Michael Pyrcz: Associate Professor of The University of Texas at Austin in the Department of Petroleum and Geosystems Engineering.
With an assignment in the Bureau of Economic Geology, Jackson School of Geosciences. At The University of Texas at Austin, Michael teaches and supervises research on subsurface data analytics, geostatistics and machine learning. In addition, Michael accepted the role of Principal Investigator for the College of Natural Sciences, The University of Texas at Austin, freshman research initiative in energy data analytics. Before joining The University of Texas at Austin, Michael conducted and lead research on reservoir data analytics and modeling for 13 years with Chevron’s Energy Technology Company. He became an enterprise-wide subject matter expert, advising and mentoring on workflow development and best practice. Michael has written over 45 peer-reviewed publications, an open source Python data analytics package and a textbook with Oxford University Press. He is currently an associate editor with Computers and Geosciences, editorial board member for Mathematical Geosciences and the Program Chair for the Petroleum Data Driven Analytics Technical Section (PD²A) for the Society of Petroleum Engineers International. For more information go to www.michaelpyrcz.com, see his course lectures at http://y2u.be/j4dMnAPZu70, along with the demonstration numerical workflows at https://github.com/GeostatsGuy and contributions to outreach through social media at https://twitter.com/GeostatsGuy.
1a Data Analytics Reboot- Statistics Concepts.mp4; 1b Data Analytics Reboot- Spatial Sampling.mp4 1c Data Analytics Reboot- Subsurface Data Types.mp4
2a Data Analytics Reboot- Probability.mp4 2b Data Analytics Reboot- Frequentist Probability.mp4 2c Data Analytics Reboot- Bayesian Probability.mp4
2d Data Analytics Reboot- Joint, Marginal, Conditional Probability.mp4 2e Data Analytics Reboot- Bayesian Coin Demo.mp4
03Data AnalyticsUnivariate Distributions.mp4
04 Data Analytics- Univariate Statistics.mp4 04b Data Analytics Reboot- Statistical Expectation.mp4 04b Data Analytics- Statistical Expectation.mp4
05 Data Analytics- Parametric Distributions.mp4 05b Data Analytics- Monte Carlo Simulation.mp4 05c Data Analytics Distribution Transform.mp4
06 Data Analytics Spatial Heterogeneity.mp4
07 Data Analytics Confidence Intervals.mp4 07b Data Analytics Hypothesis Testing.mp4 07c Geostatistics Course Confidence Intervals and Hypothesis Testing in R.mp4 07d Data Analytics Hypothesis Testing Take II.mp4
08 Data Analytics Correlation.mp4 08b Data Analytics Bootstrap.mp4
09 Data Analytics- Q-Q & P-P Plots.mp4 09b Data Analytics- Linear Regression.mp4 9c Data Analytics Reboot- Spatial Bias.mp4 9c Data Analytics- Spatial Bias.mp4
9d Data Analytics Reboot- Spatial Declustering.mp4 9dExcel Data Analytics Reboot- Spatial Declustering.mp4 9dPython Data Analytics Reboot- Spatial Declustering.mp4
9e Data Analytics Reboot- Spatial Debiasing.mp4 9eExcel Data Analytics Reboot- Spatial Debiasing.mp4
10 Data Analytics- Spatiotemporal Stationarity.mp4 10b Data Analytics- Spatial Continuity.mp4
10c Data Analytics- Variogram Introduction.mp4 10d Data Analytics- Variogram Calculation.mp4
11 Data Analytics- Variogram Interpretation.mp4 11b Data Analytics- Variogram Modeling.mp4
12 Data Analytics Trend Modeling.mp4 12b Geostatistics Course- Kriging.mp4
13 Data Analytics- Simulation.mp4
14 Data Analytics- Indicator Methods.mp4
15 Data Analytics- Facies Modeling.mp4
16 Data Analytics- Cosimulation.mp4
18 Geostatistics Course- Machine Learning.mp4
19 Data Analytics- Principal Component Analysis.mp4
20 Data Analytics- Decision Tree.mp4
21 Data Analytics- Course Conclusion.mp4
