This course, part of the Clinical Data Science Specialization, equips learners with essential skills to work with clinical data. Over its duration, students will delve into the generation, format, ethical and legal constraints, and utilization of clinical data. Through the lens of SQL and R programming, participants will gain the ability to manipulate and tidy data, and use markdown formatted text in RMarkdown documents.
With access to an actual clinical data set and a free, online computational environment hosted by Google Cloud, learners will apply their newly acquired knowledge in a practical setting. Upon completion, individuals will be well-prepared to pursue further education in clinical data science, with the aim of leveraging healthcare data to enhance patient health in the future.
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This course encompasses modules covering clinical data generation, SQL, and R programming, providing a comprehensive introduction to working with clinical data and preparing learners for further education in clinical data science.
Welcome to Introduction to Clinical Data Science. This module provides a brief overview and sets the stage for the journey ahead, acquainting learners with the course structure and expectations.
This module delves into the different types of clinical data, providing insight into encounters, billing data, laboratory data, medication data, clinical observation data, and demographics, social, and family history data.
Here, learners will gain proficiency in SQL, learning to query and join tables, aggregate data, and utilize Google BigQuery, providing a robust foundation in database querying and manipulation.
This module introduces R and the Tidyverse, along with RStudio and RMarkdown documents. Learners will understand the data scientist's workflow and have the opportunity to apply their knowledge through programming exercises.
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