PythonNumericalDemos: Educational Data Science Python Demonstrations Repository (0.0.1)

Well documented Python workflows to help you over the intellectual hurdles of 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 well-documented Python workflows. When students struggle with a concept I make and share a new well-documented so they can try out and repeat all the examples from my lectures to learn immediately how to apply the models or theoretical concepts!

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

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

Cite As:

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

Setup

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.

Repository Summary

I use these demonstrations for:

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

Other resources that you might be interested in include:

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:

Transparency – no compiler accepts hand waiving! Coding forces your logic to be uncovered for any other scientist or engineer to review.
Reproducibility – run it, get an answer, hand it over, run it, get the same answer. This is a main principle of the scientific method.
Quantification – programs need numbers. Feed the program and discover new ways to look at the world.
Open-source – leverage a world of brilliance. Check out packages, snippets and be amazed with what great minds have freely shared.
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.
Deployment – share it with others and multiply the impact. Performance metrics or altruism, your good work benefits many others.
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!
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.
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 Repository 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

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PythonNumericalDemos: Educational Data Science Python Demonstrations Repository (0.0.1)

Well documented Python workflows to help you over the intellectual hurdles of data science!

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

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

Cite As:

Setup

Repository Summary

The Repository Author:

Michael Pyrcz, Professor, The University of Texas at Austin

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

Want to Work Together?

