Python programming I+II The purpose of this course is to teach general programming concepts using the Python language as an instruction tool. Instructor: András Aszódi. Topics The course consists of two parts that are taught on different dates. Part I In the first part we learn the basics of Python programming. Introduction to Python: basic […]

UNIX command line This course introduces the UNIX command line on a Linux or Mac. It serves as a foundation of our Bash scripting and CBE HPC cluster introduction courses. The Regular expressions course also builds on UNIX command line knowledge. Instructor: András Aszódi. Topics UNIX overview: general principles. Working with files and directories. Users, […]

CBE HPC cluster introduction This course provides an introduction to the CBE ("CLIP Batch Environment") high-performance computing cluster which is part of the Vienna Biocenter Cloud Infrastructure Project. Instructor: András Aszódi. Topics Introduction to the cluster: architecture, storage layout, access. Environment modules. Batch mode: SLURM queuing system, job scripts. Job arrays, task arrays, MPI jobs. […]

Quick introduction to data analysis with R The aim of this lecture is to give a very short overview on how to do data analysis with R. It does not replace our in-depth courses such as R as a programming language and Basic statistics with R. Instructor: András Aszódi. Target audience This lecture is presented […]

Python programming I+II The purpose of this course is to teach general programming concepts using the Python language as an instruction tool. Instructor: András Aszódi. Topics The course consists of two parts that are taught on different dates. Part I In the first part we learn the basics of Python programming. Introduction to Python: basic […]

Python programming I+II The purpose of this course is to teach general programming concepts using the Python language as an instruction tool. Instructor: András Aszódi. Topics The course consists of two parts that are taught on different dates. Part I In the first part we learn the basics of Python programming. Introduction to Python: basic […]

R as a programming language This course is aimed at colleagues who would like to learn how to use the statistical programming language "R". We will learn about the data structures and functions in R, and how to write R scripts. Instructor: András Aszódi. Topics Introduction to R: general principles. Data structures in R: vectors, […]

Probability Theory with R The main aim of this course is to teach you the basics of probability theory which is indispensable for any scientific investigation. We will focus on the intuition behind probability theory rather than a mathematically rigorous presentation. Yes, there will be equations, but they will be kept to a minimum. Instructor: […]

Think Statistics with R "It's easy to lie with statistics. It's hard to tell the truth without it." — Andrejs Dunkels, Latvian-Swedish mathematician The main aim of this course is to teach you to how to approach data analysis problems with classical statistics. We focus on the intuition behind statistical methodologies rather than on "how […]

Advanced plotting with ggplot in R The aim of this course is to teach you how to create informative scientific plots using the ggplot package in R. Instructor: András Aszódi. Topics Fundamentals of scientific visualisation. Introduction to ggplot. Data preparation, plot creation workflow. ggplot details: bars, boxes, densities, function plots, faceting. Prerequisites Basic familiarity with […]

CBE HPC cluster introduction This course provides an introduction to the CBE ("CLIP Batch Environment") high-performance computing cluster which is part of the Vienna Biocenter Cloud Infrastructure Project. Instructor: András Aszódi. Topics Introduction to the cluster: architecture, storage layout, access. Environment modules. Batch mode: SLURM queuing system, job scripts. Job arrays, task arrays, MPI jobs. […]

Bayesian Statistics with R The Bayesian framework represents an alternative approach to statistical modelling. Bayesian analysis describes how to update our initially incomplete "prior" knowledge with experimental observations (the "evidence") to obtain a better understanding (the "posterior") of the phenomenon studied. This concept fits very well the way how scientific research works. In addition, Bayesian […]