Introduction to Programming
Code: 104850 ECTS Credits: 6| Degree | Type | Year | Semester |
|---|---|---|---|
| 2503852 Applied Statistics | FB | 1 | 2 |
Contact
- Name:
- Vicenç Soler Ruíz
- Email:
- vicenc.soler@uab.cat
Use of Languages
- Principal working language:
- catalan (cat)
- Some groups entirely in English:
- No
- Some groups entirely in Catalan:
- Yes
- Some groups entirely in Spanish:
- No
Prerequisites
None
Objectives and Contextualisation
Learn to develop a computer program.
Create and design computer algorithms that allow the resolution of problems with structured programming.
Use the Python programming language as the language used to develop the exercises.
Competences
- Make efficient use of the literature and digital resources to obtain information.
- Students must be capable of applying their knowledge to their work or vocation in a professional way and they should have building arguments and problem resolution skills within their area of study.
- Students must have and understand knowledge of an area of study built on the basis of general secondary education, and while it relies on some advanced textbooks it also includes some aspects coming from the forefront of its field of study.
- Use quality criteria to critically assess the work done.
- Use software for statistical analysis, numerical and symbolic analysis, graphic visualisation, optimisation or others, to solve problems.
Learning Outcomes
- Critically assess the work done on the basis of quality criteria.
- Make effective use of references and electronic resources to obtain information.
- Students must be capable of applying their knowledge to their work or vocation in a professional way and they should have building arguments and problem resolution skills within their area of study.
- Students must have and understand knowledge of an area of study built on the basis of general secondary education, and while it relies on some advanced textbooks it also includes some aspects coming from the forefront of its field of study.
- Use Functional Programming.
Content
1. Introduction: variables, instructions, data types and algorithms
2. Conditionals and operators
3. Loops
4. Unidimensional and n-dimensional arrays: lists, dictionaries and tuples in Python
5. Functions and their parameters
6. Files
7. Classes
8. Design and development of an application
*Unless the requirements enforced by the health authorities demand a prioritization or reduction of these contents.
Methodology
Each theory session will have its practical session, where the students will be proposed to apply the concepts learnt developing some computer programs in Python.
The student will be provided of some notes with solved exercises that will help him/her follow the syllabus every week.
*The proposed teaching methodology may experience some modifications depending on the restrictions to face-to-face activities enforced by health authorities.
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.
Activities
| Title | Hours | ECTS | Learning Outcomes |
|---|---|---|---|
| Type: Directed | |||
| Lectures of problems | 15 | 0.6 | 1, 3, 5 |
| Lectures of theory | 15 | 0.6 | 1, 3, 5 |
| Type: Supervised | |||
| Lectures of practices | 30 | 1.2 | 1, 3, 2, 5 |
| Type: Autonomous | |||
| Personal work | 77 | 3.08 | 1, 3, 2, 5 |
Assessment
The assessment is done through two partial exams: a mid-term exam and another at the end. To pass the subject, an average of 5 of the two exams must be taken.
If the subject is not passed, you can go to a resit exam.
Whoever has not passed the subject by partial exams and has to go to the resit exam, will not be able to obtain more than a 7 as a final grade.
Both Mid-Term and Second exams are written. Resit exam is done with a computer.
*Student’s assessment may experience some modifications depending on the restrictions to face-to-face activities enforced by health authorities.
Assessment Activities
| Title | Weighting | Hours | ECTS | Learning Outcomes |
|---|---|---|---|---|
| Mid-Term exam | 50% | 4 | 0.16 | 1, 4, 3, 2, 5 |
| Resit exam | 100% | 5 | 0.2 | 1, 4, 3, 2, 5 |
| Second Exam | 50% | 4 | 0.16 | 1, 4, 3, 2, 5 |
Bibliography
- Mark Lutz, "Learning Python", Ed. O'Reilly
-"Python tutorial", https://www.tutorialspoint.com/python/
Software
Visual Studio Code: https://code.visualstudio.com/download