Data Structures for Bioinformatics
| Keywords |
| Classification |
Keyword |
| OFICIAL |
Computer Science |
Instance: 2018/2019 - 2S
Cycles of Study/Courses
Teaching language
Suitable for English-speaking students
Objectives
Students should be able to use the fundamental data structures and associated basic algorithms, illustrating their application through the two most widely used programming languages for Bioinformatics (Python). Concepts of object-oriented programming will be introduced as well as some more common strategies in more advanced algorithms in bioinformatics.
Learning outcomes and competences
Capacity to analyze the complexity of common data structures and to implement them in Python.
Working method
Presencial
Program
Abstract data types: attributes and methods. Collections: linked lists, queues and stacks. Algorithmic complexity. Search and insertion algorithms: linear and binary search. Sorting algorithms. Binary trees: ordered and non-ordered. Binary search trees. Heaps. Depth-first-search and breadth-first-search.
Mandatory literature
Miller Bradley N.; Problem solving with algorithms and data structures using Python.. ISBN: 1-59028-053-0
Teaching methods and learning activities
Learning based on practical implementation of common problems.
Evaluation Type
Distributed evaluation with final exam
Assessment Components
| designation |
Weight (%) |
| Exame |
50,00 |
| Trabalho escrito |
20,00 |
| Teste |
30,00 |
| Total: |
100,00 |
Amount of time allocated to each course unit
| designation |
Time (hours) |
| Estudo autónomo |
42,00 |
| Frequência das aulas |
42,00 |
| Trabalho escrito |
30,00 |
| Elaboração de projeto |
54,00 |
| Total: |
168,00 |
Eligibility for exams
The weighted average of the written assignments (40%) and practical test (60%) should be more than 8 values.
Calculation formula of final grade
3 components: written assignments (20%), practical test (30%) and final exam (50%).