Launch your career in Clinical Data Science. A six-course introduction to using clinical data to improve the care of tomorrow's patients.

4.4

stars

302 ratings

Laura K. Wiley, PhD +1 more instructor

7,272 already enrolled

Offered By

Clinical Data Science SpecializationUniversity of Colorado System

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.

Skills you will gain

About this Specialization

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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.

Applied Learning Project

Each course in the specialization culminates in a final project that is a practical application of the tools and technique you learned throughout the course. In these projects you will apply your skills to a real clinical data set using the free, fully hosted online data science environment provided by our industry partner, Google Cloud.

Shareable Certificate

Earn a Certificate upon completion

100% online courses

Start instantly and learn at your own schedule.

Coursera Labs

Includes hands on learning projects.

Learn more about Coursera Labs

Flexible Schedule

Set and maintain flexible deadlines.

Intermediate Level

Approximately 7 months to complete

Suggested pace of 2 hours/week

English

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Shareable Certificate

Earn a Certificate upon completion

100% online courses

Start instantly and learn at your own schedule.

Coursera Labs

Includes hands on learning projects.

Learn more about Coursera Labs

Flexible Schedule

Set and maintain flexible deadlines.

Intermediate Level

Approximately 7 months to complete

Suggested pace of 2 hours/week

English

Could your company benefit from training employees on in-demand skills?

Try Coursera for Business

See how employees at top companies are mastering in-demand skills

Learn more about Coursera for Business

How the Specialization Works

Take Courses

A Coursera Specialization is a series of courses that helps you master a skill. To begin, enroll in the Specialization directly, or review its courses and choose the one you'd like to start with. When you subscribe to a course that is part of a Specialization, you’re automatically subscribed to the full Specialization. It’s okay to complete just one course — you can pause your learning or end your subscription at any time. Visit your learner dashboard to track your course enrollments and your progress.

Hands-on Project

Every Specialization includes a hands-on project. You'll need to successfully finish the project(s) to complete the Specialization and earn your certificate. If the Specialization includes a separate course for the hands-on project, you'll need to finish each of the other courses before you can start it.

Earn a Certificate

When you finish every course and complete the hands-on project, you'll earn a Certificate that you can share with prospective employers and your professional network.

There are 6 Courses in this Specialization

Course1

Course 1

Introduction to Clinical Data Science

4.6

stars

367 ratings

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.

Course2

Course 2

Clinical Data Models and Data Quality Assessments

4.2

stars

55 ratings

This course aims to teach the concepts of clinical data models and common data models. Upon completion of this course, learners will be able to interpret and evaluate data model designs using Entity-Relationship Diagrams (ERDs), differentiate between data models and articulate how each are used to support clinical care and data science, and create SQL statements in Google BigQuery to query the MIMIC3 clinical data model and the OMOP common data model.

Course3

Course 3

Identifying Patient Populations

4.4

stars

35 ratings

This course teaches you the fundamentals of computational phenotyping, a biomedical informatics method for identifying patient populations. In this course you will learn how different clinical data types perform when trying to identify patients with a particular disease or trait. You will also learn how to program different data manipulations and combinations to increase the complexity and improve the performance of your algorithms. Finally, you will have a chance to put your skills to the test with a real-world practical application where you develop a computational phenotyping algorithm to identify patients who have hypertension. You will complete this work using a real clinical data set while using a free, online computational environment for data science hosted by our Industry Partner Google Cloud.

Course4

Course 4

Clinical Natural Language Processing

3.5

stars

20 ratings

This course teaches you the fundamentals of clinical natural language processing (NLP). In this course you will learn the basic linguistic principals 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. Finally, you will have a chance to put your skills to the test with a real-world practical application where you develop text processing algorithms to identify diabetic complications from clinical notes. You will complete this work using a free, online computational environment for data science hosted by our Industry Partner Google Cloud.

Instructors

Laura K. Wiley, PhD

Assistant Professor

Division of Biomedical Informatics and Personalized Medicine, Anschutz Medical Campus

23,360 Learners

6 Courses

Michael G. Kahn, MD, PhD

Professor of Clinical Informatics

Department of Pediatrics, Anschutz Medical Campus

7,499 Learners

2 Courses

Offered by

University of Colorado System

Industry Partners

Frequently Asked Questions

What is the refund policy?
If you subscribed, you get a 7-day free trial during which you can cancel at no penalty. After that, we don’t give refunds, but you can cancel your subscription at any time. See our full refund policy.
Can I just enroll in a single course?
Yes! To get started, click the course card that interests you and enroll. You can enroll and complete the course to earn a shareable certificate, or you can audit it to view the course materials for free. When you subscribe to a course that is part of a Specialization, you’re automatically subscribed to the full Specialization. Visit your learner dashboard to track your progress.
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.
Can I take the course for free?
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. If you only want to read and view the course content, you can audit the course for free. If you cannot afford the fee, you can apply for financial aid.
Is this course really 100% online? Do I need to attend any classes in person?
This course is completely online, so there’s no need to show up to a classroom in person. You can access your lectures, readings and assignments anytime and anywhere via the web or your mobile device.
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 (i.e., 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.
How long does it take to complete the Specialization?
The specialization will take approximately 6 months to complete. However students can take the specialization at their own pace.
What background knowledge is necessary?
Some experience or awareness of programming and statistical concepts are helpful. However, Course 1 - Introduction to Clinical Data Science, provides learners with enough training in SQL and R to complete the specialization.
Do I need to take the courses in a specific order?
We highly recommend that you take Course 1 - Introduction to Clinical Data Science, first as it is meant to provide basic training and information useful for Courses 2-6. Although you may take Course 2-6 in any order, it may be helpful to take them sequentially.
Will I earn university credit for completing the Specialization?
This Specialization doesn't carry university credit, but some universities may choose to accept Specialization Certificates for credit. Check with your institution to learn more. Additionally, certification in this specialization may enhance professional credentials and attribute to new jobs, salary increases, or promotions.

More questions? Visit the Learner Help Center.

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Clinical Data Science Specialization

What you will learn

Skills you will gain

About this Specialization

Applied Learning Project