Algorithmics and Programming I

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Credits
7.5
Types
Compulsory
Requirements
This subject has not requirements, but it has got previous capacities
Department
CS
The course presents the elements of a computer programming language and the algorithmic basis for working with scaled and structured data. During the course the knowledge will be acquired to deal with problems of small and medium complexity.

Teachers

Person in charge

  • Jordi Petit Silvestre ( )

Others

  • Jordi Puig Rabat ( )

Weekly hours

Theory
3
Problems
0
Laboratory
2
Guided learning
0.5
Autonomous learning
7

Competences

Technical Competences

Technical competencies

  • CE2 - To be able to program solutions to engineering problems: Design efficient algorithmic solutions to a given computational problem, implement them in the form of a robust, structured and maintainable program, and check the validity of the solution.

Transversal Competences

Transversals

  • CT5 - Solvent use of information resources. Manage the acquisition, structuring, analysis and visualization of data and information in the field of specialty and critically evaluate the results of such management.
  • CT6 [Avaluable] - Autonomous Learning. Detect deficiencies in one's own knowledge and overcome them through critical reflection and the choice of the best action to extend this knowledge.
  • CT7 - Third language. Know a third language, preferably English, with an adequate oral and written level and in line with the needs of graduates.

Basic

  • CB5 - That the students have developed those learning skills necessary to undertake later studies with a high degree of autonomy

Generic Technical Competences

Generic

  • CG1 - To design computer systems that integrate data of provenances and very diverse forms, create with them mathematical models, reason on these models and act accordingly, learning from experience.
  • CG2 - Choose and apply the most appropriate methods and techniques to a problem defined by data that represents a challenge for its volume, speed, variety or heterogeneity, including computer, mathematical, statistical and signal processing methods.
  • CG5 - To be able to draw on fundamental knowledge and sound work methodologies acquired during the studies to adapt to the new technological scenarios of the future.

Objectives

  1. Be able to solve small and medium complexity calculation problems using algorithmic and programming techniques.
    Related competences: CB5, CT5, CT6, CT7, CE2, CG1, CG2, CG5,

Contents

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Activities

Activity Evaluation act


consultar la versió en català

consultar la versió en català
Objectives: 1
Theory
6h
Problems
0h
Laboratory
4h
Guided learning
0h
Autonomous learning
12h

consultar la versió en català

consultar la versió en català
Objectives: 1
Theory
6h
Problems
0h
Laboratory
4h
Guided learning
0h
Autonomous learning
12h

consultar la versió en català

consultar la versió en català
Objectives: 1
Theory
6h
Problems
0h
Laboratory
4h
Guided learning
0h
Autonomous learning
12h

consultar la versió en català

consultar la versió en català
Objectives: 1
Theory
3h
Problems
0h
Laboratory
2h
Guided learning
0h
Autonomous learning
6h

consultar la versió en català

consultar la versió en català
Objectives: 1
Theory
6h
Problems
0h
Laboratory
4h
Guided learning
0h
Autonomous learning
12h

consultar la versió en català

consultar la versió en català
Objectives: 1
Theory
6h
Problems
0h
Laboratory
4h
Guided learning
0h
Autonomous learning
12h

consultar la versió en català

consultar la versió en català
Objectives: 1
Theory
6h
Problems
0h
Laboratory
4h
Guided learning
0h
Autonomous learning
12h

consultar la versió en català

consultar la versió en català
Objectives: 1
Theory
6h
Problems
0h
Laboratory
4h
Guided learning
0h
Autonomous learning
12h

Lab test



Week: 7 (Outside class hours)
Type: lab exam
Theory
0h
Problems
0h
Laboratory
0h
Guided learning
2h
Autonomous learning
3h

Lab test



Week: 15 (Outside class hours)
Type: final exam
Theory
0h
Problems
0h
Laboratory
0h
Guided learning
2h
Autonomous learning
2h

Theory test



Week: 15 (Outside class hours)
Type: final exam
Theory
0h
Problems
0h
Laboratory
0h
Guided learning
2h
Autonomous learning
10h

Teaching methodology

The theoretical contents of the subject are taught in theory classes. These classes are complemented by practical examples and problems that students must solve in the hours of Autonomous Learning.

The laboratory sessions consolidate the knowledge acquired in the theory classes by solving programming problems related to the theoretical contents. During the laboratory classes, the teacher will introduce new techniques and leave an important part of the class for the students to work on the proposed exercises.

Evaluation methodology

There are two tests that are done in the lab: a partial (PL) and a final (FL). There is also a final written exam (FT).

The FINAL grade is calculated according to the formula:

0.6 max {0.3 PL + 0.7 FL, FL} + 0.4 FT.


The REVALUATION grade is calculated according to the formula:

0.6 RL + 0.4 RT

where RL is the grade for the laboratory exam in the re-assessment and RT is the grade for the theory exam in the re-assessment.

Bibliography

Basic:

Web links

Previous capacities

The student must have the knowledge of mathematics and computational reasoning acquired at the Baccalaureate level.