- The oil and gas industry is undergoing several challenges to be able to extract most of the value from data science teams. In my experience, these challenges can be summarized in the following ways. Infrastructure limitations, data platform may not be available to hold and process large amount of structured and unstructured data. Additionally, computational resources may not be available to facilitate development of prototypes and deployment of production solutions. Another problem that we find is that data are generally scattered, subject to high variety and inconsistent governance. This require to architect the right platform to ensure fast, consistent, and clean data for effective analytics consumption. We also encounter the failing in posing the right question, and project scope in a data science project may lead to discouragement and misjudgment in properly valuing analytics for the business. It is critical to avoid bringing solutions to non-existing problems. Additionally, corporate culture needs to be prepared to understand and embrace the potential that analytics brings into the business. This requires clear management and executive support from the get-go. Training and strategies to facilitate collaboration and communication between domain experts and data scientists are critical Last but not least, data science and data engineering talent is sparse in oil and gas, so it is a true challenge to assemble the right team. This concludes our first lecture. In the next lectures, we will dive deeper into multiple topics, including data exploration, clustering, classification, and regression. Thanks for your attention, and see you in the next lecture.