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

Activity no.: 3918-22-00-00 
Enrollment deadline: 14/02/2022
PlaceCSS - Center for Sundhed og Samfund
Øster Farimagsgade 5, 1353 København K
Date and time07.03.2022, at: 09:00 - 18.03.2022, at: 14:00
Regular seats30
Course fee4,680.00 kr.
LecturersMartin Sikora
ECTS credits5.00
Contact personKirsten Wivel-Snejbjerg    E-mail address: kws@adm.ku.dk
Enrolment Handling/Course OrganiserPhD 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 graduate schools in the other Nordic countries. 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. Manage large-scale datasets using the UNIX command line
2. Import, visualize, transform and summarize datasets using the R statistical programming language
3. Understand 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 dataset

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 half of the course will be dedicated to practical aspects of computational data analysis using the UNIX shell and the R statistical programming language. Topics include an introduction to UNIX and R; data visualization and data wrangling in R using the tidyverse suite of packages; as well as reproducible computational workflows (snakemake). In the second half of the course, 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.

PhD fellows in the “Life, Earth and Environmental Sciences” Programme (required course) or related fields.

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


Combination of lectures and practical computational exercises

Course director
Martin Sikora, Associate Professor, Globe Institute, University of Copenhagen.

Martin Sikora, Associate Professor, Globe Institute, University of Copenhagen.
Fernando Racimo, Associate Professor, Globe Institute, University of Copenhagen.
Shyam Gopalakrishnan, Associate Professor, Globe Institute, University of Copenhagen, shyam.gopalakrishnan@sund.ku.dk

Thorfinn Sand Korneliussen, Assistant Professor, Globe Institute, University of Copenhagen

Block 3, 2 weeks from 7/3/22 – 18/3/22. Monday-Friday 9:00 – 14:00

Course location
Teaching room Kommunehospital

Please register before 14/2/22
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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