Home Data Science and GovernanceArtificial Intelligence How many fields in data science?

How many fields in data science?

by Massimo

Data Science is a research activity mostly

Data-driven scientific discovery is regarded as the fourth science paradigm 

 The twenty-first century has ushered in a new age that is coined as data science  and big data analytics. Data-driven scientific discovery is regarded as the fourth science paradigm. Data science has been a core driver of the new-generation science, technologies and economy, and is driving new researches, innovation, profession, applications and education across both disciplines and business domains. 

There are many scientific and technical challenges associated with big data, ranging from data capture, creation, storage, search, sharing, modeling, representation, analysis, learning, visualization, explanation, and decision making. Among the many data characteristics and complexities to be addressed, I mention  the hybridization of heterogeneous, multisource, hierarchical,
interactive, dynamic, multidimensional, and quality-poor data mixed with real-time business operations, strategic planning, decision-making, value creation, and future developments.
The field of data sciences and big data analytics have been evolving from statistics since half century ago to broad areas including but not limited to data and signal analytics, knowledge discovery, information retrieval, machine learning, statistics, optimization, computing, and data management. The literature defined areas of Data Science that requires in depth knowledge and pure research to be effective. By synergizing the three big areas—statistics, informatics and computing, data science has been spreading to essential and specific areas such as 

  1. data intelligence and complexity analysis
  2. representation, modeling, analytics, mining and learning including statistical and deep learning 
  3. computational intelligence including neural networks, evolutionary computing, fuzzy systems
  4. neuroscience and linguistics
  5. behavioral science and social and economic computing
  6. uncertainty and optimization
  7. system and modeling infrastructures and architectures
  8. networking and interoperation
  9. social issues including
  10. privacy, security, trust, value and impact,
  11. enterprises, services, applications, solutions and systems
  12. simulation, visualization and explanation


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