The Initiative Reinforces the Integration Between Data Science, Industrial Applications and Artificial Intelligence in the Energy Sector
Radix and UnIBP launch the first edition of the course “Analysis and Exploration of Industrial Data in the Oil, Gas and Energy Sector”, and therefore, this movement highlights the growing need for professionals specialized in data, operational efficiency, and artificial intelligence. The training will be online and live from 11/18 to 12/13, and thus, it is aimed at professionals working in these segments who seek to integrate data science, industrial applications, and AI. The central goal of the training is to boost talents capable of facing complex challenges, and in this way, the initiative reinforces the importance of reliability and operational efficiency in strategic industrial environments.
Course Structure and Integration with Industrial Practices
The course represents the beginning of an advanced track in AI and machine learning, and therefore, it presents 24 hours of content distributed in modules that combine theory and practice. Students start with good development practices in Python, and thus, they use tools like Git to ensure traceability and reproducibility of projects in regulated environments. Additionally, participants explore typical industrial data, and in this way, they interpret information from production sensors, predictive maintenance histories, inspections of pipelines, and subsea equipment. Statistical and visualization techniques are continuously applied, and therefore, they allow for the identification of anomalies, operational failures, deviations, and optimization opportunities. In the final stage, the course addresses pre-processing and qualification of industrial data, and with that, it prepares information to feed predictive models aimed at asset integrity and operational reliability. Participants also analyze a real case, and therefore, they bring learning closer to field routines.
Highlights from the Leadership of Radix and UnIBP
João Carlos Chachamovitz, CEO of Radix, emphasizes that the partnership combines know-how and strategic vision, and thus, it strengthens the continuous training of professionals in the energy sector. According to him, the initiative creates a consistent talent pipeline, and therefore, it responds to the growing demands of a constantly evolving market. Luana Britto, Manager of Radix University, reinforces that the sector requires specialists capable of transforming data into high-impact decisions, and in this way, the course enables scientists, engineers, analysts, and developers to stand out through the use of AI. She highlights that the track serves as an entry point, and therefore, it serves as a foundation for future modules focused on machine learning and industrial applications. Claudia Rabello, Corporate Executive Director of IBP, states that the partnership strengthens the role of UnIBP as a promoter of knowledge and innovation, and with that, she emphasizes that the sector is increasingly data-driven, automated, and AI-oriented. Karen Cubas, Manager of UnIBP, adds that the university’s commitment involves preparing professionals with a forward-thinking perspective, and in this way, the initiative connects knowledge and practice to develop essential technical competencies.
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In the 1970s, oil became a weapon of war and paralyzed the entire world, and now the same thing is happening again with Iran closing off the route for 20% of the planet’s fuel.
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When oil prices rise, the gas station increases prices the next day, but when it drops by 13% at once, no one explains why gasoline remains at the same price for months.
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The largest fuel distributor in Brazil has just been forced to import diesel and gasoline on its own after Petrobras reduced its monthly deliveries.
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After Trump gave Iran 48 hours to reopen the route for 20% of the world’s oil, the barrel skyrocketed to $117, dropped 13% with a truce, and the Central Bank had to inject $2 billion to stabilize the dollar.
Technical Prerequisites and Focus on Skill Development
The course requires intermediate knowledge in Python, and therefore, it requires basic notions of statistics and linear algebra, as well as familiarity with NumPy, Pandas, and PyTorch. These prerequisites ensure that participants can follow the track efficiently, and thus, they support the advancement towards technical qualification.

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