TecnoCampus Foundation
eCampus
University Business
eCampus

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General information

Subject type: Mandatory

Coordinator: Vladimir Bellavista Parent

Trimester: Third term

Credits: 4

Teaching staff: 

Vladimir Bellavista Parent
Montserrat Estañol Lamarca 

Academic year: 2025

Teaching course: 1

Languages ​​of instruction

  • Catalan

Competencies / Learning Outcomes

Basic skills

  • B2_That students know how to apply their knowledge to their job or vocation in a professional way and have the skills they demonstrate by developing and defending arguments and solving problems within their area of ​​study

  • B5_That students have developed those learning skills necessary to undertake further studies with a high degree of autonomy

Specific skills

  • EFB3_Ability to understand and master the basic concepts of discrete mathematics, logic, algorithms and computational complexity, and their application for solving engineering problems

  • EFB4_Basic knowledge of the use and programming of computers, operating systems, databases and computer programs with application in engineering

Presentation of the subject

This subject closes the block of programming subjects in the first year.

We begin by studying the use of volatile data stores (in memory) most frequent: cue i Battery, lists, sets, maps..., while continuing the approach to the OOP started in the previous subjects. We continue with a brief introduction to the techniques of recursive programmingTo finish showing the use offiles as a non-volatile storage tool.

The classroom (physical or virtual) is a safe space, free of sexist, racist, homophobic, transphobic and discriminatory attitudes, whether towards students or teachers. We trust that together we can create a safe space where we can make mistakes and learn without having to suffer prejudice from others. 

Contents

1. Data collections

  • Sequential collections: Piles (Stack (Height)), cues (Queues), Lists (List)
  • Sets (Set)
  • Associative collections (Maps o Dictionaries)

2. Recursion

  • Recursive Algorithms Vs. iterative algorithms
  • Recursive sequence treatment. Strategies

3. Permanent storage: archives

  • I / O to and from files. Channels
  • Text files
  • Data files (binary)

Activities and evaluation system

The final grade for the subject (QF) will be calculated as detailed below

TEO: theory qualification, assessable through exams 

PRAC: internship qualification

QPTP: weighted rating TEO (65%) and PRAC (35%)

QF: final grade of the subject

 

QPTP = TEO x 0,65 + PARC x 0,35

si TEO <5 keys QF = TEO

si TEO > = 5 keys QF = QPTP

 

Recovery

The practical part of the subject (qualification practices) It is NOT recoverable..

To attend the retake it is necessary to have taken the final exam. 

For students attending therecovery exam its theory qualification (TEO) will be the one obtained in this retake test and your final grade (QF) will be calculated with the formulas detailed above.

 

Considerations on content authorship and the use of generative AI

1.- In general, Tecnocampus establishes: any form of academic fraud will be sanctioned in accordance with the center's evaluation regulations. In the event that signs of fraud are detected, including the improper use of generative artificial intelligence tools, the subject's teaching staff may call the student for an individual interview with the aim of verifying their authorship.

2.- In the particular case of this subject, given its fundamental nature, the student is required not only to provide solutions to certain problems, but also to be able to generate them autonomously, without any external help. For this reason, the use of generative artificial intelligences (IAG) to resolve the problems posed in the subject —whether in exercises, practices or tests— is counterproductive, is strictly prohibited and will be considered a case of fraud by plagiarism. In this sense, the use of IAGs to generate programming code is not allowed, not even in the form of fragments, even if this code is later modified or personalized. The critical use of IAGs as a vehicle to resolve doubts about the subject is not considered a misuse of these mechanisms as long as this does not contradict what has been indicated previously and the student does not lose sight of the fact that he may obtain incorrect answers and/or not adjusted to the contents of the subject. Failure to comply will result in a grade of NP in the subject - without the right to retake.

3.- It is the student's responsibility to avoid plagiarism in all its forms. In the context of this subject, plagiarism also means using or adapting code that has not been developed entirely individually (or within the group in the case of group activities). Providing code that gives rise to plagiarism is also a form of plagiarism and will be treated in the same way. In summary, we can say that the evaluation activities must be resolved in a strictly non-collaborative manner (in the case of group activities, collaboration cannot extend beyond the group). In the event of plagiarism being detected, appropriate measures will be taken and will have an effect on all parties involved.

Bibliography

Basic

Java® Platform, Standard Edition & Java Development Kit Version 24 API Specification. https://docs.oracle.com/en/java/javase/24/docs/api/index.html

Sesa and Nogueras. EDA: class notes, examples and exercises. Internal publication of the TCM.

Complementary

Trail: Collections (The Java™ Tutorials) https://docs.oracle.com/javase/tutorial/collections/