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

About

Releases 1

Packages

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Name		Name	Last commit message	Last commit date
Latest commit History 605 Commits
About_Michael_Pyrcz.pdf		About_Michael_Pyrcz.pdf
Binomial_Demo_Environmental.xlsx		Binomial_Demo_Environmental.xlsx
Bootstrap.ipynb		Bootstrap.ipynb
CITATION.cff		CITATION.cff
Confidence_Interval_BySteps.ipynb		Confidence_Interval_BySteps.ipynb
Convolution_Simulation_Demo.ipynb		Convolution_Simulation_Demo.ipynb
DT_Demo.ipynb		DT_Demo.ipynb
Experiential_Bootstrap_MCS.ipynb		Experiential_Bootstrap_MCS.ipynb
Experiential_DecisionTree.ipynb		Experiential_DecisionTree.ipynb
Interactive_Bayesian_Linear_Regression.ipynb		Interactive_Bayesian_Linear_Regression.ipynb
Interactive_Bayesian_Updating.ipynb		Interactive_Bayesian_Updating.ipynb
Interactive_Bootstrap.ipynb		Interactive_Bootstrap.ipynb
Interactive_Bootstrap_CowbowHat.ipynb		Interactive_Bootstrap_CowbowHat.ipynb
Interactive_Central_Limit_Theorem.ipynb		Interactive_Central_Limit_Theorem.ipynb
Interactive_Confidence_Interval.ipynb		Interactive_Confidence_Interval.ipynb
Interactive_Convolution_kNearest.ipynb		Interactive_Convolution_kNearest.ipynb
Interactive_Correlation_Coefficient.ipynb		Interactive_Correlation_Coefficient.ipynb
Interactive_Correlation_Coefficient_Issues.ipynb		Interactive_Correlation_Coefficient_Issues.ipynb
Interactive_DBSCAN.ipynb		Interactive_DBSCAN.ipynb
Interactive_DecisionMaking.ipynb		Interactive_DecisionMaking.ipynb
Interactive_Decision_Tree.ipynb		Interactive_Decision_Tree.ipynb
Interactive_Declustering.ipynb		Interactive_Declustering.ipynb
Interactive_Distribution_Transformations.ipynb		Interactive_Distribution_Transformations.ipynb
Interactive_Gibbs_Sampler.ipynb		Interactive_Gibbs_Sampler.ipynb
Interactive_Hypothesis_Testing.ipynb		Interactive_Hypothesis_Testing.ipynb
Interactive_LASSO_Regression.ipynb		Interactive_LASSO_Regression.ipynb
Interactive_Linear_Solutions.ipynb		Interactive_Linear_Solutions.ipynb
Interactive_MarginalJointConditional.ipynb		Interactive_MarginalJointConditional.ipynb
Interactive_Model_Fitting.ipynb		Interactive_Model_Fitting.ipynb
Interactive_Monte_Carlo_Methods.ipynb		Interactive_Monte_Carlo_Methods.ipynb
Interactive_Monte_Carlo_Simulation.ipynb		Interactive_Monte_Carlo_Simulation.ipynb
Interactive_NeuralNetwork_SingleLayer.ipynb		Interactive_NeuralNetwork_SingleLayer.ipynb
Interactive_Norms.ipynb		Interactive_Norms.ipynb
Interactive_Optimization.ipynb		Interactive_Optimization.ipynb
Interactive_Overfit.ipynb		Interactive_Overfit.ipynb
Interactive_PCA.ipynb		Interactive_PCA.ipynb
Interactive_PCA_Eigen.ipynb		Interactive_PCA_Eigen.ipynb
Interactive_PP_Plot.ipynb		Interactive_PP_Plot.ipynb
Interactive_ParametricDistributions.ipynb		Interactive_ParametricDistributions.ipynb
Interactive_Polynomial_Solution.ipynb		Interactive_Polynomial_Solution.ipynb
Interactive_PreDrill_Prediction.ipynb		Interactive_PreDrill_Prediction.ipynb
Interactive_QQ_Plot.ipynb		Interactive_QQ_Plot.ipynb
Interactive_RadialBasisFunctions.ipynb		Interactive_RadialBasisFunctions.ipynb
Interactive_Ridge_Regresion.ipynb		Interactive_Ridge_Regresion.ipynb
Interactive_Sampling_Methods.ipynb		Interactive_Sampling_Methods.ipynb
Interactive_Shapley_Values.ipynb		Interactive_Shapley_Values.ipynb
Interactive_Simple_Kriging.ipynb		Interactive_Simple_Kriging.ipynb
Interactive_Simple_Kriging_Behavoir.ipynb		Interactive_Simple_Kriging_Behavoir.ipynb
Interactive_Simulation.ipynb		Interactive_Simulation.ipynb
Interactive_Sivia_Coin_Toss.ipynb		Interactive_Sivia_Coin_Toss.ipynb
Interactive_Spatial_Aggregate_Uncertainty.ipynb		Interactive_Spatial_Aggregate_Uncertainty.ipynb
Interactive_Spectral_Clustering.ipynb		Interactive_Spectral_Clustering.ipynb
Interactive_Spurious_Correlations.ipynb		Interactive_Spurious_Correlations.ipynb
Interactive_String_Effect.ipynb		Interactive_String_Effect.ipynb
Interactive_Trend.ipynb		Interactive_Trend.ipynb
Interactive_Uncertainty_Checking.ipynb		Interactive_Uncertainty_Checking.ipynb
Interactive_Variogram_Calculation.ipynb		Interactive_Variogram_Calculation.ipynb
