In this talk, I will discuss the importance of the FAIR principles for the software tools we use to process data. Ranging from small analysis scripts to full fledged data processing pipelines, software needs to be FAIR to enable other researchers to reproduce our own experiments and reuse our...
R is a popular language and environment that allows powerful and fast manipulation of data, offering many statistical and graphical options. This course aims to introduce R as a tool for statistics and graphics, with the main aim being to become comfortable with the R environment. It will focus...
- Programming basics
- Python data structures
- Functions part 1
- Functions part 2 (main, modules and variable scope)
- Reading and writing files
- Exception and documentation
- Plotting graphs
- Data bases part 1
- Data bases part 2
- XML and Web...
Scientific topics: Software engineering
Python is a flexible programming language that is becoming increasingly popular for scientific computing. The course is is split into 12 modules and runs over two half days. At the end of each module there a number of exercises to help solidify the learning. By the end of...
R is a programming language and associated environment developed for statistical computing and data analysis. It provides many powerful tools for statistics, data visualisation and bioinformatics.
- What R is suitable for.
- How to use R...
Perl is a programming language for getting your job done. It is designed to make the easy jobs easy, without making the hard jobs impossible. Perl is also well known for BioPerl, a collection of modules which greatly simplify complicated bioinformatics tasks.
The purpose of this training is to teach general programming concepts using Python as an instruction tool.
Introduction to Python: basic principles.
Python data structures: strings, tuples, lists, dictionaries, sets.
Object-oriented programming: how to model coffee machines in Python...