Data science and analytics are two hot buzzwords that are currently trending in the oil and gas industry.
There are also some people with engineering degrees and oilpatch experience that are switching or considering switching their careers to become data scientists because of the higher demand and usually pretty good salaries.
In this article, we will take a look at what are data science and analytics as well as the skills required to work in these fields.
How Data Science and Analytics are Used in the Oil and Gas Industry?
With a collapse of oil and gas prices in 2014, there is a visible trend in the oil and gas industry toward lowering expenses as much as possible to increase revenue.
This often involves improving efficiencies and automating manual processes.
Some examples are automating drilling/intervention/completion operations and using computers to analyze production/reservoir/equipment maintenance data.
In addition data analytics help to focus on key performance indicators (KPIs) and structure operations in a way that helps to improve output.
It usually takes longer for new technologies and ideas to enter the oil and gas market in comparison to other industries because many people who are working in the oilpatch for 20-30 years are hesitant to try something new.
I can’t count how many times I had to help some of the older members of the team with basic computer tasks like creating Excel tables, PDFs, or even attaching files to emails.
However, things are changing and people in the industry are starting to see the benefits of using data science and analytics even though many of them still don’t fully understand it and treat it as a black box.
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What is the Difference Between Data Science and Data Analytics?
The main problem many companies are facing is not the lack of data but being able to analyze it and come up with trends that can be used to improve efficiencies.
Another challenge is that a lot of this data is unstructured and is not stored in proper formats.
What’s even worse, not a long time ago a lot of data was manually written on paper and not stored in a digital format.
Nowadays most of the data comes in a form of Excel or ASCII files.
Some examples are drilling, geological, completions, and maintenance reports in addition to raw data acquired by various data acquisition systems.
That’s where data science and analytics come into play.
Data science and analytics have many overlaps and some people use these two terms interchangeably.
This is especially common in smaller companies where data scientists might perform both data science and data analyst tasks.
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What is Data Science?
Data science has to do quite a bit with statistics and math.
It involves analyzing data to find patterns that can be used to improve different processes.
Data scientists are often involved in R & D and perform quite a bit of mathematical modeling.
They are also often the ones who create the models that later can be used for data analytics.
Data science is usually more theoretical than data analytics.
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What is Required to Work as Data Scientist?
Many people that work as data scientists have a background in math.
They are familiar with various software packages used for data analysis. In addition, they are proficient in Python, R, and SQL.
Python is one of the most commonly used programming languages and it is pretty easy to learn.
R is the language used for statistical data analysis
Structured Query Language (SQL) is a programming language used to allow various applications to communicate with a database where structured data can be stored.
Data science relies heavily on programming, artificial intelligence, and machine learning.
Currently, some universities are starting to offer courses in petroleum engineering with a minor in digital engineering.
Data scientists usually get paid more than data analysts.
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What Is Data Analytics?
Data analysts usually know what data they are looking for while data scientists are looking at the bigger picture trying to figure out which data might be useful.
Data analytics is usually more specific than data science and often involves working closely with operations to create reporting systems that they can use.
This might include reporting on key performance indicators (KPIs) that are used by management, sales, and operations to make better business and operational decisions.
What is Required to Work as Data Analyst?
To work as a data analyst you will need to have some knowledge of Python, SQL, and some VBA but it doesn’t have to be as in-depth as in the case of someone who wants to be a data scientist.
Most of the time data analysts work with software packages like PowerBi or Spotfire for example.
To get the required skills to work as data scientists or analysts many people take online programming boot camps or even Udemy courses.
Udemy courses are pretty cheap and you can get some for $20.
Boot camps on the other hand are more expensive ($7000 – $20000) and require more time commitment.
The difference between boot camps and Udemy courses is that boot camps have deadlines and help you to stay on track.
In addition, you will usually learn more during boot camps, will be able to network, and will have higher chances of landing a job than someone who just took a Udemy course.
In general, to start working as a data scientist it helps if you have some formal math education or even Ph.D. because it is a more math-intensive field than data analytics.
To increase your chances of landing a job as a data scientist or analyst it helps if you can showcase some of the projects that you were working on in your free time.
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References:
www.northeastern.edu/graduate/blog/data-analytics-vs-data-science/