When you enroll in this course, you'll also be enrolled in this Specialization.
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There are 4 modules in this course
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.
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.
Learn what clinical data science is all about and get access to the free technology environment hosted by Google Cloud!
What's included
4 videos6 readings2 assignments
Show info about module content
4 videos•Total 16 minutes
Welcome to Introduction to Clinical Data Science•2 minutes
Introduction to Clinical Data Science•4 minutes
Clinical Data Regulations•6 minutes
Introduction to the MIMIC-III Data Set•5 minutes
6 readings•Total 50 minutes
Get help and meet other learners in this course. Join your discussion forums!•5 minutes
Introduction to Specialization Instructors•5 minutes
Course Policies•5 minutes
Accessing Course Data and Technology Platform•15 minutes
Regulations and Health Privacy Resources•10 minutes
MIMIC-III Resources and References•10 minutes
2 assignments•Total 60 minutes
Week 1 Assessment•30 minutes
Week 1 Practice Quiz•30 minutes
Introduction: Clinical Data
Module 2•1 hour to complete
Module details
Clinical data are complex. Walk through the four-W's of clinical data to understand where they come from and what they look like.
What's included
7 videos2 assignments
Show info about module content
7 videos•Total 35 minutes
Introduction to Clinical Data•5 minutes
Encounters•5 minutes
Billing Data•6 minutes
Laboratory Data•8 minutes
Medication Data•7 minutes
Clinical Observation Data•2 minutes
Demographics, Social and Family History•2 minutes
2 assignments•Total 46 minutes
Week 2 Assessment•16 minutes
Week 2 Practice Quiz•30 minutes
Tools: SQL
Module 3•3 hours to complete
Module details
Develop basic skills in SQL (Structured Query Language) and query the real clinical data set used in the Clinical Data Science Specialization.
What's included
5 videos6 readings2 assignments
Show info about module content
5 videos•Total 18 minutes
Introduction to Databases•4 minutes
Querying Tables with SQL•2 minutes
Joining Tables with SQL•5 minutes
Aggregating Data with SQL•4 minutes
Introduction to Google BigQuery•3 minutes
6 readings•Total 107 minutes
Introduction and Learning Objectives for Programming Examples and Exercises•5 minutes
Guide to Google BigQuery Interface•10 minutes
Querying and Aggregating Individual Tables with Google BigQuery •45 minutes
Querying and Joining Multiple Tables with Google BigQuery•30 minutes
Joining Tables with SQL•15 minutes
Note about the Assessment•2 minutes
2 assignments•Total 50 minutes
Week 3 Assessment•20 minutes
Programming Exercises Practice Quiz•30 minutes
Tools: R and the Tidyverse
Module 4•2 hours to complete
Module details
Learn how to use the tidyverse to implement your Clinical Data Science Workflow in R.
What's included
2 videos3 readings2 assignments
Show info about module content
2 videos•Total 6 minutes
Introduction to R and the Tidyverse•3 minutes
Introduction to RStudio•3 minutes
3 readings•Total 87 minutes
Working with RMarkdown Documents•10 minutes
The Data Scientist's Workflow•75 minutes
Note about the Assessment•2 minutes
2 assignments•Total 22 minutes
Week 4 Assessment•0 minutes
Programming Exercises Practice Quiz•22 minutes
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Instructor
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Learner reviews
4.5
430 reviews
5 stars
67.20%
4 stars
23.02%
3 stars
4.41%
2 stars
2.55%
1 star
2.79%
Showing 3 of 430
K
KY
4·
Reviewed on Nov 15, 2020
The course is great overall. But when it comes to teaching SQL and R, there is too much to learn with just a few explanation. It is not easy for those who do not have background knowledge on those.
M
MM
5·
Reviewed on May 8, 2020
I started out without any idea on how to navigate R Programming and by the end of this course is wasn't such a novice anymore.....
P
PE
5·
Reviewed on Feb 28, 2024
I was able to learn and practice SQL and R using Google BigQuery on Google Cloud, and used real-world clinical database.
I live in an area that restricts access to Google products. Will I be able to complete the specialization?
Unfortunately at this time we can only allow students who have access to Google services (e.g., a gmail account) to complete the specialization. This is because we give students access to real clinical data and our privacy protections only allow data sharing through the Google BigQuery environment.
When will I have access to the lectures and assignments?
To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
What will I get if I subscribe to this Specialization?
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.
Is financial aid available?
Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.