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What you'll learn

Knows (the value of) all aspects of data management and acknowledge the importance of initial data analysis.

Knows the pros and cons of statistical methods and can choose the appropriate data analysis approach in common health related problems.

Is able to interpret statistical results and to draw responsible conclusions.

Skills you'll gain

Data Analysis

Data Cleansing

Data Quality

Statistical Methods

Statistical Hypothesis Testing

Statistical Modeling

Exploratory Data Analysis

Statistical Reporting

Descriptive Statistics

Regression Analysis

Data Ethics

Statistical Inference

Probability & Statistics

R Programming

Data Collection

There are 5 modules in this course

First, you will learn how to obtain, safely gather, clean and explore data. Then, we will discuss that because data are usually obtained from a sample of a limited number of individuals, statistical methods are needed to make claims about the whole population of interest. You will discover how statistical inference, hypothesis testing and regression techniques will help you to make the connection between samples and populations. A final important aspect is interpreting and reporting. How can we transform information into knowledge? How can we separate trustworthy information from noise? In the last part of the course, we will cover the critical assessment of the results, and we will discuss challenges and dangers of data analysis in the era of big data and massive amounts of information. In this course, we will emphasize the concepts and we will also teach you how to effectively perform your analysis using R. You do not need to install R on your computer to follow the course, you will be able to access R and all the example data sets within the Coursera environment. This course will become part of the to-be-developed Leiden University master program Population Health Management. If you wish to find out more about this program see the last reading of this Course!

From Individuals to Data

From data to information I: statistical inference

From data to information II: regression techniques

From information to knowledge