Course Summary
This course provides an introduction to clinical data science and covers the basics of working with electronic health records (EHRs) for clinical research. Students will learn how to extract, clean, and transform EHR data, as well as how to use statistical methods to analyze and interpret it.Key Learning Points
- Learn the basics of clinical data science and electronic health records
- Extract, clean, and transform EHR data for clinical research
- Use statistical methods to analyze and interpret EHR data
Related Topics for further study
Learning Outcomes
- Learn how to extract and transform EHR data for clinical research
- Understand the basics of statistical analysis for clinical data science
- Develop skills in data cleaning and statistical interpretation
Prerequisites or good to have knowledge before taking this course
- Basic knowledge of programming concepts
- Familiarity with statistical methods
Course Difficulty Level
IntermediateCourse Format
- Online
- Self-paced
- Video lectures
- Assignments and quizzes
Similar Courses
- Data Science for Healthcare
- Healthcare Data Analytics
Related Education Paths
Notable People in This Field
- Cardiologist and Digital Health Advocate
- Surgeon and Author
Related Books
Description
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.
Knowledge
- 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.
Outline
- Welcome to the Clinical Data Science Specialization
- Welcome to Introduction to Clinical Data Science
- Introduction to Clinical Data Science
- Clinical Data Regulations
- Introduction to the MIMIC-III Data Set
- Introduction to Specialization Instructors
- Course Policies
- Accessing Course Data and Technology Platform
- Regulations and Health Privacy Resources
- MIMIC-III Resources and References
- Week 1 Practice Quiz
- Week 1 Assessment
- Introduction: Clinical Data
- Introduction to Clinical Data
- Encounters
- Billing Data
- Laboratory Data
- Medication Data
- Clinical Observation Data
- Demographics, Social and Family History
- Week 2 Practice Quiz
- Week 2 Assessment
- Tools: SQL
- Introduction to Databases
- Querying Tables with SQL
- Joining Tables with SQL
- Aggregating Data with SQL
- Introduction to Google BigQuery
- Introduction and Learning Objectives for Programming Examples and Exercises
- Guide to Google BigQuery Interface
- Querying and Aggregating Individual Tables with Google BigQuery
- Querying and Joining Multiple Tables with Google BigQuery
- Joining Tables with SQL
- Note about the Assessment
- Programming Exercises Practice Quiz
- Week 3 Assessment
- Tools: R and the Tidyverse
- Introduction to R and the Tidyverse
- Introduction to RStudio
- Working with RMarkdown Documents
- The Data Scientist's Workflow
- Note about the Assessment
- Programming Exercises Practice Quiz
Summary of User Reviews
Discover the world of clinical data science through this course on Coursera. The course has received positive reviews from users and is highly recommended. One key aspect that many users thought was good is the hands-on experience provided in the course.Pros from User Reviews
- The course provides hands-on experience in clinical data science
- Excellent introduction to the field of clinical data science
- The course is well-structured and easy to follow
- The instructors are knowledgeable and provide clear explanations
- The quizzes and assignments help reinforce the concepts learned
Cons from User Reviews
- Some users found the course to be too basic
- The course could benefit from more advanced topics
- The lectures can be lengthy and difficult to follow at times
- The course requires a significant time commitment
- Some users found the course to be too focused on R programming
Laura K. Wiley, PhD
4.6 Raiting
The Complete Pandas Bootcamp 2021: Data Science with Python
- 4.7
-
![]()
![]()
![]()
![]()
![]()
Pandas fully explained | 150+ Exercises | Must-have skills for Machine Learning & Finance | + Scikit-Learn and Seaborn ######### UPDATE (August 2021) ###########...
Algorithms: Design and Analysis, Part 1
- 0.0
-
![]()
![]()
![]()
![]()
![]()
Welcome to the self paced course, Algorithms: Design and Analysis! Algorithms are the heart of computer science, and the subject has countless practical applications as well as intellectual depth....
Data Science for Business Innovation
- 4.2
-
![]()
![]()
![]()
![]()
![]()
The course is a compendium of the must-have expertise in data science for executive and middle-management to foster data-driven innovation. Introduction to Data-driven Business...
ERPO: A Civil Approach to Gun Violence Prevention Teach-Out
- 0.0
-
![]()
![]()
![]()
![]()
![]()
Evidence shows that 1 life is saved for every 10-20 Extreme Risk Protection Orders (ERPOs) issued. 1. Define what ERPO laws are and how they work;...
