. Introduction to petroleum data analytics and computations; use of pre-built computational functions and packages for purposes of interpolation, gradient approximation, calculation of area under the curve, vector and matrix manipulation, and solving ordinary differential equations relevant to petroleum engineering; use of pre-built statistical functions and packages to solve petroleum engineering problems; exploratory data analysis and data preprocessing on large petroleum engineering and geophysical datasets; big-data visualization to generate insights and discover relationships; regression, classification, and clustering relevant to petroleum engineering; neural networks for regression and classification on petroleum engineering data; basic evaluation of data-driven models and basic computations using equations specific to petroleum engineering. Credits 2. 1 Lecture Hour. 3 Lab Hours.
. Introduction to petroleum data analytics and computations; use of pre-built computational functions and packages for purposes of interpolation, gradient approximation, calculation of area under the curve, vector and matrix manipulation, and solving ordinary differential equations relevant to petroleum engineering; use of pre-built statistical functions and packages to solve petroleum engineering problems; exploratory data analysis and data preprocessing on large petroleum engineering and geophysical datasets; big-data visualization to generate insights and discover relationships; regression, classification, and clustering relevant to petroleum engineering; neural networks for regression and classification on petroleum engineering data; basic evaluation of data-driven models and basic computations using equations specific to petroleum engineering. Credits 2. 1 Lecture Hour. 3 Lab Hours.
