Reflection: National Security, Data & Government Bureaucracy
- Video: One More Thing for This Week: Congratulations!
- Reading: Course Survey
Grading Policy
Learners must earn at least 80% overall to pass. The course grade is based on an introductory quiz (12.5%), three module quizzes (25% each), and a final quiz (12.5%).
James B. Hudak Professor of Health Policy
Course content developed by U-M faculty and managed by the university. Faculty titles and affiliations are updated periodically.
Intermediate Level
Some related experience required
