Identifying Data 2019/20
Subject (*) Programming II Code 614G01006
Study programme
Grao en Enxeñaría Informática
Descriptors Cycle Period Year Type Credits
Graduate 2nd four-month period
 First Basic training 6
Language
Spanish
English
Teaching method Face-to-face
Prerequisites
Department Ciencias da Computación e Tecnoloxías da Información
Computación
Coordinador
Alonso Pardo, Miguel angel
 E-mail
miguel.alonso@udc.es
Lecturers
Alonso Pardo, Miguel angel
Barreira Rodriguez, Noelia
Bolón Canedo, Verónica
Cabrero Canosa, Mariano Javier
De Moura Ramos, Jose Joaquim
Gómez Rodríguez, Carlos
Guijarro Berdiñas, Berta M.
Hernandez Pereira, Elena Maria
Monroy Camafreita, Juan
Paz López, Alejandro
Pérez Sánchez, Beatriz
Sanchez Maroño, Noelia
Vilares Ferro, Jesus
 E-mail
miguel.alonso@udc.es
noelia.barreira@udc.es
veronica.bolon@udc.es
mariano.cabrero@udc.es
joaquim.demoura@udc.es
carlos.gomez@udc.es
berta.guijarro@udc.es
elena.hernandez@udc.es
juan.monroy@udc.es
alejandro.paz.lopez@udc.es
beatriz.perezs@udc.es
noelia.sanchez@udc.es
jesus.vilares@udc.es
Web http://moodle.udc.es
General description A materia pertence ao bloque de materias de Linguaxes e Programación do Módulo de Formación Básica da titulación, cunha forte interrelación coas materias do Módulo Común á Rama de Informática. As relacións mais estreitas establécense con Bases de Datos, Algoritmos e Deseño Software.
Un segundo bloque temático de materias relacionadas é o que forman aquelas da Materia Matemáticas, e dentro deste grupo, especialmente a materia Matemática Discreta.
Respecto ao perfil profesional, moitas áreas da computación requiren a habilidade de traballar coas estruturas de datos que se estudan nesta materia.

Study programme competencies
Code Study programme competences
A3 Capacidade para comprender e dominar os conceptos básicos de matemática discreta, lóxica, algorítmica e complexidade computacional e a súa aplicación para a resolución de problemas propios da enxeñaría.
A4 Coñecementos básicos sobre o uso e a programación dos ordenadores, sistemas operativos, bases de datos e programas informáticos con aplicación na enxeñaría.
B1 Capacidade de resolución de problemas
B3 Capacidade de análise e síntese
C3 Utilizar as ferramentas básicas das tecnoloxías da información e as comunicacións (TIC) necesarias para o exercicio da súa profesión e para a aprendizaxe ao longo da súa vida.
C6 Valorar criticamente o coñecemento, a tecnoloxía e a información dispoñible para resolver os problemas cos que deben enfrontarse.

Learning aims
Learning outcomes Study programme competences
Understanding the mechanisms of dynamic memory management. A4
 B1
 C6
Understanding the mechanisms of abstraction in the design of data structures. A4
 B1
B3
 C3
C6
Building specifications, designing the abstract type from them, using appropriate data structures. A3
A4
 B1
B3
 C3
C6
Using appropriate data structures and program algorithms to solve real problems. A3
A4
 B1
B3
 C3
C6
Assuming the need for a good specification and a good design as steps prior to coding. A4
 B3
 C6
Internalizing good programming practices. A4
 B3

Contents
Topic Sub-topic
Dynamic Memory Management Program memory organization.
Definition of pointer variables.
Dynamic memory allocation and deallocation.
Pointer assignment and comparison operations.
Introduction to Abstract Data Types Abstraction in programming: Concept, Evolution of abstract data types in computer programming, ADT and Object Oriented Programming.
Modularity in programming languages.
Abstract Data Type (ADT): Definition and concept, Differences between datatype, data structure and ADT, construction of ADT, Advantages of data abstraction.
Lists Informal specification of List ADT.
Implementation of List ADT.
Ordered list ADT: specification and implementation.
Multilists and multiordered lists: concept, representations and usage.
Stacks Informal specification of Stack ADT.
Implementation of Stack ADT.
Application on computer science.
Queues Informal specification of Queue ADT.
Implementation of Queue ADT.
Queue variations. Priority queues.
Application on computer science.
Trees Tree definition and terminology.
Binary Tree ADT: Informal specification, Implementation.
Binary Tree traversals.
Binary Search Trees Binary Search Trees.
AVL Trees.

Planning
Methodologies / tests Competencies Ordinary class hours Student’s personal work hours Total hours
Guest lecture / keynote speech A3 A4 B1 B3 30 30 60
Problem solving A3 B1 B3 C6 10 14 24
Laboratory practice A4 B1 B3 C3 C6 20 26 46
Objective test A3 A4 B1 B3 3 15 18
 
Personalized attention 2 0 2
 
(*)The information in the planning table is for guidance only and does not take into account the heterogeneity of the students.

Methodologies
Methodologies Description
Guest lecture / keynote speech The teacher will make a brief description of the topics and basic objectives pursued, in order to provide students with an overview of the subject. In addition they will establish relationships with other concepts previously acquired to build a timeline, and set out the recommended bibliography. They will then develop the theoretical contents using the guest lecture methodology.
Problem solving In order to reinforce the theoretical concepts, practical cases will be presented, which initially will be resolved by the teacher to guide students. As the theoretical development advance, students will solve problems organized into working groups. This activity, as well as discussion and active participation in class, will be assessed as part of the final mark.

