Syllabus database for doctoral courses
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Syllabus database for doctoral courses
SYLLABI FOR DOCTORAL COURSES
| Swedish title | Grundläggande Python-språk i hälsorelaterad forskning |
|---|---|
| English title | Fundamentals of using Python in Health Related Research |
| Course number | 5316 |
| Credits | 1.5 |
| Responsible KI department | Institutionen för global folkhälsa |
| Specific entry requirements | Epidemiology I: Introduction to epidemiology; Biostatistics I: Introduction for epidemiologists and Biostatistics II: Logistic regression for epidemiologists, or equivalent courses. |
| Grading | Passed /Not passed |
| Established by | The Committee for Doctoral Education |
| Established | 2021-03-12 |
| 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: https://www.python.org/about/gettingstarted/ | Course responsible |
Nicola Orsini Institutionen för global folkhälsa Nicola.Orsini@ki.se |
| Contact person |
Anastasia Urban Institutionen för global folkhälsa 0852483350 0737121534 anastasia.urban@ki.se |
