Data Science describes a broad range of theories, algorithms, and tools that lead to a better understanding and predictive modeling of the world around us. It is an interdisciplinary field of scientific methods, processes, and systems to extract knowledge or insights from data, structured or unstructured. It employs techniques and theories drawn from many fields within the broad areas of mathematics, statistics, and computer science; equally important are the application areas that provide the context and domain-specific principles with which data science can have an impact and reach its potential.

The broad umbrella of Data Science includes these topics:

  • Theories and algorithms from statistics and machine learning for making predictions from data that are heterogeneous, multimodal, and multiscalar.
  • Data management techniques for efficiently organizing and accessing large and complex data sets.
  • Data-driven exploration and modeling across the sciences, humanities, social sciences, medicine, engineering, economics, and education, that utilize theories and methods from data science to make domain-specific advances, as well as opening up broad new research questions and challenges.
  • The human aspect of data, including human-computer-interaction, visualization, privacy, and social policy.

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