Oil & Gas Training
and Competency Development
Competency Management system SLB NEXT

Fundamentals of Data Analytics - (Remote Instructor-Led Series)

This is a virtual course covering the Fundamentals of Data Analytics agenda, which introduces the data analytics techniques to extract knowledge from raw data. The course aims to educate the class audience on how to create data-driven models through the data mining pipeline that consists of data exploration, data preprocessing, machine learning modeling, and model evaluation. The course combines theoretical knowledge with hands-on training of the data analytics techniques. After taking this course, the participants should be able to build and evaluate data-driven models via the machine learning approach.
This is a practical course with 50% of the time dedicated to hands-on sessions using the Data Science profile in Schlumberger's DELFI Cognitive E&P environment and / or Orange Open Source Data Mining Software suite which is based on visual programming. Hands-on sessions will be based on oil and gas related datasets.

This course will be delivered entirely online, over a total of 20 hours (five 4-hour sessions over 5 days).

Day 1

Session 1 - 4 hours

  • Introduction to Data Analytics
  • Exploratory Data Analysis (EDA) (Visualization and Descriptive Statistics)
  • Hands-on EDA

On the first day of the course the participants will be able to get a bird-eye view of the data analytics process. They will explore one of the four data analytics modules called exploratory data analysis, a necessary step to get the feel of the data. Learning will be reinforced via hands-on training using the DELFI Data Science Profile and / or Orange.

Day 2

Session 2 - 4 hours

Data Preprocessing

  • Data Preprocessing (including Principal Compenant Analysis)
  • Hands-on Data Preprocessing

Supervised Machine Learning

  • Decision Tree
  • Hands-on Decision Tree

The second day will start with data preprocessing, a necessary step to clean and format the data before building machine learning models. Later in the day, supervised machine learning concepts will be introduced, and participants will be able to build data-driven models via a supervised machine learning algorithm called Decision Tree. Learning will be reinforced via hands-on training using the DELFI Data Science Profile and / or Orange.

Day 3

Session 3 - 4 hours

Supervised Machine Learning

  • Model Evaluation
  • Hands-in Model Evaluation
  • Regression (Linear and Logistics)

On the third day of the course, the participants will learn about model evaluation matrices (e.g., confusion matrix, ROC, AUC, etc.) and model evaluation methods (e.g., 10-fold cross validation) will also be discussed. Later in the day, regression analysis, particularly linear regression and logistic regression will be introduced. Learning will be reinforced via hands-on training using the DELFI Data Science Profile and / or Orange.

Day 4

Session 4 - 4 hours

Ensemble Methods

  • Hands-on Regression
  • Ensemble Methods (Bagging, Boosting and Random Forest)
  • Hands-on Ensemble Methods

On the fourth day of the course, participants will complete a hands-on session on regression analysis, after which they will be introduced to more advanced machine learning techniques of ensemble methods. They will be able to implement the complete data mining pipeline including model building and model evaluation using the DELFI Data Science Profile and / Orange.

Day 5

Session 5 - 4 hours

Unsupervised Machine Learning

  • Cluster Analysis (k-Means and Hierarchical)
  • Hands-on Cluster Analysis
  • Class feedback and wrap-up

On the last day of the course, the participants will be introduced to unsupervised machine learning algorithms. The course will end with a hands-on session on clustering using the DELFI Data Science Profile or Orange.

Learning activity mix

Geoscientists, Engineers, IT professionals and aspiring Citizen Data Scientists working in the oil and gas industry
who want to get introduced to data analytics techniques for building data-driven models.

  • Introduction to Data Analytics
  • Exploratory Data Analysis (EDA)
  • Data Preprocessing (including Principal Component Analysis)
  • Supervised Machine Learning - Decision Tree, Model Evaluation and Regression
  • Ensemble Methods
  • Unsupervised Machine Learning - Cluster Analysis

No prior experience of data analysis is required, although basic math and statistics knowledge is useful.

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