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Applied data science refers to the practical application of data science methods and techniques to solve real-world problems in various fields such as business, healthcare, finance, and government. It involves the use of statistical analysis, machine learning, data visualization, and other data-driven approaches to extract insights and knowledge from large and complex datasets.
Advanced applied data science involves using more sophisticated algorithms and models to analyze data and make predictions or decisions. This includes deep learning techniques such as convolutional neural networks, recurrent neural networks, and generative adversarial networks, as well as reinforcement learning and natural language processing. Advanced data scientists also have a deep understanding of mathematical and statistical concepts, and can effectively communicate their findings to both technical and non-technical audiences.
To become an advanced data scientist, one must have a solid foundation in mathematics, statistics, computer science, and data science principles. It's also important to continually stay up-to-date with new advancements in the field and learn how to apply them to real-world problems. Additionally, strong communication and collaboration skills are essential, as data scientists often work in cross-functional teams with stakeholders from different backgrounds and levels of expertise.
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