The data science of educational management and policy, with barton poulson videos

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I love art. I believe that practicing art makes for a better data science practice. But mostly I love it because it’s art: it’s beautiful, rewarding, expressive, and fun. I have deep connections to art: I studied design for most of my undergraduate career; I’ve been involved with modern dance for years; I love poetry; I’m a regular patron of the opera; I play the saxophone – of which Gioachino Rossini exclaimed “This is the most beautiful sound I have ever heard!” – and I have a long-term plan for computer-assisted composition and live looping in the indie classical genre.

But it was only a few years ago, when I was on a sabbatical to learn more about data visualization, that I (re)discovered the connection between my data work and my aesthetic interests. I learned about “creative coding” and how to use visualization tools – Processing, in particular – to create art. I also learned how to use Max/MSP/Jitter to create music and, at the same time, capture, analyze, and transform video. These developments led to a surprising turn for a data person: showings in two galleries, a commission for a modern dance performance – in partnership with choreographer Jacque Bell (and my wife) – and two years at Utah Valley University working with a Presidential Fellowship to devise methods for live video looping in modern dance. And, through it all, I have maintained an abiding interest in using the lessons learned to enrich my data practice and teaching.

Learn how data science changes the way that education is planned, carried out, and evaluated.Read more.

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  • The data science of educational management and policy, with barton poulson videos
    Data Science Courses

Learn how data science changes the way that education is planned, carried out, and evaluated.

Introduction

  • Data science for education management and policy
1. Applying Data Science
  • Data science and education
  • Curriculum planning
  • Online education
  • Data science and exceptional students
  • Standardized and adaptive testing
  • Retention and attrition pipeline
  • Educational evaluation
  • Ethical challenges in educational data
  • Careers for data scientists in education
Conclusion

4.7 rating at LinkedIn Learning based on 30 ratings

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