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Advanced Topics in Data Analysis
Provider: Faculty of Health and Medical Sciences

Activity no.: 3339-21-00-00There are no available seats 
Enrollment deadline: 29/03/2021
Date and time26.04.2021, at: 09:00 - 07.05.2021, at: 14:30
Regular seats16
Course fee9,840.00 kr.
LecturersFernando Racimo
Shyam Gopalakrishnan
ECTS credits5.00
Contact personKirsten Wivel-Snejbjerg    E-mail address: kws@snm.ku.dk
Enrolment Handling/Course OrganiserPhD administration     E-mail address: fak-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. Understand the probabilistic principles behind statistical analysis of large-scale datasets in the life, earth and environmental sciences
2. Identify which types of statistical methods are appropriate for different types of large-scale datasets
3. Analyze data in an efficient manner using the R or a similar statistical language
4. Diagnose and assess the results of advanced statistical tests, accounting for the assumptions each test implies
5. Explain the basic principles of modern high-performance statistical methods, e.g. Monte Carlo methods and deep learning.

This course is meant as an in-depth exposure to the state-of-the-art statistical techniques commonly used in life, environmental and earth sciences. It is also a natural follow-up to the course on Fundamentals in Large-Scale Data Analysis offered within the “Life, Earth and Environmental Sciences” Programme. The attendees will learn about the probabilistic underpinnings behind popular inferential methods, while also applying these methods on practical, real-world examples, using the R programming language. We will especially focus on large-scale datasets, often involving a high number of variables. The students will learn how to use advanced statistical techniques, while also obtaining an understanding of the assumptions underlying these methods, as well as their scope and limitations. First, the students will be exposed to the principles behind frequentist and Bayesian inference. Then, we will introduce the students to supervised learning methods, including regression models, mixed models, shrinkage methods and support vector machines. This will be followed by a section on unsupervised learning, including PCA, MDS and clustering. Finally, we will provide a broad overview of advanced methods, including deep learning and random forests, in various scientific

The course is broadly meant for students in life, earth and/or environmental sciences who aim to develop their statistical and computational toolbox, in order to be able to tackle large-scale datasets. Students should have some background in basic probability, statistical inference and/or data science.

Course prerequisites
1. A basic understanding of probability theory and distributions.
2. The student must have taken the “Fundamentals in Large-Scale Data Analysis” course OR the student must have a waiver - by demonstrating their knowledge of the contents of the basic data analysis course.

Please take a look at the learning objectives of the “Fundamentals in Large-Scale Data
Analysis” course for details on what skills the student is expected to have at the end of that

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


Lectures interspersed with discussions and group work involving computational exercises in R and the unix console.

Course directors
- Fernando Racimo, Associate Professor, University of Copenhagen, fracimo@sund.ku.dk
- Shyam Gopalakrishnan, Associate Professor, University of Copenhagen, shyam.gopalakrishnan@sund.ku.dk

- Shyam Gopalakrishnan (course director)
- Fernando Racimo (course director)
- Martin Sikora, Associate Professor, KU
- Gabriel Renaud, Associate Professor, DTU
- Hannah Owens, Postdoc, KU
- David Duchene, Postdoc, KU

Teaching assistants
- Graham Gower, Postdoc, KU
- Evan Irving-Pease, Postdoc, KU
- Julian Regalado Perez, Postdoc, KU

Block 4 - 2 weeks – 26 April to 7 May 2021 (Except 30 April), all days 09:00-14:30

Course location
Teaching rooms in EvoGenomics - Kommunehospital

Please register before March 29th 2021.

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.

Course books:
- Statistical Thinking from Scratch - MD Edge
- An Introduction to Statistical Learning - James et al.

Further reading:
Probability, Statistics and Machine Learning
- Elements of Statistical Learning - freely available online: https://web.stanford.edu/~hastie/ElemStatLearn/printings/ESLII_print12_toc.pdf
- Statistical Inference - Casella and Berger
- Bayesian Data Analysis - Gelman et al.
- Machine Learning: A probabilistic perspective [ select chapters ]

The R programming language
- R in action - Kabacoff
- R for data science - Wickham & Grolemund

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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