Data Science in Petroleum Industry

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Big Data can be used for Supply Analytics, Predictive Maintenance, Proactive EHS, Seismic Analysis, Reservoir Modelling, Drilling Services, and Production management. There are several areas where big data can be used to benefit refining and oil exploration industry: Production Optimization. Data science is a broad career path that is undergoing developments and thus promises abundant opportunities in the future. Data science job roles are likely to get more specific, which in turn will lead to specializations in the field . But it's not just access to data that helps you make smarter decisions, it's the way you analyze it. That's why it's important to understand the four levels of analytics: descriptive, diagnostic, predictive and prescriptive.




Applications of Data Science
  • Healthcare. Healthcare companies are using data science to build sophisticated medical instruments to detect and cure diseases.
  • Gaming. ...
  • Image Recognition. ...
  • Recommendation Systems. ...
  • Logistics. ...
  • Fraud Detection. ...
  • Internet Search. ...
  • Speech recognition.

What is Petroleum Data Analyst ? 

Petroleum Data Analytics provides a set of tools and techniques for both conventional and unconventional resources to extract patterns and trends from data and construct predictive models to assist decision making and optimization. this programme is tailored to deal specifically with managing the physical and digital data that are used across the oil and gas industry, to understand and evaluate the subsurface and the petroleum reserves located there.

Is Python Enough For Data Science ? 

Python is a high-level, general-purpose programming language known for its intuitive syntax that mimics natural language. You can use Python code for a wide variety of tasks, but three popular applications include: Data science and data analysis. Web application development.


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