The Data Science of Healthcare, Medicine, and Public Health
2h 32mBeginner2022-08-26
Authors

Barton Poulson
Professor, Designer, Data Analytics Expert
Course details
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.
Skills covered
Data Science FoundationsProject Management SkillsProject ManagementData ScienceOne-Off
Concepts
0. Introduction
- 01 - Healthcare and data science
1. Connecting Data Science
- 02 - Data science and COVID-19
2. Researching Diseases and Populations
- 03 - Shared history of health and data visualization
- 04 - Measuring health and disease
- 05 - Researching diseases in populations
- 06 - Genetic data and healthcare
3. Researching Treatment and Outcomes
- 07 - Diagnosing diseases
- 08 - Drug (re)discovery
- 09 - Predicting outcomes
- 10 - Treatments and ROI
4. Patient Care
- 11 - Selecting treatments and interventions
- 12 - Individualized treatment
- 13 - Robotic surgery
- 14 - 3D printing and healthcare
- 15 - Virtual reality in healthcare
- 16 - Telemedicine
5. Self-Care
- 17 - Wearables and health monitoring
- 18 - Self-serve healthcare
- 19 - Patient experience
6. Medical Administration and Insurance
- 20 - Administrative burden for doctors and staff
- 21 - Blockchain and medical data
- 22 - Data science and health insurance
7. Challenges
- 23 - Limitations and barriers
- 24 - Data ethics in healthcare
- 25 - Preparing for an unknown future
8. Continuing Your Data Science of Healthcare Learning Journey
- 26 - Next steps and additional resources
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