This is the place where all the files needed for our tutorials will be linked. Additionally, here you will always find any important information regarding our classes.
The first thing to do is to get familiar with the ‘Materials’ section.
If you have any questions or encounter any problems, you can contact me via email through the icon at the bottom of the page
The final deadline for your projects will be at the end of the
semester, but it is advisable to get started as soon as possible. I will
provide you with all the project details at a later date. For now,
concentrate on choosing your project topic and locating a suitable
dataset in .csv or .xlsx format, which is the
most user-friendly option. Think about what genuinely interests you and
what you can effectively present through data in the form of a brief
article with a model and visualizations. Ensure your article addresses a
specific problem or problems rather than merely presenting the dataset.
Below, you’ll find some sources for datasets that might assist you in
finding a suitable one:
Through the link below, you can download the template for the project assignment, along with all of the requirements. The deadlines will be published here at a later time.
As some of you had a problem with the installation of the rethinking package, I am sharing instructions on how to do it in this file:
Thank you for your hard work and dedication! It wasn’t an easy subject, but one that gave you practical abilities to understand data and scientific, Bayesian inference. Congratulations to all the people who passed, especially to those who were able to collect more than 100 points. I would love to give you a grade higher than 5, but it’s not technically possible 😅
If you didn’t pass and still aspire to achieve that, contact me and we will figure out how to solve it before the exam session ends.
Also, if you think that something is wrong with the points, let me know.
The total summary of the points can be found here:
In this section, there will be tutorial materials used in our classes.
In this section, I provide some introductory remarks and links to materials worth reading or watching. They are arranged in order from basic to more advanced levels. These materials cover mostly what you will learn in our classes, so they might be very helpful if you miss a tutorial or two.
I strongly recommend installing R and RStudio on your personal computer and becoming familiar with them.
To set up the installation, follow this tutorial:
To learn the very basics of using R and RStudio, which were covered in our first meeting, you can watch the following videos:
Additionally, you can follow a very useful R tutorial in the swirl package. You can find more information about swirl here:
I recommend starting with this part:
1: R Programming: The basics of programming in R
Furthermore, if you really want to delve into R basics, I recommend reading the relevant chapter from Kruschke’s book, “Doing Bayesian Data Analysis”: