Graduate School of health and Medical Sciences

Fundamentals in Computational Analysis of Large-Scale Datasets
Provider: Faculty of Health and Medical Sciences

Activity no.: 3918-25-00-00	There are 21 available seats
	Enrollment deadline: 31/01/2025

Date and time	03.03.2025, at: 09:00 - 14.03.2025, at: 14:00
Regular seats	25
Course fee	11,040.00 kr.
Lecturers	Martin Sikora
ECTS credits	5.00

Contact person	Oliver Tafdrup E-mail address: gi-administration@sund.ku.dk

Enrolment Handling/Course Organiser	PhD administration E-mail address: phdkursus@sund.ku.dk

Aim and content This is a generic course. This means that the course is reserved for PhD students at the Graduate School of Health and Medical Sciences at UCPH. Anyone can apply for the course, but if you are not a PhD student at the Graduate School, you will be placed on the waiting list until enrollment deadline. After the enrolment deadline, available seats will be allocated to the waiting list. The course is free of charge for PhD students at Danish universities (except Copenhagen Business School), and for PhD students at NorDoc member faculties. All other participants must pay the course fee Learning objectives A student who has met the objectives of the course will be able to: 1. Import, visualize, transform and summarize datasets using the R statistical programming language 2. Be familiar with tools to create reproducible and scalable data analysis workflows 3. Describe basic concepts in probability theory 4. Distinguish supervised and unsupervised statistical learning and their applications 5. Perform a comprehensive exploratory analysis on a given real-world datas Content The topic of this course is to provide the attendees with a broad introduction into the fundamentals of modern computational data analysis. The aim is to equip the attendees with the basic tools for “making sense of data", from the fundamentals of working with large-scale datasets to introductory probability theory and statistics. The first week of the course is dedicated to practical aspects of computational data analysis using the UNIX shell and the R statistical programming language. Topics include data visualization and data wrangling in R using the tidyverse suite of packages as well as reproducible computational workflows (bash scripting / snakemake). In the second week, students will be introduced to basic concepts in probability theory and statistics, with topics including a probability theory bootcamp; introduction to supervised learning (linear regression); and introduction to unsupervised learning (PCA). The students will learn these topics through a combination of introductory lectures and hands-on analysis examples on real-world datasets. Participants PhD fellows in the “Life, Earth and Environmental Sciences” Programme (required course) or related fields where quantitative data analysis skills are requirements. Relevance to graduate programmes The course is relevant to PhD students from the following graduate programmes at the Graduate School of Health and Medical Sciences, UCPH: Life, Earth and Environmental Sciences Biostatistics and Bioinformatics Oral Sciences, Forensic Medicine and Bioanthropology Language English Form Combination of lectures and practical computational exercises Course director Martin Sikora, Associate Professor, Globe Institute, University of Copenhagen. martin.sikora@sund.ku.dk Teachers Martin Sikora, Associate Professor, Globe Institute, University of Copenhagen. martin.sikora@sund.ku.dk Shyam Gopalakrishnan, Associate Professor, Globe Institute, University of Copenhagen, shyam.gopalakrishnan@sund.ku.dk Antonio Fernandez Guerra, Assistant Professor, Globe Institute, University of Copenhagen antonio.fernandez-guerra@sund.ku.dk Dates Block 3, 2 weeks from 3/3/25 – 14/3/25. Monday-Friday 9:00 – 14:00 Course location Teaching room Kommunehospital Registration Please register before 31/1/25 Expected frequency annual Seats to PhD students from other Danish universities will be allocated on a first-come, first-served basis and according to the applicable rules. Applications from other participants will be considered after the last day of enrolment. Note: All applicants are asked to submit invoice details in case of no-show, late cancellation or obligation to pay the course fee (typically non-PhD students). If you are a PhD student, your participation in the course must be in agreement with your principal supervisor.

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