Data and statistics are foundational to policy research and practice. As the Social Policy Institute continues to grow, developing opportunities for people to increase knowledge and skills in these areas is a focus of our organization. One example of how we are doing this is a new opportunity led by the Brown School at Washington University in St. Louis and Social Policy Institute: Data and Statistics for Policy Practice.
Designed as a collaboration by faculty, staff, and doctoral students, Data and Statistics for Policy Practice gives students an extra-curricular opportunity to learn, practice, and engage through a collection of self-paced modules and resources. While this particular opportunity is designed for students in the Masters of Social Policy and Graduate Policy Scholars programs at Washington University, it’s the foundation of something bigger.
In addition to building capacity and interest in data and statistics for students, the materials created and feedback received will help to inform the development of an additional training initiative for current and emerging social sector leaders in data science for social impact. This future initiative is part of a multi-year partnership with the Mastercard Center for Inclusive Growth. The end objective of these expanded data science training programs is to equip more people with the skills and knowledge to apply data science to large-scale impact.
To give a little more context about the importance of data science and details about Data and Statistics for Policy Practice, SPI research assistant, Tyler Frank, participated in a Q&A.
Social Policy Institute: What is Data and Statistics for Policy Practice?
Tyler Frank: Data and Statistics for Policy Practice was created to supplement the courses and supports already available at the Brown School while cultivating additional community around data, statistics and data science. We understand that there is no one ‘correct way’ to apply data and statistics to policy practice.
Students who participate will learn about the research process from start to finish, finding and navigating data, data management, methods and tools for data and statistics, visualizing and interpreting data, and applications to policymaking. Along with the content, students also have the opportunity to gain insight from people from various backgrounds and experiences.
SPI: How did this program come to be?
TF: It started with ideas and meetings about the concepts of what we were trying to create. Later, it became a physical product reflecting the group’s imagination and creativity. We must admit, the task of creating a “non-course” from scratch felt daunting, but that’s what a team is for! We each brought different perspectives to the process and learned from each other throughout the experience.
Speaking of teams, our team represents a subset of the diverse interests of students, faculty, and staff who have applied data and statistics to their work. The team includes Brown School and SPI faculty, staff and students: Dan Ferris, Tyler Frank, Jason Jabbari, Sarah Moreland-Russell, Pranav Nandan, Stephanie Skees, and Yingying Zeng. Additionally, students Ashley Byrd, Laura McDermott, and Angelica Santiago-Gonzalez, created and facilitated a recorded conversation around how data, statistics, and evidence are incorporated into policymaking on Capitol Hill. Additional faculty and students have also provided helpful insights and early feedback — we are thankful for the opportunity to collaborate on this endeavor.
SPI: Can someone who is not a data and statistics pro take this class?
TF: Yes! No previous experience needed. The modules are self-paced and feature numerous resources for reference and further learning curated by a team. There is no requirement to be proficient in data science or statistics and the increased familiarity with the content will benefit students interested in learning new skills and knowledge. Students can start from scratch at the beginning or choose specific topics they would like to build familiarity and capacity in (finding data, cleaning data, analyzing data, visualizing, etc.).
SPI: You’re very purposeful in not calling this a class or course. Why is that?
TF: Data and Statistics for Policy Practice is not a course in that there are no grades, due dates, or requirements. However, the content is structured using modules and activities so students can choose how and where they would like to engage. Students may come into the content as they are able and start or supplement their work around data and statistics. This program was designed to meet the increasing interest of Masters of Social Policy and Graduate Policy Scholars new to data and statistics, so it is supplemental to the course offerings.
SPI: Why is this type of programming important?
TF: Data and Statistics for Policy Practice is grounded in real-world practice with topics students would experience in and outside the classroom. The modules comprise the building blocks for research and quantitative methodology and in one place. By synthesizing the material interested learners do not need to search through the plethora of books, articles, and websites for similar materials.
In addition, this program is a space for people with a range of expertise, background, and stories to connect through a shared interest. Many graduate students arrive without previous experience or familiarity working with data and statistics. Some might even think that it’s inaccessible or not for them. Being around faculty and fellow students who are passionate and able to draw the connections to social policy and how data and statistics can be used for social good and impact can spark someone’s interest to learn more.
Through this journey, we hope students make new connections through the content and conversations around the material. This serves as a beginning or checkpoint through students’ academic and professional experience with statistics and data, not the end of the adventure. We’re creating community that makes learning fun and unintimidating and bringing together participants with a wide range of knowledge and experience to learn from each other as well as the modules.
Questions about data science and social policy? Connect with us by emailing email@example.com.