Advanced Clinical Data Science

  • 0.0
Approx. 4 hours to complete

Course Summary

This course is designed for healthcare professionals who want to advance their skills in clinical data analysis. Students will learn how to collect, manage, and analyze clinical data using advanced statistical methods and machine learning techniques.

Key Learning Points

  • Gain practical experience in data analysis and machine learning techniques for healthcare data
  • Learn how to collect and manage clinical data to support research and clinical decision making
  • Understand the ethical and legal issues surrounding clinical data management and analysis

Related Topics for further study


Learning Outcomes

  • Understand the principles of clinical data management
  • Apply statistical methods and machine learning techniques to healthcare data
  • Develop skills in data visualization and communication

Prerequisites or good to have knowledge before taking this course

  • Basic understanding of statistical concepts
  • Familiarity with R and Python programming

Course Difficulty Level

Advanced

Course Format

  • Online
  • Self-paced
  • Interactive

Similar Courses

  • Applied Healthcare Data Analysis
  • Clinical Research Data Management
  • Advanced Statistics for Data Science

Related Education Paths


Related Books

Description

This course prepares you to deal with advanced clinical data science topics and techniques including temporal and research quality analysis.

Outline

  • Introduction: Advanced Clinical Data Science
  • Welcome to Advanced Clinical Data Science
  • Introduction to Analytic Integrity
  • Replicability of EHR-Based Analyses
  • Interpreting Analytic Questions
  • Implementing Analytic Questions
  • Data Quality in Real World Electronic Health Records
  • Data Quality in ETL
  • Impact of Workflow on Data Quality
  • Keep Calm and Data Science On
  • Introduction to Specialization Instructors
  • Course Policies
  • Week 1 Assessment
  • Tools and Techniques: Temporality
  • Introduction to Temporality
  • Temporality: Medicine and Healthcare
  • Temporality: Electronic Health Records
  • Changes Over Time: Medicine and Healthcare
  • Changes Over Time: Electronic Health Records
  • Temporality and Analytics
  • Understanding Dates and Times: R & SQL
  • Understanding Dates and Times: MIMIC-III and Common Data Models
  • Week 2 Assessment
  • Tools and Techniques: Missing Data
  • Introduction to Missing Data
  • Types of Missing Data
  • Types of Missing Data: Electronic Health Records
  • Types of Missing Data: Clinical Care
  • Data Missingness Techniques: Electronic Health Records
  • Data Missingness Techniques: Record Linkage and Data Quality
  • Data Missingness Techniques: Clinical Care
  • Missing Data Wrap Up
  • Week 3 Assessment
  • Practical Application: Careers in Clinical Data Science
  • Introduction to Careers in Clinical Data Science
  • Understanding Job Opportunities in Clinical Data Science
  • Clinical Data Science by Any Other Name
  • Clinical Data Science Competencies
  • Evaluating Clinical Data Science Opportunities
  • Wrapping up the Specialization

Summary of User Reviews

Discover the benefits of advanced clinical data science with this comprehensive course. Users praise the course for its practical approach and emphasis on real-world applications.

Key Aspect Users Liked About This Course

Practical approach and emphasis on real-world applications.

Pros from User Reviews

  • In-depth coverage of advanced topics in clinical data science.
  • Practical exercises and case studies that help users apply their knowledge.
  • Engaging and knowledgeable instructors who provide helpful feedback.
  • Flexible scheduling and self-paced learning options.
  • Great preparation for a career in clinical data science.

Cons from User Reviews

  • Some users find the course material to be dense and difficult to understand.
  • The course may be too advanced for beginners in data science.
  • Some users feel that the course could benefit from more interactive elements.
  • The course may not be suitable for users who are looking for a more theoretical approach to data science.
  • The cost of the course may be prohibitive for some users.
English
Available now
Approx. 4 hours to complete
Michael G. Kahn, MD, PhD, Laura K. Wiley, PhD
University of Colorado System
Coursera
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