Introduction to Statistics & Data Analysis in Public Health
Welcome to Introduction to Statistics & Data Analysis in Public Health!This course will teach you the core building blocks of statistical analysis - types of variables, common distributions, hypothesis testing - but, more than that, it will enable you to take a data set you’ve never seen before, describe its keys features, get to know its strengths and quirks, run some vital basic analyses and then formulate and test hypotheses based on means and proportions. You’ll then have a solid grounding to move on to more sophisticated analysis and take the other courses in the series. You’ll learn the popular, flexible and completely free software R, used by statistics and machine learning practitioners everywhere. It’s hands-on, so you’ll first learn about how to phrase a testable hypothesis via examples of medical research as reported by the media. Then you’ll work through a data set on fruit and vegetable eating habits: data that are realistically messy, because that’s what public health data sets are like in reality. There will be mini-quizzes with feedback along the way to check your understanding. The course will sharpen your ability to think critically and not take things for granted: in this age of uncontrolled algorithms and fake news, these skills are more important than ever.
Prerequisites
Some formulae are given to aid understanding, but this is not one of those courses where you need a mathematics degree to follow it. You will need only basic numeracy (for example, we will not use calculus) and familiarity with graphical and tabular ways of presenting results. No knowledge of R or programming is assumed.
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
Syllabus
Syllabus - What you will learn from this course
Week 1
Introduction to Statistics in Public Health
Week 2
Types of Variables, Common Distributions and Sampling
Week 3
Introduction to R and RStudio
Week 4
Hypothesis Testing in R
FAQ
When will I have access to the lectures and assignments?
Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:
What will I get if I subscribe to this Specialization?
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.
Is financial aid available?
Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.
Reviews
This course is an excellent introduction to statistical analysis in public health. In addition, the course provides a useful start to the application of R in statistical analysis.
I liked the way the whole course has been designed. I never felt stressed up. It was all fun. The speaker was excellent in blending real life example with statistics.
Challenging but very informative. Sometimes I was not clear on the concept but usually there was a discussion a couple of clicks away which elucidated things,
This course is great. I like how it is structured and the feedback after activities. Combines theory and practices in an efficient way. Congratulations.
