- Programming - Calculus

Competences CECRI1 (1 ECTS) Evidence Assessment: PRACTICE LABORATORY, WORK TEAM (CASE) CECRI6 (Algorithms) (1.5 ECTS) Evidence Assessment: TESTING, LABORATORY PRACTICE, GROUP AND INDIVIDUAL WORK CECRI7 (TYPES AND DATA STRUCTURES) (2.5 ECTS) Evidence Assessment: TESTING, LABORATORY PRACTICE, GROUP AND INDIVIDUAL WORK CGB4 (0.50 ECTS) Evidence Assessment: PRACTICE LABORATORY TEAM WORK CGB% (0.50 ECTS) Evidence Assessment: LABORATORY PRACTICE, GROUP WORK - Capacity to analyze and synthesize (PO e). - Capacity to organize and plan the work (PO d). - Resolution of problems (PO e). - Working as a team (PO d). - Capacity to put in practice theory knowledge (PO e). 2. Specific Competences. a. Cognitive (to know). - General knowledge about algorithms (PO a). - Understanding of basic data structures (PO k). - Familiarity with advanced data structures (PO k). b. Procedural/instrumental (to be able to do). - To be able to design and analyze the algorithms complexity (PO a). - To be able to understand and use different data structures (PO k). - To be able to implement program solutions to specific problems using these tools (PO e). c. Attitude (Being) - Ability to solve problems through algorithms (PO e). - Ability to clarify, simplify and efficiency of solving problems (PO e and k). - Ability to question and conclude various solutions to any problem (PO e and k). The learning outcomes are: Individual work: ¿ Resolution by students of problems that must prove they have the ability to combine theory and practice. (PO a, e, k) Group work: Case Study on design and implementation of data structures. (PO a, d, e, k)

1. Introduction to Abstract Data Type 2. Linear Abstract Data Types b. Stacks c. Queues. d. Lists: singly and doubly linked lists 3. Algorithms I: Complexity a. Analysis of Algorithms b. Types of complexity c. Function Time. d. Notation Big-O. e. Worst and best cases. 4. Algorithms II: recursion. 5. Hierarchic Abstract Data types: Trees a. General Trees b. Binary Trees c. Tree Trasverse: preorder, inorder, postorder d. Search Binary Trees. e. Balanced BST. 6. Graphs a. Definition Graph ADT. Applications b. Implementation based on adjacency matrix. c. Implementation based on adjacency list. d. Graph trasversal: Depth-first search and breadth-first search. e. Dijkstra's algorithm 7. Divide and Conquer

1. Theory Lectures with the objective of acquire the cognitive specific competences (PO a and k). 2. Academic activities guided by the teacher: 2.1. With the teacher: to solve exercises devoted to analyze, design and implement cases with different level of complexity in collaboration with students (PO a and k). Some of the exercises will be carried out in computer laboratories (PO k). 2.2. Student work: Homework, individually or cooperatively, with exercises, implementation cases and basic readings from bibliography proposed by the teacher (PO k and e). Moreover, these activities can be performed as: a. Individual work consisting on developing solutions to the problems and exercises posed by the teacher. b. Working cooperatively developing solutions to the problems proposed by the teacher (PO d). 3. Mid-term partial exam and final exam (PO a, e, k). 4. There will be a group tutorship for each small group to solve the queries and doubts of students.