Statistical Analysis with R for Public Health
Master statistics and R programming for public health research. Learn key analysis methods including regression, survival analysis, and data interpretation.
Master statistics and R programming for public health research. Learn key analysis methods including regression, survival analysis, and data interpretation.
This comprehensive specialization teaches statistical analysis for public health using R programming. Students learn essential concepts from basic statistical thinking to advanced methods like linear regression, logistic regression, and survival analysis. The program emphasizes hands-on learning through real-world public health datasets, covering topics such as sampling, uncertainty, variation, and distributions. Designed for beginners, it requires no prior knowledge of statistics or R, making it ideal for those interested in quantitative public health research.
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বাংলা, پښتو, اردو, 3 more
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What you'll learn
Master statistical thinking for public health research. Analyze data using descriptive statistics and graphical methods in R. Apply linear and logistic regression techniques to health data. Interpret statistical outputs and assess the role of chance and bias. Use survival analysis for public health outcomes. Formulate and test scientific hypotheses. Handle real-world public health datasets.
Skills you'll gain
This course includes:
53 Hours PreRecorded video
Multiple assessments across 4 courses
Access on Mobile, Desktop, Tablet
FullTime access
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There are 4 courses in this program
Introduction to Statistics & Data Analysis in Public Health
Course 1 · 15 Hours to complete · 4 modules
Linear Regression in R for Public Health
Course 2 · 14 Hours to complete · 4 modules
Logistic Regression in R for Public Health
Course 3 · 12 Hours to complete · 4 modules
Survival Analysis in R for Public Health
Course 4 · 11 Hours to complete · 4 modules
Fee Structure
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