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In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
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Statistical Software for Data Analysis and Clinical Trials01:12

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Statistical software is pivotal in data analysis and clinical trials by providing tools to analyze data, draw conclusions, and make predictions. These software packages range from simple data management applications to complex analytical platforms, supporting various statistical tests, models, and simulation techniques. Their significance lies in their ability to handle vast amounts of data with precision and efficiency, enabling researchers to validate hypotheses, identify trends, and make...
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SAS, short for Statistical Analysis System, is a powerful data analysis, management, and visualization tool. Developed by the SAS Institute in the early 1970s, SAS has evolved into a comprehensive software suite used across various industries for statistical analysis, business intelligence, and predictive modeling.
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Project-Based Learning Guidelines for Health Sciences Students: An Analysis with Data Mining and Qualitative Techniques
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Data science for analyzing and improving educational processes.

Shadi Aljawarneh1, Juan A Lara2

  • 1Software Engineering Department, Jordan University of Science and Technology, Irbid, Jordan.

Journal of Computing in Higher Education
|November 3, 2021
PubMed
Summary

This review explores emerging trends in Educational Data Science for Smart Education, highlighting techniques like Machine Learning and IoT. Recommendations are provided to enhance research quality in this evolving field.

Keywords:
Educational data miningEducational data scienceEducational processesLearning analytics

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Area of Science:

  • Educational Data Science
  • Smart Education

Background:

  • Systematic review of recent trends and contributions in Educational Data Science.
  • Focus on state-of-the-art frameworks and techniques in data science applied to education.

Discussion:

  • Exploration of diverse approaches including Information Fusion, Soft Computing, Machine Learning, and Internet of Things.
  • Analysis of rigorously reviewed, world-class manuscripts.

Key Insights:

  • Identification of emerging trends shaping the future of educational data science.
  • Synthesis of current research methodologies and technological applications.

Outlook:

  • Recommendations for researchers, practitioners, and scholars to improve research quality.
  • Suggestions for advancing the field of Educational Data Science in the Smart Education era.