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Advanced Clinical Data Science

Approx. 4 hours to complete

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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

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.

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