Advanced Applied Statistics (Autumn 2023)

Abstract

This course introduces fundamental and advanced concepts in statistics and probability from a data-science perspective. The aim of the course is for the student to be familiarised with probabilistic and statistical methods that are widely used in data analysis.

Description

The aim of the course is to enable the student to work systematically with data sets with several variables which is important in regard to performing statistical analyses in data science. The course builds on the knowledge acquired in courses such as “Applied statistics” and “Machine Learning” and intends to give the student additional tools to identify, and solve statistical problems.

Formal prerequisites

• The prerequisites required for admission to the course are Linear Algebra and Optimisation or equivalent (vectors and matrices, eigendecomposition, univariate calculus) and Applied Statistics or equivalent (basic probability theory, expectation and variance, univariate distributions, data presentation and visualisation).
• Students must be able to programme. The default language is Python, but other languages are possible.

Intended learning outcomes

After the course, the student should be able to:

• Analyze statistical problems and reason about the most appropriate methods to apply
• Apply and reflect on advanced applied statistical methods
• Identify and describe problems that can be solved using multivariate techniques
• Implement basic statistical algorithms and interpret results
• Summarize the results of an analysis in a statistical report

Learning activities

The course consists of lectures and seminars ending with a project for the last part of the course. Classes will consist of lectures, seminars, independent programming exercises and discussion sessions.
The default language is Python, but other languages are possible.

For the final project you will specify and work on a relevant project of your choice. In this project you will apply the techniques and algorithms studied during the course on relevant problems. Besides the hours planned for lectures, seminars, tutorial, and exercise, supervision sessions for the projects are planned which complement the theory covered during the lectures and are necessary for meeting the learning objectives of the course. Short lectures will provide theoretical foundations and walk-through examples of relevant data mining algorithms while programming exercises will focus on students discussing, applying, and implementing the central algorithms themselves. 

Course literature

Bradley Efron and Trevor Hastie. Computer-age statistical inference. Student edition. Cambridge University Press, 2021.

A free PDF of the 2016 edition (which is identical to the 2021 student edition except for the lack of exercises) can be found here:
https://hastie.su.domains/CASI/

The exercises are also available as a separate download from the book website:
https://hastie.su.domains/CASI_files/PDF/CASI_Exercises.pdf

Additional readings will be assigned as needed.

Student Activity Budget

Estimated distribution of learning activities for the typical student

• Preparation for lectures and exercises: 20%
• Lectures: 20%
• Exercises: 30%
• Project work, supervision included: 30%

Ordinary exam

Exam type:
D: Submission of written work with following oral, External (7-point scale)
Exam variation:
D2G: Submission for groups with following oral exam supplemented by the submission. Shared responsibility for the report.

reexam

Exam type:
D: Submission of written work with following oral, External (7-point scale)
Exam variation:
D22: Submission with following oral exam supplemented by the submission.

Time and date

Ordinary Exam - submission Thu, 4 Jan 2024, 08:00 - 14:00
Ordinary Exam Mon, 22 Jan 2024, 09:00 - 21:00
Ordinary Exam Tue, 23 Jan 2024, 09:00 - 21:00