In this course, we’re going to go over analytical solutions to common healthcare problems. I will review these business problems and you’ll build out various data structures to organize your data. We’ll then explore ways to group data and categorize medical codes into analytical categories. You will then be able to extract, transform, and load data into data structures required for solving medical problems and be able to also harmonize data from multiple sources. Finally, you will create a data dictionary to communicate the source and value of data. Creating these artifacts of data processes is a key skill when working with healthcare data.
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Universidad de California, Davis
UC Davis, one of the nation’s top-ranked research universities, is a global leader in agriculture, veterinary medicine, sustainability, environmental and biological sciences, and technology. With four colleges and six professional schools, UC Davis and its students and alumni are known for their academic excellence, meaningful public service and profound international impact.
- 5 stars63,63 %
- 4 stars27,27 %
- 3 stars4,54 %
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Principales reseñas sobre ANALYTICAL SOLUTIONS TO COMMON HEALTHCARE PROBLEMS
Excellent material and a great introduction to data analytics!
Very informative. Would have preferred more practical examples on data analysis
Very good, although I would suggest the Health Informatics as a starting course
Acerca de Programa especializado: Health Information Literacy for Data Analytics
This Specialization is intended for data and technology professionals with no previous healthcare experience who are seeking an industry change to work with healthcare data. Through four courses, you will identify the types, sources, and challenges of healthcare data along with methods for selecting and preparing data for analysis. You will examine the range of healthcare data sources and compare terminology, including administrative, clinical, insurance claims, patient-reported and external data. You will complete a series of hands-on assignments to model data and to evaluate questions of efficiency and effectiveness in healthcare. This Specialization will prepare you to be able to transform raw healthcare data into actionable information.
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