Imperial College London
Statistical Analysis with R for Public Health Specialization
Imperial College London

Statistical Analysis with R for Public Health Specialization

Master Statistics for Public Health and Learn R. Develop your statistical thinking skills and learn key data analysis methods through R

Taught in English

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Victoria Cornelius
Alex Bottle

Instructors: Victoria Cornelius

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Specialization - 4 course series

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4.7

(1,653 reviews)

Beginner level

Recommended experience

1 month at 10 hours a week
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Prepare for a degree

What you'll learn

  • Recognise the key components of statistical thinking in order to defend the critical role of statistics in modern public health research and practice

  • Describe a given data set from scratch using descriptive statistics and graphical methods as a first step for more advanced analysis using R software

  • Apply appropriate methods in order to formulate and examine statistical associations between variables within a data set in R

  • Interpret the output from your analysis and appraise the role of chance and bias as explanations for your results

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Specialization - 4 course series

Get in-depth knowledge of a subject

4.7

(1,653 reviews)

Beginner level

Recommended experience

1 month at 10 hours a week
Flexible schedule
Learn at your own pace
Prepare for a degree

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Specialization - 4 course series

Introduction to Statistics & Data Analysis in Public Health

Course 115 hours4.7 (1,413 ratings)

What you'll learn

  • Defend the critical role of statistics in modern public health research and practice

  • Describe a data set from scratch, including data item features and data quality issues, using descriptive statistics and graphical methods in R

  • Select and apply appropriate methods to formulate and examine statistical associations between variables within a data set in R

  • Interpret the output from your analysis and appraise the role of chance and bias

Skills you'll gain

Category: Run basic analyses in R
Category: Formulate a scientific hypothesis
Category: R Programming
Category: Understand common data distributions and types of variables

Linear Regression in R for Public Health

Course 214 hours4.8 (493 ratings)

What you'll learn

  • Describe when a linear regression model is appropriate to use

  • Read in and check a data set's variables using the software R prior to undertaking a model analysis

  • Fit a multiple linear regression model with interactions, check model assumptions and interpret the output

Skills you'll gain

Category: Correlation And Dependence
Category: Linear Regression
Category: R Programming

Logistic Regression in R for Public Health

Course 312 hours4.8 (346 ratings)

What you'll learn

  • Describe a data set from scratch using descriptive statistics and simple graphical methods as a first step for advanced analysis using R software

  • Interpret the output from your analysis and appraise the role of chance and bias as potential explanations

  • Run multiple logistic regression analysis in R and interpret the output

  • Evaluate the model assumptions for multiple logistic regression in R

Skills you'll gain

Category: Logistic Regression
Category: R Programming

Survival Analysis in R for Public Health

Course 411 hours4.5 (303 ratings)

What you'll learn

  • Run Kaplan-Meier plots and Cox regression in R and interpret the output

  • Describe a data set from scratch, using descriptive statistics and simple graphical methods as a necessary first step for more advanced analysis

  • Describe and compare some common ways to choose a multiple regression model

Skills you'll gain

Category: Understand common ways to choose what predictors go into a regression model
Category: Run and interpret Kaplan-Meier curves in R
Category: Construct a Cox regression model in R

Instructors

Victoria Cornelius
Imperial College London
2 Courses15,484 learners

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