ExcelNumericalDemos: Educational Data Science Excel Demonstrations Repository (0.0.1)

Interactive dashboards to help you over the intellectual hurdles of data science!

If you can't explain it simply, you don't understand it well enough - Alberta Einstein

To reach more students and working professionals with my Data Analytics and Geostatistics, Spatial Data Analytics and Machine Learning courses, I have developed a set of Excel interactive dashboards. When students struggle with a concept I make a new interactive dashboard so they can learn by playing with the statistics, models or theoretical concepts, and virtually everyone has Excel!

Michael Pyrcz, Professor, The University of Texas at Austin, Data Analytics, Geostatistics and Machine Learning

Twitter | GitHub | Website | GoogleScholar | Book | YouTube | LinkedIn

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Cite As:

Pyrcz, Michael J. (2021). ExcelNumericalDemos: Educational Data Science Excel Demonstrations Repository (0.0.1). Zenodo. https://zenodo.org/doi/10.5281/zenodo.5564991

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Setup

A minimum environment includes:

• Microsoft Excel > 2010 - note, VBA is not required

Datasets are embedded in the Excel files.

Repository Summary

To me 'coding up' or 'building out' a method or workflow in Excel without VBA is the ultimate case of explaining it simply! So while I do code in FORTRAN, C++ (20 years experience), VBA, R and Python, I challenge myselt to put methods and workflows in Excel to provide hands-on experiential learning that reaches more students. Why do I feel this way?

• Assessibility - in STEM everyone has access to Excel. This is even more true with the online applications Microsoft now provides and the vast majority of scientists and engineers know the basics of working with Excel
• Interpretability - one can easily interogate a method or workflow in Excel, just click on the cell to see the equation
• Set Up - there is no set up needed to get students started with these demonstrations

I teach in a lot of places and I teach a lot of things. I adjust to get the job done. Now, if you are convinced that I'm old fashion, check out my:

• R workflow repository
• Python workflow repository
• GeostatsPy package

The Author:

Michael Pyrcz, Professor, The University of Texas at Austin

Novel Data Analytics, Geostatistics and Machine Learning Subsurface Solutions

With over 17 years of experience in subsurface consulting, research and development, Michael has returned to academia driven by his passion for teaching and enthusiasm for enhancing engineers' and geoscientists' impact in subsurface resource development.

For more about Michael check out these links:

Twitter | GitHub | Website | GoogleScholar | Book | YouTube | LinkedIn

Want to Work Together?

I hope this content is helpful to those that want to learn more about subsurface modeling, data analytics and machine learning. Students and working professionals are welcome to participate.

• Want to invite me to visit your company for training, mentoring, project review, workflow design and / or consulting? I'd be happy to drop by and work with you!

• Interested in partnering, supporting my graduate student research or my Subsurface Data Analytics and Machine Learning consortium (co-PIs including Profs. Foster, Torres-Verdin and van Oort)? My research combines data analytics, stochastic modeling and machine learning theory with practice to develop novel methods and workflows to add value. We are solving challenging subsurface problems!

• I can be reached at mpyrcz@austin.utexas.edu.

I'm always happy to discuss,

Michael

Michael Pyrcz, Ph.D., P.Eng. Professor, Cockrell School of Engineering and The Jackson School of Geosciences, The University of Texas at Austin

More Resources Available at: Twitter | GitHub | Website | GoogleScholar | Book | YouTube | LinkedIn