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85285 - Programming

Academic Year 2021/2022

                
                        Docente:
                        Alessandro Amoroso
                    
                        Credits:
                        12
                    
                        SSD:
                        INF/01
                    
                        Language:
                        English
                    
                        Moduli:
                        
                            Alessandro Amoroso
                            (Modulo 1)
                        
                            Zeynep Kiziltan
                            (Modulo 2)
                        
                        Teaching Mode:
                        
                                    Traditional lectures (Modulo 1)
                                
                                    Traditional lectures (Modulo 2)
                                
                            Campus:
                            Bologna
                        
                            Corso:
                            First cycle degree programme (L) in
                            Genomics (cod. 9211)

                            Teaching resources on Virtuale

Learning outcomes

By the end of the course, the student knows the principles, the tools and the methodologies pertaining to computer programming. The successful student can find solutions to basic programming problems and to choose the best informatics tools to solve specific programming challenges. At the end of the hands-on lab, the student can program in at least one of the main languages used in bioinformatics.

Course contents

Introduction to Python. Simple numerical programs. Functions scoping and abstractions. Structured types, mutabilty and higher-order functions. Testing and debugging. Exceptions and assertions. Classes and object-oriented programming. A simplistic introduction to algorithmic complexity. Some simple algorithms and data structures. Dynamic programming. Stochastic programs, probability and distributions. Monte Carlo simulations. Sampling and confidence intervals. Understanding experimental data. Lies, damned lies, and statistics. Quick look at machine learning.