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 Python

2. Main scientific libraries

          2.1. Algebra and Calculus. NumPy, SimPy, SciPy

          2.2. Data analysis with Pandas

          2.3. Optimizing data visualization, MatPlotLib / Seaborn

3. Numerical solution of economic optimization problems

4. Introduction to Mathematica

5. Optimization with Mathematica

6. Generation of graphics and their sensitivity analysis

7. Analysis of financial data with Mathematica

• 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
• Abell, M. L., & Braselton, J. P. (2021). Mathematica by example. Academic Press.
• Wolfram Mathematica (2023). An Elementary Introduction to the Wolfram Language. Wolfram. Recuperat , a https://www.wolfram.com/wolfram-u/courses/wolfram-language/an-elementary-introduction-to-the-wolfram-language/

Activitats d'avaluació:

The final grade of the course will be the result of weighting the participation in class (20%), the exercises and case studies throughout the course (80%). Exercises and case studies have to be handed in before the specified deadline. Yet, you can still hand in your exercise or case study 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 three days, the maximum grade you can achieve for this work is 2.5.

In case you assisted less than 60% of the classes, the exercises and case studies are weighted 40% and assistance and participation in class 20%. The remaining 40% of the grade is covered by a final exam.


Those who do not pass the continuous assessment described above can take a resit exam to evaluate the knowledge and skills acquired in the class. The grade of the resit examen 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 that have not handed in more 30% of the tasks will qualified as "No Presentado".

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 course a minimum grade of 5 points out of 10 is required.

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.