In most areas of health, data is being used to make important decisions. As a health population manager, you will have the opportunity to answer interesting questions by using data. In this course, we will discuss data analysis from a responsible perspective, which will help you to extract useful information from data and enlarge your knowledge about specific aspects of interest of the population. First, you will learn how to safely obtain, gather, clean and explore data. Then, we will discuss that data. Since 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 the data. 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 introduce the concepts through video-lectures, reading activities, individual home assigments and discussiong through group assigments. We will also teach you how to effectively perform your analysis using R, through interactive tutorials and live sessions.
Concise description of the course objectives formulated in terms of knowledge, insight and skills students will have acquired at the end of the course. The relationship between these objectives and achievement levels for the programme should be evident.
(max 150 words)
Upon successful completion of this course, the student:
can explain how to obtain, store, clean and explore the data necessary to answer a research question.
can design data collection protocols and perform initial data analysis.
can recall basic and modern statistical concepts (like estimation, testing and regression) and recognize them as a collection of tools to analyze complex data.
can apply different types of statistical techniques, interpret and report the results given the characteristics of the data and study design.
can choose the appropriate data analysis methods in common population health management research situations.
can recognise the challenges and dangers of data analysis in the era of big data and massive amounts of information.
can critically assess data analysis results in the context of population health management.
can effectivelly communicate data analysis results and formulate recommendations in the context of population health management.
All course and group schedules are published on MyTimeTable.
The exam dates have been determined by the Education Board and are published in MyTimeTable.
It will be announced in MyTimeTable and/or Brightspace when and how the post-exam feedback will be organized.
Mode of instruction
Students are assessed according to the following three obligatory components:
Week 1-2 – Online:
20% Peer review assessment
Week 3 – On Campus:
30% Group presentation
Week 4 – Final week:
50% Final assignment
All components combined make up the grade for the course. It is compulsory to participate in each of the components in order to receive a grade.
Details on the assessment can be found in the assessment plan on Brightspace.
A minimum result of 5,5 for the overall assessment is required to pass.
If the result is less than 5,5 or if the student didn’t participate in one of the components, the student is given the opportunity to resit the assessment as one assignment that covers all the learning goals of the course.
A final grade of 5,5 minimum is considered sufficient.
The reading list can be found on Brightspace. The material consists of presentations and pdf files. There is no need to purchase literature, as the presented material is not commercialized.
Registration must be completed via MyStudyMap. Registration in MyStudyMap gives you automatic access to the course in Brightspace. For more information, please visit the Leiden University website for students.
Mar Rodríguez-Girondo, PhD - firstname.lastname@example.org
This course is a combination of online education and on campus education at Leiden University Campus The Hague.