Imperial College London
Introduction to Statistics & Data Analysis in Public Health
Imperial College London

Introduction to Statistics & Data Analysis in Public Health

This course is part of Statistical Analysis with R for Public Health Specialization

Taught in English

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Alex Bottle

Instructor: Alex Bottle

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Course

Gain insight into a topic and learn the fundamentals

4.7

(1,431 reviews)

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95%

Beginner level

Recommended experience

15 hours (approximately)
Flexible schedule
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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

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Assessments

14 quizzes

Course

Gain insight into a topic and learn the fundamentals

4.7

(1,431 reviews)

|

95%

Beginner level

Recommended experience

15 hours (approximately)
Flexible schedule
Learn at your own pace
Prepare for a degree

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This course is part of the Statistical Analysis with R for Public Health Specialization
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There are 4 modules in this course

Statistics has played a critical role of in public health research and practice, and you’ll start by looking at two examples: one from eighteenth century London and the other by the United Nations. The first task in carrying out a research study is to define the research question and express it as a testable hypothesis. With examples from the media, you’ll see what does and does not work in this regard, giving you a chance to define a research question from some real news stories.

What's included

5 videos7 readings2 quizzes2 discussion prompts1 plugin

This module will introduce you to some of the key building blocks of knowledge in statistical analysis: types of variables, common distributions and sampling. You’ll see the difference between “well-behaved” data distributions, such as the normal and the Poisson, and real-world ones that are common in public health data sets.

What's included

6 videos3 readings5 quizzes3 discussion prompts

Now it’s time to get started with the powerful and completely free statistical software R and its popular interface RStudio. With the example of fruit and vegetable consumption, you’ll learn how to download R, import the data set and run essential descriptive analyses to get to know the variables.

What's included

2 videos10 readings2 quizzes1 discussion prompt

Having learned how to define a research question and testable hypothesis earlier in the course, you’ll learn how to apply hypothesis testing in R and interpret the result. As all medical knowledge is derived from a sample of patients, random and other kinds of variation mean that what you measure on that sample, such as the average body mass index, is not necessarily the same as in the population as a whole. It’s essential that you incorporate this uncertainty in your estimate of average BMI when presenting it. This involves the calculation of a p value and confidence interval, fundamental concepts in statistical analysis. You’ll see how to do this for averages and proportions.

What's included

4 videos14 readings5 quizzes2 discussion prompts1 plugin

Instructor

Instructor ratings
4.8 (408 ratings)
Alex Bottle
Imperial College London
6 Courses64,522 learners

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