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    3. The Data Science of Healthcare, Medicine, and Public Health [Updated: 6/23/2025]

    The Data Science of Healthcare, Medicine, and Public Health [Updated: 6/23/2025]

    Posted By: IrGens
    Date: 23 Jun 2025 17:00:50
    The Data Science of Healthcare, Medicine, and Public Health [Updated: 6/23/2025]

    The Data Science of Healthcare, Medicine, and Public Health [Updated: 6/23/2025]
    .MP4, AVC, 1280x720, 30 fps | English, AAC, 2 Ch | 2h 32m | 436 MB
    Instructor: Barton Poulson

    Data analytics plays an increasingly pivotal role in how we go about managing our healthcare systems and personal health. This nontechnical course explores a variety of ways to apply data science to medicine and public health. Data scientist Barton Poulson discusses the ways that dramatically increased quantities of data—from genetic testing, genealogy, brain scans, and other sources—are changing the theory and practice of healthcare. As he looks over key data sources, Barton explores the different methods for analyzing this wealth of data, including machine learning and predictive modeling. He also details the extraordinary effect of the COVID-19 pandemic on the ways data is used in healthcare, and details how data science has changed the areas of patient care, self-care, and medical insurance. Barton also looks at some challenges the increased use of data presents, from ethical questions to logistical implementations, and how data science can help identify unanticipated problems.

    Learning objectives

    • Identify the major applications of data science across healthcare domains including disease research, treatment planning, patient care, and healthcare administration. (Knowledge level)
    • Explain how specific data science methodologies are being applied to address critical challenges in healthcare, from disease diagnosis to treatment selection. (Comprehension level)
    • Compare and contrast the various data technologies (EHRs, wearables, genetic datasets, blockchain) that are transforming healthcare delivery and research. (Analysis level)
    • Analyze ethical considerations and equity challenges in healthcare data science applications. (Analysis level)
    • Evaluate the limitations, barriers, and future potential of data science solutions in healthcare contexts. (Evaluation level)
    • Design a conceptual framework for applying data science methods to address a specific healthcare challenge. (Creation level)

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    The Data Science of Healthcare, Medicine, and Public Health [Updated: 6/23/2025]
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