Course overview
This course introduces the opportunities, challenges and current development of data analytics applications in oil and gas industry. The theory and fundamental equations, as well as understanding data driven methods are covered. Practical methods, real field examples will equip students to apply data analytics and machine learning methods in petroleum engineering. The course covers the following topics with specific applications in resources engineering: Overview of Data Analytic, Introduction to Programming in Python, Univariate and Multivariate Descriptive Statistics, Supervised Machine Learning, and clustering.
- Introduction & Python Fundamentals
- Supervised Learning
- Regression & Unsupervised Learning
Course learning outcomes
- Use Python basic commands and deal with specialty data types
- Apply Python Machine Learning Packages in resources engineering applications
- Describe the fundamentals of Descriptive and Predictive Analytics
- Learn how to perform regression, data clustering, feature extraction and classification
- Learn how to use basic artificial neural networks
- Choose the most appropriate ML and DA model
Degree list
The following degrees include this course