This course teaches you the fundamentals of transforming clinical practice using predictive models. This course examines specific challenges and methods of clinical implementation, that clinical data scientists must be aware of when developing their predictive models.
Offered By


About this Course
Offered by

University of Colorado System
The University of Colorado is a recognized leader in higher education on the national and global stage. We collaborate to meet the diverse needs of our students and communities. We promote innovation, encourage discovery and support the extension of knowledge in ways unique to the state of Colorado and beyond.
Syllabus - What you will learn from this course
Introduction: Clinical Prediction Models
Learn about the many types of clinical prediction models that exist and how they are put into practice.
Tools: Ensuring Model Usability
Understand how qualitative methods can be used to develop clinical prediction models that are more likely to transform clinical practice.
Techniques: Model Implementation and Sustainability
Learn about the different tools that are used to implement clinical prediction models in practice and the factors that affect implementations over time.
Techniques: Data Selection, Model Building, and Evaluation
Understand how the different types of clinical data can be used in prediction models and learn how choices made during model construction affect the utility of the model in practice.
About the Clinical Data Science Specialization
Are you interested in how to use data generated by doctors, nurses, and the healthcare system to improve the care of future patients? If so, you may be a future clinical data scientist!

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