
Introduction to Algorithmics
Code: 107737Credits: 6
| 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.
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 |