Interactive_Variogram_Calculation_Modeling.ipynb		Interactive_Variogram_Calculation_Modeling.ipynb
Interactive_Variogram_Calculation_Modeling_Krige.ipynb		Interactive_Variogram_Calculation_Modeling_Krige.ipynb
Interactive_Variogram_Modeling.ipynb		Interactive_Variogram_Modeling.ipynb
Interactive_Variogram_Nugget_Effect.ipynb		Interactive_Variogram_Nugget_Effect.ipynb
Interactive_Variogram_h_scatter.ipynb		Interactive_Variogram_h_scatter.ipynb
Interactive_kMeans_Clustering.ipynb		Interactive_kMeans_Clustering.ipynb
LICENSE		LICENSE
LU+Decomposition.ipynb		LU+Decomposition.ipynb
Lorenz_coefficient_demo.ipynb		Lorenz_coefficient_demo.ipynb
Monte_Carlo_simulation.ipynb		Monte_Carlo_simulation.ipynb
NSCORE.ipynb		NSCORE.ipynb
PCA_Demo.ipynb		PCA_Demo.ipynb
Polynomial_Model_Components.ipynb		Polynomial_Model_Components.ipynb
PyGSLIB_declus_python_demo.ipynb		PyGSLIB_declus_python_demo.ipynb
PyGSLIB_variogram_python_demo.ipynb		PyGSLIB_variogram_python_demo.ipynb
PythonDataBasic_Measures_of_Central_Tendency.ipynb		PythonDataBasic_Measures_of_Central_Tendency.ipynb
PythonDataBasics_Bivariate_Statistics.ipynb		PythonDataBasics_Bivariate_Statistics.ipynb
PythonDataBasics_Bivariate_Visualization.ipynb		PythonDataBasics_Bivariate_Visualization.ipynb
PythonDataBasics_BoostrapHypothesis.ipynb		PythonDataBasics_BoostrapHypothesis.ipynb
PythonDataBasics_Bootstrap.ipynb		PythonDataBasics_Bootstrap.ipynb
PythonDataBasics_BootstrapConfidence.ipynb		PythonDataBasics_BootstrapConfidence.ipynb
PythonDataBasics_ConfidenceInterval_HypothesisTesting.ipynb		PythonDataBasics_ConfidenceInterval_HypothesisTesting.ipynb
PythonDataBasics_DataFrame.ipynb		PythonDataBasics_DataFrame.ipynb
PythonDataBasics_Distribution_Transformations.ipynb		PythonDataBasics_Distribution_Transformations.ipynb
PythonDataBasics_DistributionsTransformations.ipynb		PythonDataBasics_DistributionsTransformations.ipynb
PythonDataBasics_Expectation.ipynb		PythonDataBasics_Expectation.ipynb
PythonDataBasics_Feature_Ranking.ipynb		PythonDataBasics_Feature_Ranking.ipynb
PythonDataBasics_Heterogeneity_Metrics.ipynb		PythonDataBasics_Heterogeneity_Metrics.ipynb
PythonDataBasics_Hypothesis.ipynb		PythonDataBasics_Hypothesis.ipynb
PythonDataBasics_HypothesisTesting.ipynb		PythonDataBasics_HypothesisTesting.ipynb
PythonDataBasics_LinearRegression.ipynb		PythonDataBasics_LinearRegression.ipynb
PythonDataBasics_Lorenz_Coefficient.ipynb		PythonDataBasics_Lorenz_Coefficient.ipynb
PythonDataBasics_MachineLearning.ipynb		PythonDataBasics_MachineLearning.ipynb
PythonDataBasics_Measures_of_Central_Tendency.ipynb		PythonDataBasics_Measures_of_Central_Tendency.ipynb
PythonDataBasics_ParametricDistributions.ipynb		PythonDataBasics_ParametricDistributions.ipynb
PythonDataBasics_ParametricDistributions_Fitting.ipynb		PythonDataBasics_ParametricDistributions_Fitting.ipynb
PythonDataBasics_PedictiveMachineLearning.ipynb		PythonDataBasics_PedictiveMachineLearning.ipynb
PythonDataBasics_Pipelines.ipynb		PythonDataBasics_Pipelines.ipynb
PythonDataBasics_QQ_Plot.ipynb		PythonDataBasics_QQ_Plot.ipynb
PythonDataBasics_Searching.ipynb		PythonDataBasics_Searching.ipynb
PythonDataBasics_SpatialBootstrap.ipynb		PythonDataBasics_SpatialBootstrap.ipynb
PythonDataBasics_Statistics.ipynb		PythonDataBasics_Statistics.ipynb
PythonDataBasics_Univariate_Statistics.ipynb		PythonDataBasics_Univariate_Statistics.ipynb

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GeostatsGuy/PythonNumericalDemos

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PythonNumericalDemos: Educational Data Science Python Demonstrations Repository (0.0.1)

Well documented Python workflows to help you over the intellectual hurdles of data science!

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

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

Cite As:

Setup

Repository Summary

The Repository Author:

Michael Pyrcz, Professor, The University of Texas at Austin

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

Want to Work Together?

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

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