Online Course – Google Clinical Data Science Certified Professional Internship

Launch your career in clinical data science. A six-lesson course to introduce you to using clinical data to improve patient care for tomorrow.

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קורס אונליין - התמחות מקצועית מוסמכת במדעי הנתונים הקליניים של Google

Professional Certificate

Intermediate level

No prior knowledge required

Time to complete the course

7-day free trial

No unnecessary risks

Skills you will acquire in the course

  • Understand the data types and structures in electronic health records
  • Apply basic informatics methodologies to clinical data
  • Provide appropriate clinical and scientific interpretation of analyses performed
  • Anticipate barriers to implementing informatics tools in complex clinical settings
  • Demonstrate capabilities by completing practical implementation projects using real clinical data

What you will learn in the course

Courses for which the course is suitable

  • Clinical Health Data Scientist
  • Clinical Data Analyst
  • Medical Informatics Specialist
  • Healthcare software developer
  • Health researcher
  • Health Systems Analyst
  • Health data consultant
  • Healthcare Project Manager

Internship – 6-part course series

Are you interested in learning how to use data generated by doctors, nurses, and the healthcare system to improve patient care in the future? If so, you may be an aspiring clinical health data scientist!

This specialization provides teachers with hands-on experience using electronic health records and informatics tools to conduct clinical data science. This six-course series is designed to build on learners’ existing skills in statistics and programming, while providing examples of specific challenges, tools, and appropriate interpretations of clinical data.

During the internship you will learn how to:

  • Understand the data types and structures in electronic health records
  • Apply basic informatics methodologies to clinical data
  • Provide appropriate clinical and scientific interpretation of analyses performed
  • Anticipate barriers to implementing informatics tools in complex clinical settings

You will demonstrate your abilities by completing practical implementation projects that use real clinical data.

This internship is supported by a collaboration with Google Cloud. Thanks to this support, all learners will be able to access a free, online-hosted scientific data environment! Please note that you must have access to a Google account (such as a gmail account) to access clinical data and the computing environment.

Applied Learning Project

Each internship course concludes with a final project that is a practical application of the tools and techniques you learned throughout the course. In these projects, you will apply your skills to a real clinical data set using a free, web-hosted data science environment provided by our industry partner, Google Cloud.

Details of the courses that make up the specialization

Introduction to Clinical Data Science

  • Course 1 – 8 hours – 4.5 (398 ratings)
  • What you will learn:
    • Describe how each type of clinical data is produced.
    • Write SQL code to join two or more tables.
    • Write R code to manipulate and sort data.
    • Write text in Markdown format and integrate with R code in RMarkdown documents.

Clinical data models and data quality assessments

  • Course 2 – 17 hours – 4.2 (63 ratings)
  • What you will learn:
    • Interpret and evaluate data model designs using entity-relationship diagrams (ERD).
    • Difference between data models and explanation of how each model supports clinical care and data science.
    • Create SQL statements in Google BigQuery to explore the MIMIC3 clinical data model.

Identifying patient populations

  • Course 3 – 13 hours – 4.5 (39 ratings)
  • What you will learn:
    • Create a computational phenomenological algorithm.
    • Evaluate the algorithm’s performance in the context of the analytical goal.
    • Create combinations of at least three data types using Boolean logic.
    • Explain the impact of data types on computational phenotyping performance.

Clinical Natural Language Processing

  • Course 4 – 12 hours – 3.6 (22 ratings)
  • What you will learn:
    • Identifying and distinguishing between the complexities of text mining and natural language processing.
    • Write basic regular expressions to recognize clinical text.
    • Evaluate and select record segments for analytical questions.
    • Write R code to search text windows for keywords.

Predictive Models and Clinical Practice Change

  • Course 5 – 11 hours
  • What you will learn:
    • Fundamentals of changing clinical practice using predictive models.
    • Challenges and methods for clinical implementation.

Advanced Clinical Data Science

  • Course 6 – 4 hours – 4.8 (22 ratings)
  • What you will learn:
    • Advanced technological topics in clinical data science.
    • Qualitative-temporal analysis and qualitative analysis for research.

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