There are few things to do before the course starts. Depending on your background these may take some time even up to 40h so do read carefully through the list and allow plenty of time for preparations.

Three ToDo categories:

pre-course mathematical revisions incl. a short quiz with 23rd April, 21:00 submission deadline
setting up your computer
setting up communication channels

Pre-course math revisions

Biostatistics and Machine Learning are based on mathematics so it is time to revise the basic concepts.

Go to https://nbisweden.github.io/workshop-mlbiostatistics/session-precourse-math/docs/ and have a look at the 7 chapters covering basics of mathematical notations, sets, functions, differentiation, integration, vectors and matrices.
If you feel like you could use some practise: try solving the exercises at the end of each session before looking at the answers.
When you happy are with your revisions take the math quiz. You can only attempt the quiz once and the quiz submission deadline is April 23rd, 21:00
Note that you will get access to the quiz once you are enrolled to the course on Canvas. See below for "setting-up communication channels".

Setting-up environment

During the course we will be using R programming language within RStudio Desktop editor.

We will try to keep coding simple but we do assume that you have a basic understanding of R and your computer is set-up with R version 3.5 and R Studio Desktop.

We will be showing some example code using R Markdown (.rmd) or Quarto (.qmd) compatible with R Markdown. If you are not yet using notebooks to combine text and code together, we encourage you to check out Quarto tutorials at https://quarto.org/docs/get-started/ to learn the basics (do choose R Studio tutorial).

R skills that will be useful during the course are:

using R as calculator
being able to work with vectors and matrices, incl. subsetting and matrix multiplication
reading in data from .csv files, e.g. with read_csv()
printing top few rows or last few rows, e.g. with head() and tail()
using in-built summary functions such as sum(), min() or max()
being able to use documentation pages for R functions, e.g. with help() or ?()
using if else statements, writing simple loops and functions.
making simple plots (scatter plots, histograms), both with plot() and ggplot()
using tidyverse() for data transformations, e.g. filtering rows, selecting columns, creating new columns etc.
being able to install CRAN packages e.g. with install.packages()
being familiar with R Markdown format (.rmd) and/or Quarto (.qmd)

There is a self-test quiz available for practising and checking R skills (same quiz was was linked to the course application) R skills.

Some open resources to learn more R:

Statistical Inferences via Data Science, A modernDive into R and Tidyverse by Chester Ismay and Albert Y.Kim
R for Data Science by Hadley Wickham & Garrett Grolemund

R packages

In addition to base packages we will be using some packages that require installation. A preliminary list is here.

Setting-up communication

You should get an email invitation to the Canvas course few days after you receive the email with pre-course information. Check your inbox and spam folder for an invitation. Once on Canvas, do check that:

you can access the math quiz
you can see and post to "Discussions". Feel free to write few words under "Introduce yourself"

Questions

In case of any issues, do email us at edu.ml-biostats@nbis.se