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Data Science and Analytics: An Overview from Data-Driven Smart Computing, Decision-Making and Applications

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This paper explores data science and advanced analytics for smart decision-making in the Fourth Industrial Revolution. It covers machine learning methods and ten application domains, highlighting future research directions.

Keywords:
Advanced analyticsData scienceData science applicationsDecision-makingDeep learningMachine learningPredictive analyticsSmart computing

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Area of Science:

  • Data Science
  • Advanced Analytics
  • Machine Learning

Background:

  • The Fourth Industrial Revolution (Industry 4.0) generates vast digital data (IoT, business, health, etc.).
  • Extracting insights from this data enables smart decision-making across various applications.

Purpose of the Study:

  • To provide a comprehensive overview of Data Science and advanced analytics methods.
  • To explore the application of these methods for enhancing intelligence and capabilities through smart decision-making.
  • To identify potential real-world application domains and future research directions.

Main Methods:

  • Review of advanced analytics techniques, including machine learning modeling.
  • Analysis of data-driven smart computing and decision-making principles.
  • Summarization of ten real-world application domains.

Main Results:

  • Data science and advanced analytics offer actionable insights for automatic and smart computing.
  • Ten diverse application domains (business, healthcare, cybersecurity, etc.) are identified for data-driven solutions.
  • Key challenges and future research avenues within data science are highlighted.

Conclusions:

  • This paper serves as a reference for data science and advanced analytics.
  • It emphasizes the importance of data-driven solutions for real-world problems.
  • It guides researchers, decision-makers, and developers in leveraging data science effectively.