• July 20, 2020 - July 24, 2020
    8:00 am - 5:00 pm

Course Length:        5 Days               Course Dates:         July 20 – 24, 2020                      Course Venue:         Calgary, Alberta, Canada

Course Description

Every day, careful analysis of data is leading to new insights, discoveries, and opportunities. Consider, for example, how companies like Amazon, Facebook, and Netflix use “big data” to generate recommendations that are made to millions of people each day. Yet while the potential benefits of more and more data are obvious, the increasing flood of data also presents challenges, with some companies describing themselves as “drowning in data.” However, it is not just IT companies that are “drowned in data”. Data analytics is equally of importance to oil and gas industry; think of large volume of data coming from sensors, equipment, and devices, alarm data, process and production data etc. This course will equip you with the tools that are needed for efficient and effective data crunching and analysis.

Who Should Attend?

Professional reservoir engineers, production engineers, petrophysicists, geophysicists, geologists, oil and gas data analysts, and asset managers interested in learning how to use R tools for data science and data analysis.

What You Will Learn

By the end of this course, participants should be able to:

  • Understand how to use the R packages to write programs, access the various data analytic tools, and document and automate analytics processes.
  • Demonstrate understanding of some of the most powerful and popular R libraries and packages for data analytic and visualization
  • Demonstrate a working knowledge of the R tools ideally suited for data analytic tasks, including:
  • Accessing data (e.g., text files, time series, databases), cleaning and normalizing data
  • Exploring data (e.g., simple statistics, correlation matrices, visualization)
  • Modeling data (e.g., statistics, network analysis using igraph, machine learning)

Course Outline:

  • Introduction and setting up integrated analysis environment
  • Accessing data – data understanding in order to rapidly evaluate data of interest
  • Preparing data – data preprocessing/wrangling in order to get an informative, manageable dataset
  • Numerical analysis – prediction based on statistical tools such as regression, classification, and clustering
  • Interpretation of results through visualization and careful evaluation