Education Data Done Right: Volume II
Education Data Done Right: Volume II
Building on Each Others' Work
About the Book
Six data analysts with expertise in public education agencies across the country have teamed up to write a new volume for the Education Data Done Right (EDDR) series.
Following the success of the first volume in this series, which covered missing elements critical to success in building data capacity in education agencies, this new volume showcases applied data science strategies and lessons learned in agencies across the country. New chapters explain how to document work to ensure continuity of operations, navigate data governance, and ensure transparency and reproducibility of agencies’ important work; how early warning systems can be useful in agencies’ work; and why self-awareness of how identities shape the work is one of the most important skills data analysts must have.
Written by Dorothyjean Cratty, LaCole Foots, Wendy Geller, Jared Knowles, Justin Meyer, and Ellis Ott, this new volume covers education data science strategies and lessons with a perspective only possible from authors who’ve been in the trenches and gotten the work done. The series is intended for education agency data analysts, and data managers, strategists, and leaders seeking to improve how their agency operates. Many education agency data analysts come from a social science research background and the transition to work inside agencies can come with a lot of new challenges. This series is a guide through those challenges.
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About the Contributors
Table of Contents
- Education Data Done Right: Volume 2
- Praise for Volume 1
- 1 Introduction
- 2 Organizing and Documenting Analysis Projects
- 2.1 Introduction
- 2.2 What to Do Before You Do Anything
- 2.3 Why Organize and Document?
- 2.4 How to Organize and Document
- 2.5 What a Finished Product Should Look Like
- 2.6 Managing Work in Progress
- 2.7 Writing Good Code
- Code Should Do What It Says It Does: Don’t Get Crazy With Names
- Include Comments as Needed but Try to Make the Code Speak for Itself
- Make it Easy to Repeat the Same Report on a New Population
- Reduce, Reuse, Recycle: Use Functions and Packages to Repeat Processes Efficiently
- Quality Assurance: Check Before Your Wreck
- 2.8 Different Audiences Need Different Things
- 2.9 Conclusion
- 3 The Answers Are Within
- 4 Transparency: Helping Others See What the Data Truly Reveal
- 4.1 The wide span of work and people involved
- 4.2 What transparency in education data means to me (Ellis)
- 4.3 Being transparent about the data
- 4.4 What are you representing and to whom?
- 4.5 Where could simple transparency metrics go wrong?
- 4.6 Explaining complex transparency measures
- 4.7 Conclusion
- 5 Data Governance
- 5.1 Let’s Talk About Data Governance
- 5.2 The Two Types of Problems in Data Governance
- 5.3 Three Data Governance Frames
- 5.4 A Data Governance Guide
- 5.5 Testing Data Governance with Drills
- 5.6 Conclusions and Other Resources
- 6 Early Warning Systems: A Consumer’s Guide
- 6.1 Introduction
- 6.2 Why Build an EWS?
- 6.3 EWS Prerequisites
- 6.4 Guiding development
- 6.5 The design process
- 6.6 Pilot and Rollout
- 6.7 Rollout
- 6.8 Documentation
- 6.9 Monitoring and Reporting
- 6.10 Future Work and Closing Thoughts
- 6.11 References and Further Reading
- 7 Reflections and Conclusions
- Acronyms and Acknowledgments
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