Public University of Navarre



Castellano | Academic year: 2020/2021
Double Bachelor's Degree in Data Science and Management, Business Administration at the Universidad Pública de Navarra
Course code: 507109 Subject title: DATA STRUCTURES
Credits: 6 Type of subject: Mandatory Year: 1 Period: 2º S
Department: Estadística, Informática y Matemáticas
Lecturers:
PINA CALAFI, ALFREDO (Resp)   [Mentoring ] ARMENDARIZ IÑIGO, JOSÉ ENRIQUE   [Mentoring ]

Partes de este texto:

 

Module/Subject matter

  • Module: Basic Formation
  • Subject: Computer Science

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General proficiencies

  • CT4. Ability to work in muldisciplinary and multicultural teams.
  • CT5. Ability to perform project-oriented work

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Specific proficiencies

  • CG1. Applying analytical and abstraction thinking, intuition and logical thinking to identify and analyze complex problems, and to search and pose solutions in a multidisciplinary environment.
  • CG4. Using theoretical and practical thinking to extract information from homogeneous/heterogeneous datasets, particularly from large datasets.
  • CE2. Using techniques to represent and fuse data and information.
  • CE6. Knowing fundamentals of computer programming, including code efficiency and the limitations of basic data structures in code design.

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Learning outcomes

  • RA8. Identifying problems with natural recursive solutions.
  • RA9. Understanding the concepts of stack and queue in the context of computer programming.
  • RA10. Understanding the concepts of graph and tree in the context of computer programming.
  • RA11. Designing modular programs for complex problem solution.
  • RA12. Using efficient file handling in data analysis.
  • RA13. Identifying the relationships between files and databases.

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Methodology

Methodology-Activity 

Attendance (hours) 

Self-study (hours) 

A1- Lecture / Collaborative classes 

26 

 

A2- Lab Sessions 

30 

 

A3- Study and autonomous work of the student 

 

86 

A4- Tutoring 

 

4 

A5- Assessment tests 

4 

 

Total 

60 

90 

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Relationship between formative activities and proficiencies/learning outcomes

Methodology-Activity 

Proficiency 

CE2, CE6, CG4 

A1, A5 

CG1, CT4, CT5, CG4 

A2, A3, A4 

CG1, CT4, CT5, CE2, CE6 

A4 

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Languages

English.

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Evaluation

Learning outcome 

Evaluation system 

Weight (%) 

Retake policy 

RA8, RA9, RA10, RA12, RA13 

Theoretical/Practical exam (Individually performed). Every student must get at least 50% in this exam to average with the rest of the evaluable aspects.

50 

Yes 

RA8, RA9, RA10, RA11, RA12, RA13 

Deliverables and practical exams 

40 

No 

RA8, RA9, RA10, RA11, RA12, RA13 

Continuous assessment. Active participation in the course 

10 

No

 

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Contents

Stack and Queues. Tree programming. Graphs. Recursion. Modularity. Introduction to file handling.

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Agenda

  • The Stack Abstract Data Type (ADT);
  • The Queue & Double Ended Queue (Deque) ADTs;
  • The List ADT;
  • Introduction to Computational Complexity Analysis
  • Introduction to Recursion
  • The Binary Tree ADT;
  • The Binary Search Tree (BST) ADT;
  • Introduction to Graphs;
  • Programming features: Modularity with Python, Files with Python¿

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Bibliography

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


The basic bibliography of the course is:

  • Kent D. Lee, Steve Hubbard, Data Structures and Algorithms with Python, Ed. Cham. Springer International Publishing.

The complementary bibliography of the course is:

  • Jim Knowlton, Python: Create ¿ Modify ¿ Reuse, Wrox Press, 2008.

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Location

Universidad Pública de Navarra, Campus Arrosadía, Pamplona.

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