https://github.com/GeostatsGuy/DataScienceInteractivePython

Python interactive dashboards for learning data science

To support my students in my Data Analytics and Geostatistics, Spatial Data Analytics and Machine Learning courses and anyone else learning data analytics and machine learning, I have developed a set of Python 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!

---

Pyrcz, Michael J. (2021). DataScienceInteractivePython: Educational Data Science Interactive Python Dashboards Repository (0.0.1). Zenodo. https://doi.org/10.5281/zenodo.5564966

---

To further support my students, I'm using Binder to host some of my interactive Python spatial data analytics, geostatistics and machine learning demonstration workflows online. Some of my students are having issues with setting up their local computing environments and instantiating the interactive workflows.

• I hope this will assist these students and remove barriers for these educational tools to invite a wider audience that may benefit from experiential learning - playing with the systems and machines in real-time.

Click on the link above to launch binder with container to run the included workflow.

A minimum environment includes:

• Python 3.7.10 - due to the depdendency of GeostatsPy on the Numba package for code acceleration
• MatPlotLib - plotting
• NumPy - gridded data and array math
• Pandas - tabulated data
• SciPy - statistics module
• ipywidgets - for plot interactivity
• GeostatsPy - geostatistical algorithms and functions (Pyrcz et al., 2021)

The required datasets are available in the GeoDataSets repository and linked in the workflows.

The interative Python examples include a variety of topics like:

• Bayesian and frequentist statistics
• univariate and bivariate statistics
• confidence intervals and hypothesis testing
• Monte Carlo methods and bootstrap
• inferential machine learning, principal component and cluster analysis
• predictive machine learning, norms, model parameter training and hyperparameter tuning, overfit models
• uncertainty modeling checking
• spatial data debiasing
• variogram calculation and modeling
• spatial estimation, issues and trend modeling
• spatial simulation and summarization over realizations
• decision making in the presence of uncertainty

If you want to see all my shared educational content check out:

• Resources Inventory
• GeostatsGuy Lectures

I hope this is helpful to anyone interested to learn about spatial data analytics, geostatistics and machine learning. I'm all about remoing barriers to education and encouraging folks to learn coding and data-driven modeling!

Sincerely,

Michael

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:

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