The digital learning ecosystem An efficient management approach to capability development, delivering smarter teams, improved productivity and better business outcome for the managers.
The digital learning ecosystem Digitally and seamlessly connecting you, the learner, with pertinent learning objects and related technologies ensuring systematic, engaging and continued learning.
Industry and client recognition
Best Outreach Program Finalist: WorldOil Awards
Overall Customer Satisfaction Score
Training provider of the year: 2013, 14 and 15
Upstream learning simulator With more than 50,000 participants instructed in various disciplines, data driven OilSim runs real-world oil and gas business scenarios and technical challenges.Engaging. Educational. Enjoyable
Develop measurable skills and capabilities
This three-day course introduces the data analytics techniques to extract knowledge from raw data. The course aims to educate 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 R programming language. Hands-on session will be based on oil and gas related datasets.
Exploratory Data Analysis and Data Preprocessing
Objective: 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 two of the four data analytics modules called exploratory data analysis, a necessary step to get the feel of the data, and data preprocessing, a necessary step to clean and format the data before building machine learning models. Learning will be reinforced via hands-on training in R.Day 2
Supervised Machine Learning
Objective: On the second day of the course, the participants will be able to build data-driven models via supervised machine learning algorithms. Two representative machine learning algorithms - one for classification and the other for regression - will be covered. Model evaluation matrices (e.g., confusion matrix, ROC, AUC, etc.) and model evaluation methods (e.g., 10-fold cross validation) will be discussed towards the end of the day.Day 3
Ensemble Methods and Unsupervised Machine Learning
Objective: On the third day of the course, the participants will be introduced to more advanced machine learning techniques of ensemble methods and unsupervised machine learning algorithms. They will be able to implement the complete data mining pipeline including model building and model evaluation in R.
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.
Customize your own learning journey and track your progress when you start using a defined learning path.