Module 1 – Overview

What is Data Science – Overview of the course, and an outline of the scope of data science.
Data Science for E&P – Addressing the role of data science in E&P and an example application to log data quality control and reconstruction using machine learning.
Module 2 – Data Science Toolkit – Notebooks, Visualization, and Communication

Overview of the data science toolkit.
Hands-on workshop introducing the toolkit and getting started with Python scripts and notebooks.
Overview of how to manage and use Python packages.
Hands-on workshop on Python packages covering how to install and manage packages, and how to use packages from your Python notebooks
Overview of data visualization with SandDance.
Hands-on workshop introducing SandDance for interactive data visualization using a dataset of offshore wells from the UK Continental Shelf.
Overview of Markdown – a lightweight markup language for adding simple formatting to plain text documents, and documenting Python notebooks.
Hands-on workshop on Markdown for formatting text documents and annotating Python notebooks.
Module 3 – Python Fundamentals

Python 101 – Introduction to Python fundamentals including variables, types, statements, expressions, control flow, and functions.
Hands-on workshop on Python 101.
Python 102 – More Python fundamentals including data structures, modules, files and folders, JavaScript Object Notation (JSON), serialization.
Hands-on workshop on Python 102.
Module 4 – Computational Thinking

Introduction to Computational Thinking – the analytical and logical processes of decomposing a complex task and expressing it in a form that can be performed by a computer.
Hands-on workshop on Computational Thinking applied to the design and implementation an interactive base map for UK E&P data.
Module 5 – Exploratory Data Analysis

Exploratory Data Analysis – Introduction to the Exploratory Data Analysis process and key Python packages: pandas for data analysis and plotly for statistical graphics.
Hands-on workshop on exploratory data analysis – reading data into pandas data frames, handling dates, merging datasets, creating statistical graphics figures with plotly, exporting figures.
Statistical Graphics – Demonstration of a gallery of statistical graphics samples
Descriptive Statistics – Introduction to univariate and multivariate statistics.
Module 6 – Exploring E&P Data

Well header data – Introduction to handling well header data (surface location and attributes) using the pandas and plotly packages.
Hands-on workshop on well header data – including import, data cleaning, date handling, posting well data on cultural/satellite base map and visualizing historical trends.
Production data – Introduction to handling field production data using the pandas and plotly packages.
Hands-on workshop on field production data – including import, data cleaning, date handling, queries, visualizing hierarchical and time series data.
Well log data – Introduction to handling wireline logs from LAS files using the lasio, pandas, and plotly packages.
Hands-on workshop on well log and tops data – including LAS (.las) file import, merging tops, and data visualization.
Seismic data – Introduction to handling seismic SEG-Y data using the segyio, and plotly packages.
Hands-on workshop on seismic data – including SEG-Y (.segy) file import, extracting binary and trace headers, visualizing seismic trace data, and calculating seismic attributes
Module 7 – Geospatial Data

Coordinate reference systems – Introduction to the geographic and projected coordinate systems, defining a coordinate reference system from EPSG codes, offsets between coordinate reference systems, and transforming positions between reference systems.
Hands-on workshop on coordinate reference systems.
Module 8 – Machine Learning Fundamentals

Machine Learning – introduction to the fundamentals of machine learning including background concepts from probability theory, the different types of machine learning, and the basic workflow to build and evaluate models from data.
Python scikit-learn – introduction to the Python scikit-learn package for machine learning, including a demonstration of typical pipelines and workflows.
Supervised learning with regression – introduction to regression including traditional linear regression, random forest regression, hyperparameter optimization, and performance evaluation.
Hands-on workshop on regression for reconstructing wireline logs using random forests.
Unsupervised Learning – introduction to unsupervised learning for dimensionality reduction, clustering and outlier detection.
Hands-on workshop on unsupervised learning for outlier detection and clustering of wireline logs.
Explainable Machine Learning – introduction to explainable machine learning: techniques for looking inside the so-called black box models of machine learning to understand why particular predictions are made and which variables are important.