## Applied High-Throughput Analysis

In the PDF file Prerequisites course AHTA, you can find basic knowledge and some links referring to basic statistical topics required for this course. Note that you only need to understand these topics from a conceptual point of view: What do they do? Why/how are they used? Which kind of assumptions do they make?

#### Additional information

Crucially, for those struggling/getting started with R, please have a closer look at the links provided with the course of Statistical Genomics below.

For more background on epigenetics: see https://en.wikipedia.org/wiki/Epigenetics (of special interest are: 1.2 - Contemporary; 3 - Mechanisms, particularly first half of 3.1)

For students who lack hands-on-expertise with Linux, an excellent introduction can be found here: https://missing.csail.mit.edu/.

## Statistical Genomics

On the website https://statomics.github.io/SGA/ you can find all information regarding this course. The prerequisites are the successful completion of a basic course of statistics that covers topics on data exploration and descriptive statistics, statistical modeling, and inference: linear models, confidence intervals, t-tests, F-tests, anova, chi-squared test. The basis concepts may be revisited in the online course at https://statomics.github.io/PSLS/ (English) and https://statomics.github.io/sbc21/ (Dutch).

In addition, knowledge of programming in R is preferred. A primer to R and Data visualization in R can be found at:

- R Basics: https://dodona.ugent.be/nl/courses/335/
- R Data Exploration and Visualization: https://dodona.ugent.be/nl/courses/345/

## Predictive modelling

In the PDF file Prerequisites course Predictive Modelling, you can find for each lecture which basic knowledge is required and corresponding Wikipedia links. In addition, basic programming skills, in particular Python (e.g. https://dodona.ugent.be/), are required.

## Selected Topics in Mathematical Optimization

Following is required:

- Good knowledge of basic linear algebra and calculus (matrices, differential calculus, integral calculus, ...)

- Knowledge of basic probability theory (distribution functions, moments, Bayes’ rule, ...)

- A basic knowledge of (scientific) programming in one of the following languages: C, Python, Julia. (Knowledge of Python: Numpy/Scipy is an advantage). People that have no programming experience are advised to look into introductory courses of Python (e.g. https://dodona.ugent.be/).

Prior knowledge of numerical algorithms is advantageous but not necessary.

## Linux

Having some idea about basic concepts in Linux can be helpful, not only for the course “Programming for Bioinformatics” but also for other courses and tasks.

Here is the link to the course of Prof. Fostier: https://github.ugent.be/jfostier/DP.

An excellent introduction can also be found here: https://missing.csail.mit.edu/.