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Introduction to Data Analytics, Geostatistics and Machine Learning by Michael pyrcz from UT Austin

作者: 发布者:Li Yong 责任编辑: 发布时间:2020-04-14 浏览次数:46

 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

课程中所用数据.docx