Applied Statistical Modeling and Big Data Analytics

Three-day course on using statistical modeling and data analytics for insights from big data, including hands-on problem-solving. (Course materials provided)


Course Description (PDF)

Course Outline

01. Foundational Concepts

Big data analytics, machine learning and artificial intelligence concepts; Data, statistics, and probability; Distributions; Confidence intervals

02. Basic Regression Analysis

Linear regression; Understanding regression statistics; Non-parametric regression

03. Multivariate Statistics 

Dimension reduction; Cluster analysis; Data visualization

04. Machine Learning Basics

Overview of techniques; Evaluating model performance; Variable importance; Model aggregation

05. Machine Learning for Regression and Classification

Classification/regression trees; Random forest; Gradient boosting machine; Support vector machine; Neural networks; Deep learning

06. Miscellaneous Topics and Wrap-Up

Experimental design and response surface analysis; Uncertainty quantification; Selected literature review; Key takeaways and resources; Data analytics dos and don’ts

Who Should Attend?

This course is designed for Petroleum Engineers, Geoscientists and other disciplines interested in becoming informed users of Statistical Modeling and Data analytics in the E&P realm, and efficiently interacting with Data Scientists to develop practical data-driven applications for their projects.

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