cortizbon / pythonnumericaldemos

Well-documented Python demonstrations for spatial data analytics, geostatistical and machine learning to support my courses.

PythonNumericalDemos: Educational Data Science Demonstrations Repository (1.0.0)

Professor Michael Pyrcz, The University of Texas at Austin

A collection of Python demos for spatial data analytics, geostatistics and machine learning methods.

Please cite as:

Pyrcz, Michael J. (2021). PythonNumericalDemos: Educational Data Science Demonstrations Repository (1.0.0). Zenodo. https://doi.org/10.5281/zenodo.5564991

I use these demonstrations for:

1. in-lecture walkthroughs
2. hands-on experiential learning with specific tasks suggested in the accompanying lecture notes
3. example workflows to support my students

Other resources that you might be interested in include:

• R workflow repository
• Excel workflow repository
• GeostatsPy package

While I have more than 20 years experience coding in FORTRAN, C++ and VBA, I just started with Python a couple of years ago. I love it. I code less and get more done! I recommend Python to any engineers or scientists. Here's some reasons to learn to code:

1. Transparency – no compiler accepts hand waiving! Coding forces your logic to be uncovered for any other scientist or engineer to review.

2. Reproducibility – run it, get an answer, hand it over, run it, get the same answer. This is a main principle of the scientific method.

3. Quantification – programs need numbers. Feed the program and discover new ways to look at the world.

4. Open-source – leverage a world of brilliance. Check out packages, snippets and be amazed with what great minds have freely shared.

5. Break Down Barriers – don’t throw it over the fence. Sit at the table with the developers and share more of your subject matter expertise for a better product.

6. Deployment – share it with others and multiply the impact. Performance metrics or altruism, your good work benefits many others.

7. Efficiency – minimize the boring parts of the job. Build a suite of scripts for automation of common tasks and spend more time doing science and engineering!

8. Always Time to Do it Again! – how many times did you only do it once? It probably takes 2-4 times as long to script and automate a workflow. Usually worth it.

9. Be Like Us – it will change you. Users feel limited, programmers truly harness the power of their applications and hardware.

Alright, that is enough of my ranting. Especially, since I haven't even introduced myself yet!

The Author:

Michael Pyrcz, Associate Professor, 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 [email protected].

I'm always happy to discuss,

Michael

Michael Pyrcz, Ph.D., P.Eng. Associate Professor The Hildebrand Department of Petroleum and Geosystems Engineering, Bureau of Economic Geology, The Jackson School of Geosciences, The University of Texas at Austin

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