Scientific programming

Seminar lectures and practicals in informatics room on the following topics:

Python programming language:
- Quick revision on:
- Variables, Expressions and Statements
- Strings, Lists, Tuples, Dictionaries and indexes
- Functions and Classes
- Functions of second order for manipulation of lists and data collections
- Principles of object programming
- Simple Abstract Data Types: Stacks, Queues, Trees and Graphs
- File management
- Libraries for the management of matrices and tables (e.g. Biopython, Pandas and NumPy). Common statistical libraries (e.g. SciPy).
- Techniques of data visualization through graphs and curves.
- Biopython tools and functions.
- Building Python libraries.
- Integration of informatics systems: hints of REST Web services and Web service invocation from Python

Practicals on Phyton implementations of dynamic programming, statistical analyses of Next Generation Sequencing data, and/or others.


R programming language:
- Main data structures in R: vectors, factors, matrices, arrays, lists and environments
- Control of execution flow: blocks, conditional statements, loops
- Functions and scripts
- Input/Output functions and operators; R data import/export
- Graphical representation of the data, heatmaps, boxplots and Venn diagrams
- Vector operations
- Packages and R "extensions"
- Building packages in R and Bioconductor
- Analyzing Next Generation Sequencing data with R and Bioconductor packages

Practicals on R implementation of statistical analyses of gene expression data (differentially expressed genes, clustering, principal component analysis, and/or others).