Applied Biostatistics Using R

R is a programming language with built-in statistical functions and the flexibility to be extended with user-written code. It is also widely used for creating high-quality visualizations and plots. Data carpentry focuses on teaching researchers essential skills and tools for efficient data management and analysis.

Courses

 

  1. Programming Language for Health Sciences and Health-related Data
  2. Programming Language for Economics
  3. Programming Language for Health Economics
  4. Programming Language for Engineering
Receive a 10% discount by registering for 2 or more courses
(Fee covers training materials)
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Programming Language for Health Sciences and Health-related Data
A scoping review is an approach to map out the breadth and depth of available research on a topic.

leeds institute
Programming Language for Economics
Upon completion of this course, learners working with economics data will be able to: define the following terms as they relate to R:

leeds institute
Programming Language for Health Economics
Upon completion of this course, learners working with health economics data will be able to: define the following terms as they relate to R
leeds institute
Programming Language for Engineering
Upon completion of this course, learners working with engineering data will be able to: define the following terms as they relate to R:

Description

Computer skills in biostatistics encompass the ability to manage, analyze, and interpret large sets of biological and health data. These skills often involve programming languages like R and Python for statistical analysis, data visualization, and algorithm development, which are essential for modern biostatistics. Proficiency in these areas allows biostatisticians to apply statistical methods to complex research questions in fields such as public health, epidemiology, and clinical research. By mastering these tools and techniques, individuals can produce reliable, data-driven insights that support healthcare decisions, improve health outcomes, and advance scientific understanding.

Course Structure

    • Course Duration: A period of 3 immersive weeks designed to guide you through live online classes.
    • Learning Hours: 6 hours of learner-instructor interaction.
    • Learning Experience: Engage with instructors and other learners in real-time and go through learning path together.
    • Interactive Assessments: Stay on track with fun quizzes, peer collaboration, and hands-on projects.
    • Certification: Showcase your achievement with an official Certificate of Completion at the end of the course!
    • Contact Support to register for two or more courses here.

Course Highlight

Target Learners

This training program is designed for individuals or groups of people who work in clinical settings, academia, pharmaceutical industries, Government, corporations, nonprofit organizations, and organizations that work on biological data.

Why this course

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