Introduction to Statistics & Data Analysis in Public Health

It's a pleasure to welcome you to Introduction to Statistics & Data Analysis in Public Health! This course will teach you the fundamental concepts of statistical analysis, such as the different types of variables, typical distributions, and hypothesis testing, but it will also give you the skills to take a data set that you have never seen before, describe its key characteristics, learn about its advantages and disadvantages, perform some critical preliminary analyses, and then create and test hypotheses based on means and proportions. After completing Statistics & Data Analysis in Public Health course, you'll be well-equipped to advance to more complex analyses and enroll in the following courses in the series. This can be seen as one of the Best Online Public Health Courses.

R, a widely used, adaptable, and totally free piece of software by statisticians and machine learning experts worldwide, will be taught to you. You will first get practical knowledge of how to formulate a testable hypothesis by studying examples of media-reported medical research. Then you'll examine a data set on fruit and vegetable consumption patterns, which is really chaotic since real-world public health data sets are like that.

Along the process of Statistics & Data Analysis in Public Health course, there will be brief tests with feedback to make sure you understand. The training will hone your capacity to think critically and not take anything for granted, which is more crucial than ever in the era of unchecked algorithms and fake news. Although some equations are provided to help with understanding, this is not one of those courses that requires you to have a degree in mathematics to follow it. Only rudimentary math skills and experience with graphical and tabular methods of results presentation are required. There is no presumption of programming or R knowledge.

What you will learn

• Substantiate how important statistics are to current public health research and practice.
• Descriptive statistics and graphical techniques in R can be used to describe a data collection from scratch, including data item attributes and data quality issues.
• R allows you to establish and analyse statistical correlations between variables within a data collection by choosing and using the right procedures.
• Interpret the results of your analysis and evaluate the impact of bias and chance

Skill you will gain

• Perform fundamental analysis in R
• R Programming
• Recognize typical data distributions and variable types
• Create a scientific premise.

Instructor: Alex Bottle

Offered by: Imperial College London

Coursera rating: 4.7/5.0, 1.285 ratings

Enroll here: https://www.coursera.org/learn/introduction-statistics-data-analysis-public-health