Course catalogue doctoral education - HT22

  • Application can be done between 2022-04-19 and 2022-05-16
Application closed
Title Fundamentals of using Python in Health Related Research
Course number 5316
Programme Epidemiology
Language English
Credits 1.5
Date 2021-10-18 -- 2021-10-22
Responsible KI department Department of Global Public Health
Specific entry requirements Epidemiology I: Introduction to epidemiology; Biostatistics I: Introduction for epidemiologists and Biostatistics II: Logistic regression for epidemiologists, or equivalent courses.
Purpose of the course This course aims at introducing students to the fundamental elements of the Python programming language. Motivating examples arising from health-related research will be used to demonstrate how to use the programming language to answer a variety of relevant questions. Learning activities will give students the possibility to learn Python the hard yet easier way – that is – problem, code, and run.
Intended learning outcomes After successfully completing this course you as a student should be able to:
• import and describe different types of data
• produce high quality figures of statistics
• estimate multivariable regression models (linear, logistic) including spline analysis
• conduct statistical inference based on the statistical model
• simulate plausible data generating mechanisms
• automatize code using looping and comprehension
Contents of the course The course is a full-time hands-on practice of Python language answering relevant health related questions based on either empirical or simulated data. The participant will learn how to import a dataset, create visualizations of distributions and statistics, estimation using popular regression models (linear, logistic), inference (likelihood based statistical tests, pointwise confidence intervals) on predicted responses or changes in predicted responses, draw pseudo-random values from theoretical probability distributions, Monte-Carlo simulations of common data generating mechanisms (interaction, non-linearity), and basic elements of programming such as creating new functions and avoid looping using comprehensions.
Teaching and learning activities Lectures, group work, exercises, and individual coding workout using Python.
Compulsory elements The individual examination (summative assessment) is compulsory.
Examination Individual written examination. Students who do not obtain a passing grade in the first examination will be offered a second chance to resubmit the examination within two months of the final day of the course. Students who do not obtain a passing grade at the first two examinations will be given top priority for admission the next time the course is offered.
Literature and other teaching material Useful link:
Number of students 8 - 25
Selection of students Eligible doctoral students will be prioritized according to 1) the relevance of the course syllabus for the applicant’s doctoral project (according to written information), 2) date for registration as a doctoral student (priority given to earlier registration date). To be considered, submit a completed application form. Give all information requested, including a short description of current research training and motivation for attending, as well as an account of previous courses taken.
More information The participant is expected to have sufficient familiarity with the computer to be able to install Python (, Jupyter Notebook ( as well as the following modules: pandas, matplotlib, numpy, scipy, statsmodels ( prior to the course start.
Additional course leader
Latest course evaluation Not available
Course responsible Nicola Orsini
Department of Global Public Health
Contact person Anastasia Urban
Institutionen för global folkhälsa