About this Course

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Learner Career Outcomes

67%

started a new career after completing these courses

33%

got a tangible career benefit from this course
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Flexible deadlines
Reset deadlines in accordance to your schedule.
Beginner Level

You will only need an interest in analysing quantitative data and familiarity with reading standard graphs and tables of data.

Approx. 16 hours to complete
English
Subtitles: English

What you will 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 will gain

Run basic analyses in RR ProgrammingUnderstand common data distributions and types of variablesFormulate a scientific hypothesis

Learner Career Outcomes

67%

started a new career after completing these courses

33%

got a tangible career benefit from this course
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Flexible deadlines
Reset deadlines in accordance to your schedule.
Beginner Level

You will only need an interest in analysing quantitative data and familiarity with reading standard graphs and tables of data.

Approx. 16 hours to complete
English
Subtitles: English

Offered by

Imperial College London logo

Imperial College London

Start working towards your Master's degree

This course is part of the 100% online Global Master of Public Health from Imperial College London. If you are admitted to the full program, your courses count towards your degree learning.

Syllabus - What you will learn from this course

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Week
1

Week 1

4 hours to complete

Introduction to Statistics in Public Health

4 hours to complete
5 videos (Total 23 min), 7 readings, 2 quizzes
5 videos
Uses of Statistics in Public Health5m
Introduction to Sampling3m
How to Formulate a Research Question3m
Formulating a research question for the Parkinson's disease and supplement studies4m
7 readings
About Imperial College & the Team10m
How to be successful in this course10m
Grading policy10m
Data set and Glossary10m
Additional Reading10m
John Snow and the Cholera outbreak of 184920m
Instructions for Quiz10m
2 practice exercises
Parkinson's Disease Study Issues15m
Research Question Formulation1h
Week
2

Week 2

4 hours to complete

Types of Variables, Common Distributions and Sampling

4 hours to complete
6 videos (Total 34 min), 3 readings, 5 quizzes
6 videos
Overview of types of variables4m
Well-behaved Distributions7m
Real-world Distributions and their Problems5m
The Role of Sampling in Public Health Research8m
How to choose a Sample4m
3 readings
Types of variables and the special case of age10m
More on the 95% Confidence Interval10m
Using your sample to estimate the population mean20m
5 practice exercises
Types of variables20m
Special case of age20m
Well-behaved Distributions20m
Ways of Dealing with Weird Data15m
Sampling10m
Week
3

Week 3

3 hours to complete

Introduction to R and RStudio

3 hours to complete
2 videos (Total 20 min), 10 readings, 2 quizzes
2 videos
How to Load Data and run Basic Tabulations in R13m
10 readings
How to Calculate Percentiles10m
Introduction to R20m
R Resources10m
Practice with R: Perform Descriptive Analysis10m
Feedback: Descriptive Analysis10m
How to judge visually if a variable is normally distributed in R10m
Practice with R - trying it out for yourself10m
Extra features in R10m
Practice with R: Extra features10m
Feedback: Extra features10m
2 practice exercises
Distributions and Medians20m
Calculations: Percentiles by Hand20m
Week
4

Week 4

5 hours to complete

Hypothesis Testing in R

5 hours to complete
4 videos (Total 20 min), 14 readings, 5 quizzes
4 videos
Hypothesis Testing6m
Choosing the Sample Size for your Study4m
Summary of Course2m
14 readings
The Coin Tossing Experiment: Part I10m
The Coin Tossing Experiment: Part II10m
The Coin Tossing Experiment: Feedback20m
Degrees of Freedom 20m
The chi-squared test with fruit and veg20m
Feedback: Sample Size and Variation10m
Comparing Two Means10m
Practice with R: Hypothesis Testing10m
Feedback: Hypothesis Testing in R10m
The Difference between t-test and Chi-squared test10m
Practice with R: Running a New Hypothesis Test10m
P values and Thresholds10m
Deaths data set for the end-of-course Assessment10m
Final R code10m
5 practice exercises
Hypothesis Testing10m
The Coin Tossing Experiment: Evaluation30m
Results: Running a New Hypothesis Test20m
Hypothesis Testing15m
End-of-course Assessment20m

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About the Statistical Analysis with R for Public Health Specialization

Statistics are everywhere. The probability it will rain today. Trends over time in unemployment rates. The odds that India will win the next cricket world cup. In sports like football, they started out as a bit of fun but have grown into big business. Statistical analysis also has a key role in medicine, not least in the broad and core discipline of public health. In this specialisation, you’ll take a peek at what medical research is and how – and indeed why – you turn a vague notion into a scientifically testable hypothesis. You’ll learn about key statistical concepts like sampling, uncertainty, variation, missing values and distributions. Then you’ll get your hands dirty with analysing data sets covering some big public health challenges – fruit and vegetable consumption and cancer, risk factors for diabetes, and predictors of death following heart failure hospitalisation – using R, one of the most widely used and versatile free software packages around. This specialisation consists of four courses – statistical thinking, linear regression, logistic regression and survival analysis – and is part of our upcoming Global Master in Public Health degree, which is due to start in September 2019. The specialisation can be taken independently of the GMPH and will assume no knowledge of statistics or R software. You just need an interest in medical matters and quantitative data....
Statistical Analysis with R for Public Health

Frequently Asked Questions

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