Curricular Unit:Code:
Algorithms and Data Structures I1093AED1
2UndergraduateComputer Systems Engineering6 ects
Learning Period:Language of Instruction:Total Hours:
Winter SemesterPortuguese/English78
Learning Outcomes of the Curricular Unit:
Upon successful completion of this course unit students should be able to (learning outcomes - LO):
LO1-Understand and use the main data structures used in algorithms
LO2-explain and analyze algorithmic complexity
LO3-explain the importance of algorithm design and its impact on performance
LO4-apply principles of algorithmic efficiency in particular cases
LO5-apply search techniques to strings like KMP
LO6-Identify and use elementary sorting methods
LO7-Identify and use sort methods such as merge sort and quick sort
LO8-Identify and use abstract data types such as stacks, queues, and priority queues
1. Fundaments of Algorithms and Data Structures
1.1. Algorithm representation and programming models
1.2. Introduction to linear and nonlinear data structures
1.3. Analysis of algorithmic complexity
1.4. Introduction to Algorithmic Design Techniques
1.5. Applications and case studies
2. Text processing algorithms (Strings)
2.1. Introduction to operations with strings
2.2. Sorting operations
2.3. Search operations
3. Elementary Data Structures
3.1. Abstract Data Types (ADT)
3.2. Arrays
3.3. Linked Lists
3.4. Stacks
3.5. Queues
4. Sorting
4.1. Introduction to the sorting problem
4.2. Elementary sorting methods
4.3. Merge Sort
4.4. QuickSort
4.5. Heaps, priority queues and Heap Sort
Demonstration of the Syllabus Coherence with the Curricular Unit's Objectives:
The syllabus presented are consistent with the learning objectives of the curricular unit since there is a large convergence between the table of contents and the knowledge that the student is supposed to acquire in each of the program topics.
The fundamental concepts of algorithmic analysis and design are presented in the introductory chapter, in the following chapters are presented various sorting and searching algorithms and linear data structures and strings.
The learning objectives are achieved by supplementing the theoretical concepts with concrete examples and exercises run in lab environment using appropriate software
Teaching Methodologies (Including Evaluation):
This Course Unit (UC) is classified as a Project and contains core competences that are not subject to examination. There are elements of continuous assessment whose weighted average is required to be positive, the Practical Score of Continuous Assessment (NPAC)
Assessment results:
a) Student achieves minimum goals (NPAC >= 9.5 values) and a positive Final Grade (NF1 >=9.5 values) in continuous assessment. Approves the UC with NF1
b) Student achieves minimum goals (NPAC >= 9.5 values) and (NF1 < 9.5 values). Can be assessed on examination. Exam assessment is independent of continuous assessment. UC Final Grade is NF2
c) Student does not achieve minimum goals (NPAC < 9.5 values). Does not pass the UC and will not be able to access the exam
Expected assessment elements:
1. Test 1
2. Test 2
3. Practical project
4. Exam
Continuous Assessment Model:
NPAC = (3)
NF1 = ((1) + (2) + NPAC)/3, NPAC >= 9.5
Exam Assessment Model (NPAC >= 9.5):
NF2 = (4)
Demonstration of the Coherence between the Teaching Methodologies and the Learning Outcomes:
The teaching/learning methodology applied in this curricular unit as well as its evaluation system is perfectly aligned with the objectives to be attained by the students at the end of the term. The theoretical concepts are presented, discussed, applied and evaluated in the context of lectures, which guarantees students a solid foundation to understand the challenges facing this area of knowledge. On the other hand, so that the study is not restricted to conceptual models, in the practical lessons are presented concrete case studies and implemented solutions for real problems using appropriate software. This combination guarantees training for students that allows them to meet the scientific goals, essential to a good understanding of the theme, as well as the ability to adapt to technological changes. The evaluation process consists of theoretical tests and practical work also guarantees a correct balance between the efforts dedicated to both components. The objective is to train professionals’ specialized in state-of-the-art techniques and tools but also ensure its ability to follow future developments. In this curriculum unit the problem of algorithms and data structures are presented and evaluated in theoretical component. These concepts are then applied in the resolution of the worksheets and practical work in the context of practical classes
[CLRS] Thomas H. Cormen, Leiserson, C., Rivest, R., & Stein, C. (2009). Introduction to Algorithms, third edition. MIT Press.
[KT] Kleinberg, J., & Tardos, E. (2006). Algorithm Design. Pearson Education.
[SW] Sedgewick, R., & Wayne, K. (2011). Algorithms, 4th Edition. Pearson Education.
[VSC] Vasconcelos, J., & Carvalho, J. V. de. (2005). Algoritmia e Estrutura de Dados: programação nas linguagens C e Java. Editora Centro Atlântico.
[AAR2] Rocha, A. A. da. Estruturas de Dados e Algoritmos em Java. FCA - Editora de Informática, ISBN 978-972-722-704-4.
[AAR1] Rocha, A. A. da. (2008). Estruturas de Dados e Algoritmos em C. FCA - Editora de Informática
Lecturer (* Responsible):
Célio Carvalho (
José Manuel Torres (