Estudia

• CG1- Seleccionar i sistematitzar la informació de forma eficient
• CG7- Expressar formalment les relacions entre les variables involucrades en un problema econòmic, utilitzant els principals instruments informàtics, matemàtics i estadístics per a la seva resolució
• ECOCE3- Entendre el funcionament dels mercats i les estratègies de les empreses que competeixen en els mercats, aplicant els conceptes i instruments de l'anàlisi econòmica a l'àmbit de l'empresa i els mercats
• ECOCE5- Aplicar la formalització matemàtica, les tècniques de modelització i d'optimització matemàtica en el plantejament i resolució de problemes d'economia i empresa
• CIFCE5- Entendre el funcionament dels mercats i agents financers i les estratègies empresarials, elaborar un pla financer i controlar la seva execució
• ADECE6- Entendre el funcionament dels mercats i les estratègies de les empreses que competeixen en els mercats, aplicant els conceptes i instruments de l'anàlisi econòmica a l'àmbit de l'empresa i els mercats
• ADECE10- Aplicar els mètodes d'optimització matemàtica, les eines bàsiques de la inferència estadística i els models economètrics per fer previsions i anàlisis empresarials

1. Introduction to the programming languages (Python,...)

2. Main scientific libraries.

          2.1. Algebra and Calculus.

          2.2. Data analysis.

          2.3. Data visualization.

3. Numerical solution of economic optimization problems.

4. Sensitivity analysis.

5. Applications in Business, Economics and Finance.

• Thomas J. Sargent and John Stachurski (2022). Python Programming for Economics and Finance. . Recuperat , a https://python-programming.quantecon.org/intro.html
• Thomas J. Sargent and John Stachurski (2022). Quantitative Economics with Python. . Recuperat , a https://python.quantecon.org/intro.html
• Judd, Kenneth L (cop. 1998 ). Numerical methods in economics . Cambridge, Mas. [etc.]: MIT Press. Catàleg
• Ljungqvist, Lars (2004 ). Recursive macroeconomic theory (2n ed.). Cambridge: Mass MIT Press. Recuperat 07-07-2017, a http://www.netLibrary.com/urlapi.asp?action=summary&v=1&bookid=126022 Catàleg

Activitats d'avaluació:

The final grade of the course will be the result of weighing the different activities. Activities have to be handed in before the specified deadline. Yet, you can still hand in your activity past the deadline. However, for every day late, your maximum grade goes down 2.5 points out of ten. Thus, if you hand in late by three days, the maximum grade you can achieve for this work is 2.5.

Those who do not pass can take a resit exam to evaluate the knowledge and skills acquired in the class. The grade of the resit exam is identical to the final grade of the class. The mark of the transcript will be Passed (5) if the mark is equal to or higher than 5.

Criteris específics de la nota «No Presentat»:
Students who have not submitted activities amounting to at least 30% of the total possible grade will receive a "No Presentado" (Not Submitted) qualification.

Avaluació única:
The unique assessment will consist of a final exam in which the knowledge and skills acquired during the course must be shown. To pass the course a minimum grade of 5 points out of 10 is required.

Requisits mínims per aprovar:
To pass the class, you need to have a minimum of 5 in the continuous assessment described above, and a minimum of 5 in the average mark of the quizzes.

A schedule of office-hours will be established (both in small groups and individually), which will be notified to students at the beginning of the semester. If face-to-face tutorials cannot be held, they will be carried out on-line with the help of the virtual platforms (Collaborate/Google Meet).

The official communication and interaction with students will be carried out preferably through e-mail and the forum of the subject, as well as in the spaces of the faculty enabled.

Course participants are assumed to have a background in mathematics, and interest in both mathematics and quantitative economics.

Use of Artificial Intelligence (AI) Tools:
The critical and responsible use of AI tools (such as ChatGPT or Copilot) is encouraged as a support for learning — for example, to clarify doubts, explore coding alternatives, or improve understanding.
However, these tools must not replace personal effort or reflection. Students are expected to understand the code they submit and are responsible for reviewing and validating its correctness. Using AI to generate code without understanding it is not academically acceptable.
Students may be asked to submit the prompts they used with AI tools, along with their code, to support transparency and reflection on the learning process.