Welcome to Advanced Applied Stats & Multivariate Calculus

We will teach you stats using a learning-by-doing approach.
Classes will use a combination of discussion groups, exercises, seminars, lectures, feedback sessions and much more.

Good ideas 💡

No one is born as a statistician, and learning statistics can be hard - especially during COVID. That's why forming a community is critical for this course. We encourage you to collaboratively take notes (e.g. using Google Drive) and establish channels to work together. For any questions about the course contents or organisation, the exam modalities or anything that isn't personal and confidential, please use the course forum (don't email the teachers).

Giving us feedback & calling us out

If you at any time feel:
 - there is something we can improve
 - something we said/did made you feel uncomfortable
 - something happened which you think we need to address in class
Please let us know!

You can always write to Christian (chrha@itu.dk) or use this form 👉 https://forms.gle/Pe8nExNXC7MbMFreA to give us anonymous feedback

Syllabus & Books
No single book covers the entire syllabus of the course, as such the course will use excerpts from multiple books & articles. Therefore you will not need to buy a course book.
Nonetheless, a good book to have is The Elements of Statistical Learning by Hastie, Tibshirani and Friedman. You can download a free copy here.

Section: MSc in Data Science
Section: MSc in Data Science

This course is a seminar-based overview of recent research on data science application areas. This course consists of a series of research-based seminars in application areas of data science. The aim is to get an overview of recent research advances in data science areas, which are possible topics for students to specialize in later parts of their MSc studies. Guest lecturers will introduce a data science topic, provide recent research samples and stipulate discussion in that area.


Read the full description here: https://learnit.itu.dk/local/coursebase/view.php?ciid=812


Section: MSc in Data Science