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Laura K. Wiley, PhD and Michael G. Kahn, MD, PhD

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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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!

This specialization provides learners with hands on experience in use of electronic health records and informatics tools to perform clinical data science. This series of six courses is designed to augment learner’s existing skills in statistics and programming to provide examples of specific challenges, tools, and appropriate interpretations of clinical data.

By completing this specialization you will know how to: 1) understand electronic health record data types and structures, 2) deploy basic informatics methodologies on clinical data, 3) provide appropriate clinical and scientific interpretation of applied analyses, and 4) anticipate barriers in implementing informatics tools into complex clinical settings. You will demonstrate your mastery of these skills by completing practical application projects using real clinical data.

This specialization is supported by our industry partnership with Google Cloud. Thanks to this support, all learners will have access to a fully hosted online data science computational environment for free! Please note that you must have access to a Google account (i.e., gmail account) to access the clinical data and computational environment.

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What's inside

Six courses

Introduction to Clinical Data Science

(0 hours)
This course prepares you for the Clinical Data Science Specialization. You will learn how clinical data are generated, their format, and ethical and legal restrictions. You will also learn enough SQL and R programming skills to complete the entire Specialization, even if you are a beginner programmer. You will have access to an actual clinical data set and a free, online computational environment for data science hosted by our Industry Partner Google Cloud.

Clinical Data Models and Data Quality Assessments

(0 hours)
This course teaches clinical data models and common data models. Upon completion, learners will understand data model designs, differentiate between data models, and use SQL to query clinical data models.

Identifying Patient Populations

(0 hours)
This course introduces computational phenotyping, a method for identifying patient populations. Learn how clinical data types perform in identifying diseases or traits. Program data manipulations and combinations to enhance algorithm performance. Apply your skills to a real-world project using a clinical data set and Google Cloud's computational environment.

Clinical Natural Language Processing

(0 hours)
This course teaches the basics of clinical natural language processing (NLP). You will learn the basic linguistic principles underlying NLP, as well as how to write regular expressions and handle text data in R. You will also learn practical techniques for text processing to be able to extract information from clinical notes.

Predictive Modeling and Transforming Clinical Practice

(0 hours)
This course teaches the fundamentals of transforming clinical practice using predictive models. It examines specific challenges and methods of clinical implementation that clinical data scientists must be aware of when developing their predictive models.

Advanced Clinical Data Science

(0 hours)
This course prepares you to handle advanced clinical data science topics and techniques, including temporal and research quality analysis.

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