Coursera

Introduction to Clinical Data Science (Coursera)

Offered by University of Colorado System,
Introduction to Clinical Data Science (Coursera)

This course will prepare you to complete all parts of the Clinical Data Science Specialization. In this course you will learn how clinical data are generated, the format of these data, and the ethical and legal restrictions on these data. You will also learn enough SQL and R programming skills to be able to complete the entire Specialization - even if you are a beginner programmer. While you are taking this course 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.

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At the end of this course you will be prepared to embark on your clinical data science education journey, learning how to take data created by the healthcare system and improve the health of tomorrow's patients.
What You Will Learn

  • Describe how each type of clinical data are generated, specifically outlining who creates the data, when and why the data are generated.
  • Write SQL code to combine two or more tables using database joins.
  • Write R code to manipulate and tidy data including: selecting columns, filtering rows, and joining data sets.
  • Write markdown formatted text and combine with R code in RMarkdown documents.

Course 1 of 6 in the Clinical Data Science Specialization

Syllabus

WEEK 1
Welcome to the Clinical Data Science Specialization
Learn what clinical data science is all about and get access to the free technology environment hosted by Google Cloud!

WEEK 2
Introduction: Clinical Data
Clinical data are complex. Walk through the four-W's of clinical data to understand where they come from and what they look like.

WEEK 3
Tools: SQL
Develop basic skills in SQL (Structured Query Language) and query the real clinical data set used in the Clinical Data Science Specialization.

WEEK 4
Tools: R and the Tidyverse
Learn how to use the tidyverse to implement your Clinical Data Science Workflow in R.

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