Public University of Navarre



Castellano | Academic year: 2021/2022 | Previous academic years:  2020/2021 
Double Bachelor's Degree in Data Science and Management, Business Administration at the Universidad Pública de Navarra
Course code: 507209 Subject title: NUMERICAL METHODS
Credits: 6 Type of subject: Mandatory Year: 2 Period: 2º S
Department: Estadística, Informática y Matemáticas
Lecturers:
HIGUERAS SANZ, M. INMACULADA (Resp)   [Mentoring ] ARRARAS VENTURA, ANDRÉS   [Mentoring ]

Partes de este texto:

 

Module/Subject matter

  • Module Level 1: Mathematics
  • Module Level 2: Advanced Mathematics

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General proficiencies

  • CB3- That students have the ability to collect and interpret relevant data (usually within their area of study) in order to make judgments that include reflection on relevant issues of a social, scientific or ethical nature.
  • CB5- That students have developed those learning skills necessary to undertake further studies with a high degree of autonomy.
  • CG1- To apply the acquired analytical and abstraction skills, intuition, and logical thinking to identify and analyze complex problems, and to seek and formulate solutions in a multidisciplinary environment.

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Specific proficiencies

  • CE7- To analyze, validate and interpret mathematical models of real-world situations, using the tools provided by differential and integral calculus of several variables, complex analysis, integral transforms and numerical methods to solve them.

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Learning outcomes

  • RA12- To understand the concept of numerical approximation, its importance and limitations.
  • RA13- To master the most basic techniques for the numerical solution of nonlinear equations and systems.
  • RA14- To master the most common interpolation techniques.
  • RA15- To get to know the most usual numerical integration techniques with error estimates.
  • RA16- To acquire some basic notions about the numerical solution of differential equations.

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Methodology

Methodology - Activity 

On-site hours 

Off-site hours 

A1- Interactive lectures 

42 

 

A2- Hands-on learning sessions  

14 

 

A3- Self-study and autonomous work  

 

88 

A4- Tutorials  

 

2 

A5- Assessment tests  

4 

 

Total 

60 

90 

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Relationship between formative activities and proficiencies/learning outcomes

Educational activity  

Proficiency 

A1Interactive lectures 

CB3, CB5, CG1, CE7 

A2- Hands-on learning sessions  

CB3, CB5, CG1, CE7 

A3- Self-study and autonomous work  

CB3, CB5, CG1, CE7 

A4Tutorials  

CG1, CE7 

A5- Assessment tests  

CG1, CE7 

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Languages

English.

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Evaluation

Learning outcome 

Assessment activity 

Weight (%) 

Resit assessment 

RA12 - RA16 

Written tests 

80% 

Yes 

RA12 - RA16 

Assignments and reports 

15% 

No 

RA12 - RA16 

Active participation 

5% 

No 

Continuous assessment is carried out by means of several written tests distributed throughout the semester as follows:

Ordinary assessment:

Written tests (individual):

  • Test A: lessons 1, 2 and 3, with a weight of 40% in the final grade.
  • Test B: lessons 4, 5 and 6, with a weight of 40% in the final grade.

In order to pass the course, the following two conditions must be fulfilled:

  • the average of the grades of Test A and Test B is not less than 5/10, and
  • the weighted average of the grades of Test A and Test B, the assignments and reports, and the active participation is not less than 5/10 (with the weights indicated in the table).

Resit assessment:

Written test (individual):

  • Test R: lessons 1, 2, 3, 4, 5 and 6, with a weight of 80% in the final grade.

In order to pass the course, the following two conditions must be fulfilled:

  • the grade of Test R is not less than 5/10, and
  • the weighted average of the grades of Test R, the assignments and reports, and the active participation is not less than 5/10 (with the weights indicated in the table).

If a student takes part in a number of assessment activities whose total weight is less than 50%, his/her final grade will be Absent.

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Contents

Introduction to numerical techniques. Direct and iterative methods for linear systems. Methods for nonlinear equations and systems. Interpolation. Numerical integration. Numerical treatment of differential equations.

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Agenda

  1. Introduction to numerical analysis. Numerical differentiation.
  2. Numerical solutions to linear systems.
  3. Numerical solutions to equations and non-linear systems.
  4. Numerical solutions to differential equations.
  5. Interpolation.
  6. Numerical integration.

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Bibliography

Access the bibliography that your professor has requested from the Library.


  • Basic bibliography:
    • 1. U.M. Ascher, C. Greif. A First Course in Numerical Methods. SIAM.
    • 2. R.L. Burden, J.D. Faires, A.M. Burden. Numerical Analysis. Brooks-Cole.
    • 3. J.D. Faires, R. Burden. Numerical Methods. Brooks-Cole.
    • 4. J. Kiusalaas. Numerical Methods in Engineering with Python 3. Cambridge University Press.
  • Additional bibliography:
    • 1. D. Kincaid, W. Cheney. Numerical Analysis. Mathematics of Scientific Computing. American Mathematical Society.
    • 2. A. Quarteroni, R. Sacco, F. Saleri. Numerical Mathematics. Springer.

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Location

Universidad Pública de Navarra, Campus Arrosadía, Pamplona.

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