Explore

Introduction to Statistics & Data Analysis in Public Health

Approx. 16 hours to complete

Save Course

Go to Course

Course Summary

This course is an introduction to statistics and data analysis in the context of public health. It covers basic statistical concepts, methods, and their applications in public health research and practice.

Key Learning Points

Learn basic statistical concepts and methods in the context of public health
Apply statistical analysis to public health research and practice
Understand the importance of data analysis and interpretation in public health

Job Positions & Salaries of people who have taken this course might have

- USA: $70,990 - $119,290
- India: ₹359,003 - ₹1,932,546
- Spain: €20,000 - €45,000
- USA: $70,990 - $119,290
- India: ₹359,003 - ₹1,932,546
- Spain: €20,000 - €45,000
- USA: $68,000 - $133,000
- India: ₹355,508 - ₹2,431,370
- Spain: €24,000 - €45,000
- USA: $70,990 - $119,290
- India: ₹359,003 - ₹1,932,546
- Spain: €20,000 - €45,000
- USA: $68,000 - $133,000
- India: ₹355,508 - ₹2,431,370
- Spain: €24,000 - €45,000
- USA: $50,000 - $99,000
- India: ₹313,237 - ₹1,752,415
- Spain: €20,000 - €40,000

Learning Outcomes

Understand basic statistical concepts and methods in the context of public health
Apply statistical analysis to public health research and practice
Interpret statistical results in the context of public health

Prerequisites or good to have knowledge before taking this course

Basic knowledge of mathematics and algebra
Familiarity with Microsoft Excel

Course Difficulty Level

Intermediate

Course Format

Online
Self-paced
Video lectures
Assignments
Quizzes

Similar Courses

Biostatistics for Big Data Applications
Epidemiology: The Basic Science of Public Health
Data Analysis and Interpretation Specialization

Related Education Paths

Related Books

Description

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.

Knowledge

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

Outline

Introduction to Statistics in Public Health
Introduction to Statistical Thinking for Public Health
Uses of Statistics in Public Health
Introduction to Sampling
How to Formulate a Research Question
Formulating a research question for the Parkinson's disease and supplement studies
About Imperial College & the Team
How to be successful in this course
Grading policy
Data set and Glossary
Additional Reading
John Snow and the Cholera outbreak of 1849
Instructions for Quiz
Parkinson's Disease Study Issues
Research Question Formulation

Types of Variables, Common Distributions and Sampling
Introduction to variables, distribution and sampling
Overview of types of variables
Well-behaved Distributions
Real-world Distributions and their Problems
The Role of Sampling in Public Health Research
How to choose a Sample
Types of variables and the special case of age
More on the 95% Confidence Interval
Using your sample to estimate the population mean
Types of variables
Special case of age
Well-behaved Distributions
Ways of Dealing with Weird Data
Sampling

Introduction to R and RStudio
How to describe distributions of real data
How to Load Data and run Basic Tabulations in R
How to Calculate Percentiles
Introduction to R
R Resources
Practice with R: Perform Descriptive Analysis
Feedback: Descriptive Analysis
How to judge visually if a variable is normally distributed in R
Practice with R - trying it out for yourself
Extra features in R
Practice with R: Extra features
Feedback: Extra features
Distributions and Medians
Calculations: Percentiles by Hand

Hypothesis Testing in R
Sampling errors for proportions and central limit theorem
Hypothesis Testing
Choosing the Sample Size for your Study
Summary of Course
The Coin Tossing Experiment: Part I
The Coin Tossing Experiment: Part II
The Coin Tossing Experiment: Feedback
Degrees of Freedom
The chi-squared test with fruit and veg
Feedback: Sample Size and Variation
Comparing Two Means
Practice with R: Hypothesis Testing
Feedback: Hypothesis Testing in R
The Difference between t-test and Chi-squared test
Practice with R: Running a New Hypothesis Test
P values and Thresholds
Deaths data set for the end-of-course Assessment
Final R code
Hypothesis Testing
The Coin Tossing Experiment: Evaluation
Results: Running a New Hypothesis Test
Hypothesis Testing
End-of-course Assessment

Summary of User Reviews

Discover the Introduction to Statistics and Data Analysis course in Public Health on Coursera. This course is highly rated by its users and has great content for those starting in statistics. Many users have praised the course's hands-on approach and practical examples.

Key Aspect Users Liked About This Course

Hands-on approach and practical examples

Pros from User Reviews

Clear and easy-to-follow lectures
Great instructors who are knowledgeable and responsive
Good mix of theory and practical examples
Course content is relevant to real-world situations
Excellent resource for beginners in statistics

Cons from User Reviews

Course can be challenging for those with no prior math background
Some users have experienced technical difficulties with the platform
Course materials can be overwhelming at times
Limited opportunity for interaction with other students
Not all topics are covered in-depth

Recommended for you

Mega Course VMware vSphere 6.7 Optimize Upgrade Troubleshoot

34. 5hrs of VMware vSphere 6. 7 lectures, lab demos to learn ESXi / vCenter, for VCP / VCAP VMware Certification and more...

Save Course

Mega Social Media Marketing Course: 13 Courses In 1

Become A Professional In Social Media Marketing: Facebook Ads, Pinterest, Instagram, Snapchat, LinkedIn & YouTube Videos Course 1 Create Super Simple And Powerful Content For Facebook, Pinterest, and Instagram...

Save Course