Public University of Navarre



Castellano | Academic year: 2024/2025 | Previous academic years:  2023/2024  |  2022/2023  |  2021/2022  |  2020/2021 
Bachelor's Degree in Data Science at the Universidad Pública de Navarra
Course code: 505108 Subject title: DATA STRUCTURES
Credits: 6 Type of subject: Basic Year: 1 Period: 2º S
Department: Estadística, Informática y Matemáticas
Lecturers:
PINA CALAFI, ALFREDO (Resp)   [Mentoring ] AMOZARRAIN PEREZ, UGAITZ   [Mentoring ]

Partes de este texto:

 

Module/Subject matter

  • Module: Basic Formation
  • Subject: Computer Science

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Contents

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

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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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Evaluation

 

Learning
outcome
Assessment
activity
Weight (%) It allows
test resit
Minimum
required grade

RA8, RA9, RA10, RA12, RA13 

Theoretical/Practical exam (Individually performed).

50 YES 5

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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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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Experimental practice program

The practical sessions will consist of developing a series of projects to be carried out either in 1, 2 or 3 weeks. They will serve to apply all the theoretical concepts and strengthen the knowledge of structured programming with Python. The projects will be known at the beginning of the course, and all the technical features will be introduced, and progressively solved, in each session.

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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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Languages

English.

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Location

Universidad Pública de Navarra, Campus Arrosadía, Pamplona.

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