Algorithmics and Programming I

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

Others

Weekly hours

Theory
3
Problems
0
Laboratory
2
Guided learning
0
Autonomous learning
7.5

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)
    Theory
    0h
    Problems
    0h
    Laboratory
    0h
    Guided learning
    0h
    Autonomous learning
    0h

    Lab test



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

    Theory test



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

    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.