When the examples used in the classes of problems or theoretical explanations involve coding or pseudocode, they will be developed showing the successive steps of top-down design. The reason is twofold: a) to get the student used to employ this method and b ) to avoid being lost in the details of the particular syntax and language features, instead of paying attention to the understanding and design of the solution.

Additional exercises will be assigned as extra-classroom activities. The student must solve them and comment/correct them with the teacher during group and/or individual tutoring . The purpose is to encourage the participation of students and promote, as far as possible, open dialogue and evaluation of solutions. After each topic, several self-assessment tests will be provided using virtual teaching resources, so that the students can verify their learning progress.
Laboratory practice Practical classes require the students to program data structures in a high-level language. Regular delivery milestones will be proposed to encourage continued study. The practical project assignment will detail the nature of the problem to solve and its specifications, which must be strictly observed. Subsequently, the role of the teacher will be to oversee the practice sessions, solving doubts and correcting misunderstandings, bad programming habits and syntax errors, etc.
Objective test Summative evaluation of the student through a final exam at the end of the semester, which will be very useful for demonstrating whether the student has acquired the skills of abstraction and design of ADTs and is sufficiently trained to use the precise skills to solve practical cases involving the application of such structures.

Personalized attention
Methodologies
Problem solving
Laboratory practice
Objective test
Description
Lectures, problem-solving sessions and practical sessions will be developed in response to student progress in understanding and assimilation of the contents. Overall progress will be made compatible with specific attention to those students who have more difficulties in the learning task and with additional support to those that present greater ease and wish to increase their knowledge.

Individual tutoring should not be used to extend the contents with new concepts, but to clarify the concepts already discussed in class. The teacher will use them as an interaction that allows him to draw conclusions about the degree of assimilation of the subject by students.

Assessment
Methodologies Competencies Description Qualification
Problem solving A3 B1 B3 C6 Various practical tasks to perform in small group tutorials will be proposed. The results obtained and the methods applied to reach the solution will be scored. The mark will only be added to the global marks once the course is passed. 10
Laboratory practice A4 B1 B3 C3 C6 The practical work are mandatory according to the conditions in each problem assignment. Students must pass all the practical assignments to pass the subject. 20
Objective test A3 A4 B1 B3 Compulsory fulfillment. Students must pass the exam to pass the subject. 80
 
Assessment comments

Practical work

- Only students with a mark
of FAIL or ABSENT in the first opportunity are allowed to deliver practical
works according to the practical definition proposed for second opportunity.

- According to article 14, paragraph
4 of existing regulations*, all students who plagiarize the work of others or
provide a copy of their practical work will be marked with FAIL, and therefore
a failing grade.

Part-time enrollment

- Students with part-time
enrollment must submit the assessment activities under the specific conditions
and deadlines. The student will have to communicate their situation to teachers.

Absent

- A student will have the
status of "Absent" if he does not attend the exam in the official
evaluation period.

Advanced opportunity in
December

- Student evaluation is
based only on a written exam.

* Normativa de evaluación, revisión y reclamación de las
calificaciones de los estudios de grado y máster universitario,
aprobadas por
Consello de Goberno de la Universidade da Coruña el 19 de diciembre de
2013. http://www.udc.es/export/sites/udc/normativa/_galeria_down/academica/avaliacionrevrecl.pdf

Sources of information
Basic Ignacio Zahonero y Luis Joyanes Aguilar (2004). Algoritmos y estructuras de datos: Una perspectiva en C. McGraw-Hill
Narasimha Karumanchi (2017). Data Structures and Algorithms Made Easy, 5th Edition. CareerMonk Publications
Kyle Loudon (1999). Mastering Algorithms with C. O'Reilly Media

Complementary Aaron M. Tenenbaum,? Yedidyah Langsam &? Moshe J. Augenstein (1989). Data Structures Using C. Prentice Hall
Reema Thareja (2014). Data Structures Using C - Second Edition. Oxford University Press
Richard F. Gilberg & Behrouz A. Forouzan (2005). Data Structures: A Pseudocode Approach with C (2nd Ed). Cengage Learning
Ignacio Zahonero, Lucas García Sánchez, Luis Joyanes Aguilar y Matilde Fernández Azuela (2005). Estructuras de datos en C (Serie Schaum). McGraw-Hill
Luis Joyanes Aguilar, Andrés Castillo Sanz, Lucas Sánchez García e Ignacio Zahonero Martínez (2002). Programación en C. Libro de problemas. McGraw-Hill
Ignacio Zahonero y Luis Joyanes Aguilar (2005). Programación en C. Metodología, Algoritmos y Estructura de Datos, 2º Edición. McGraw-Hill

Official page of CLion integrated development environment: https://www.jetbrains.com/clion/

Recommendations
Subjects that it is recommended to have taken before
Programming I/614G01001
Computer Science Preliminaries/614G01002
Discrete Mathematics/614G01004

Subjects that are recommended to be taken simultaneously

Subjects that continue the syllabus
Algorithms/614G01011
Databases/614G01013
Programming Paradigms/614G01014
Software Design/614G01015

Other comments


(*)The teaching guide is the document in which the URV publishes the information about all its courses. It is a public document and cannot be modified. Only in exceptional cases can it be revised by the competent agent or duly revised so that it is in line with current legislation.