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Introduction to Algorithmics

Code: 107737
Credits: 6
2026/2027
Degree programme Type Course
Interactive Communication FB 1

Contact lecturer

Name :
Juan Carlos Sebastián Pérez
Email :
juancarlos.sebastian@uab.cat

Group languages

You can consult this information at the end of the document.

Prerequisites

No prerequisite needed

Objectives

1- Learn the basics of computer scicence

2- Get startemsd with algorithms

Learning outcomes

  • CM09 (Apply the principles of algorithms and programming to proposals for interactive communicative projects within the framework of a professional practice that is sensitive to society’s problems and challenges.) Apply the principles of algorithms and programming to proposals for interactive communicative projects within the framework of a professional practice that is sensitive to society’s problems and challenges.
  • KM07 (Explain the basic conceptual and practical concepts, languages and application of algorithms and programming in a programming environment.) Explain the basic conceptual and practical concepts, languages and application of algorithms and programming in a programming environment.
  • SM08 (Use programming principles to turn a specific problem into a programme that solves it.) Use programming principles to turn a specific problem into a programme that solves it.
  • SM09 (Know the defining elements of an information system.) Know the defining elements of an information system.

Contents

1- Introduction to computer science


2- Introducción with algorithms

Learning activities and methodology

Title Hours ECTS Learning outcomes
Report deliveries 20 0.8 CM09, KM07
Lab practices 20 0.8 CM09, KM07
Teorethical exams 10 0.4 CM09, KM07
Attendance 10 0.4 CM09, KM07

Note: The course content will be sensitive to issues related to gender perspective and the use of inclusive language


A detailed schedule outlining the content of each session will be presented on the first day of the course and will be available on the course’s Virtual Campus, where students will find the various teaching materials deemed appropiate by the instructors and all teaching materials and necessary information for effective course monitoring. Should the teaching modality change for reasons of force majeure according to the competent authorities, the teaching staff will inform students of any modifications to the course schedule and teaching methodologies.

Annotation: within the schedule set by the centre or degree programme, 15 minutes of one class will be reserved for students to evaluate their lecturers and their courses or modules through questionnaires.

Assessment

Continuous assessment activities

Title Weight Hours ECTS Learning outcomes
Attendance 10% 40 1.6 CM09, KM07, SM08, SM09
Teorethical exams 50% 10 0.4 CM09, KM07, SM08, SM09
Lab practices 20% 20 0.8 CM09, KM07, SM08, SM09
Report deliveries 20% 20 0.8 CM09, KM07, SM08, SM09

Students must score above 5 in the exams in order to pass the course.


This course/module does not provide for a single-assessment system.


Students will be entitled to reassessment in the course if they have been evaluated on a set of activities accounting for at least two-thirds of the total course grade.

In this course, the use of Artificial Intelligence (AI) technologies is not permitted at any stage. Any assignment containing content generated by AI will be considered a breach of 5 academic integrity and may result in a partial or total penalty to the assignment grade, or more serious sanctions in severe cases.


The performance of any irregularity in an evaluation act (academic fraud, plagiarism or improper use of AI, unless this use is expressly authorized by the teaching guide), which may lead to a significant variation in the grade, assumes that this act will be graded with a 0. In the event that the teaching guide foresees that in order to pass the subject it is an essential requirement to have obtained a minimum grade in this evaluation act or that there are several irregularities in the evaluation acts of the same subject, the final grade of this subject is 0. Apart from this, a disciplinary process may be instructed to the student that incurs a y of these irregularities.


Any student suspected of submitting assignments that have been generated by AI,

written by others or copied; include unattributed AI-generated content, or fall

outside the permitted scope, may be asked to provide the preliminary work or

other materials that can demonstrate it is original work and the result of their own

authorship. They may also be asked to separately explain or justify their work.

Teachers may also use AI detection systems or carry out any verification tasks

they deem appropriate. If, after review, the instructor detects irregularities, the

assignment may be graded zero, and the student may be subject to further

disciplinary action.

Bibliography

https://pythoninstitute.org/

Software

Virtual campus

Course groups and languages

The information provided is provisional until November 30. After this date, you will be able to consult the language of each group through this link. To access the information, you will need to enter the course CODE

Type of teaching Group Language Semester Shift
(TE) Theory 6 Spanish first semester afternoon
(PLAB) Practical laboratories 61 Spanish first semester afternoon
(PLAB) Practical laboratories 62 Spanish first semester afternoon