Identifying Data 2020/21
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
Galician
English
Teaching method Hybrid
Prerequisites
Department Ciencias da Computación e Tecnoloxías da Información
Computación
Coordinador
Guijarro Berdiñas, Berta M.
 E-mail
berta.guijarro@udc.es
Lecturers
Alonso Pardo, Miguel angel
Barreira Rodriguez, Noelia
Cabrero Canosa, Mariano Javier
Gómez Rodríguez, Carlos
Guijarro Berdiñas, Berta M.
Hernandez Pereira, Elena Maria
Monroy Camafreita, Juan
Morán Fernández, Laura
Paz López, Alejandro
Pérez Sánchez, Beatriz
Sanchez Maroño, Noelia
Vilares Ferro, Jesus
 E-mail
miguel.alonso@udc.es
noelia.barreira@udc.es
mariano.cabrero@udc.es
carlos.gomez@udc.es
berta.guijarro@udc.es
elena.hernandez@udc.es
juan.monroy@udc.es
laura.moranf@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 céntrase na programación con estruturas de datos dinámicas e complexas, tratadas baixo a óptica dos tipos de datos abstractos. Pertence ao bloque de materias de Linguaxes e Programación do Módulo de Formación Básica da titulación. Presenta unha forte interrelación coas materias do Módulo Común á Rama de Informática, sendo as relacións mais estreitas con Programación I, Bases de Datos, Algoritmos e Deseño Software. Tamén presenta certa relación co bloque temático de Matemáticas, especialmente coa 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 que permitirá aos/as estudantes mellorar as súas habilidades como programadores/as.
Contingency plan 1. Modificacións nos contidos
Non se producirán cambios nos contidos.

2. Metodoloxías
*Metodoloxías docentes que se manteñen
Sesión maxistral
Solución de problemas
Prácticas de laboratorio
Proba obxectiva

Todas as metodoloxías docentes se manteñen, tan só cambia o medio de uso:
Manterase a realización síncrona das actividades ligadas a estas metodoloxías a través de Teams, nas franxas horarias que teñen asignadas no calendario oficial. Estas sesións síncronas poderán combinarse con material dixitalizado (vídeos, presentacións, etc.).
No caso das sesións maxistrais, poderán ser gravadas e postas a disposición do alumnado a través da plataforma Moodle.
No caso das “Prácticas de laboratorio” realizaranse sesións en pequenos grupos para o seguimento e apoio na realización das actividades propostas.
Por necesidades docentes técnicas ou organizativas, os estudantes poderán ser asignados a outros grupos e franxas horarias, previo acordo estudante/docente.
No caso de que o exame non poda realizarse de modo presencial, pasará a realizarse de modo online.

*Metodoloxías docentes que se modifican
Ningunha

3. Mecanismos de atención personalizada ao alumnado.
Serán os mesmos que os habilitados en condicións de normalidade (non presenciais).

4. Modificacións na avaliación
As condicións de avaliación contidas na Guía Docente non sufrirán cambios.
5. Modificacións da bibliografía ou webgrafía
Non se contemplan.


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 (ADT) 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.
Implementations of List ADT.
Ordered list ADT: specification and implementations.
Multilists and multiordered lists: concept, representations and usage.
Stacks Informal specification of Stack ADT.
Implementations of Stack ADT.
Application on computer science.
Queues Informal specification of Queue ADT.
Implementations 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 Used for theory lectures. The teacher will make a brief description of the topics and objectives , 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.
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.

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
The development lectures, problem-solving sessions and practical sessions will be carried out taking into account the progress of the students. The general progress of the class will be combined with specific attention to give additional support or expand knowledge. Laboratory practices will be carried out, in part, as autonomous work. For its correct development, periodic monitoring will be necessary to allow students to clear up errors of concept as soon as possible and to ensure the quality of the work.
In both cases, Moodle will be used to make available to the students "thematic forums" that resolve the general doubts detected related to specific activities such as the practices or proposed problems.
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. Outside teaching hours, attention is maintained in the official tutoring hours through the following channels:
- Email: Of use to make short answer queries.
- Teams: virtual meetings preferably upon request via email.

Assessment
Methodologies Competencies Description Qualification
Problem solving A3 B1 B3 C6 The results, form and conditions of completion of various scoring works that will be detailed during the course and that will be resolved during the REDUCED GROUPS TUTORIALS will be assessed.

The result of the activity, as well as the discussion and active participation in class, will be valued in the final grade.

This mark will only be added to the remaining 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 present and pass all the practical assignments with a global minimum of 4.5 out of 10 points to pass the subject.

The work submitted must be original of the student. According to article 14, section 4, of existing regulations*, the delivery of non-original works or with duplicate parts (either by copies between colleagues or by obtaining it from any other sources ...) will carry a global mark of FAIL in the ANNUAL CALL, and therefore a failing grade FOR THE TWO OPPORTUNITIES, both for the student who employed copied material and for whoever provides it. 		40
Objective test A3 A4 B1 B3 Mandatory completion. It implies a global treatment of the contents covered throughout the subject. It will be eminently practical so that students can demonstrate that they have acquired the necessary knowledge of abstraction and design, implementation and use of TADs and have trained enough in the skills required by the subject.

It is necessary to obtain a minimum grade of 4.5 out of 10 to pass the subject. 		60
 
Assessment comments

On shared responsibility for group work.

In the activities that are carried out in groups, such as the practices, all the members of the group will be jointly responsible for the work carried out and delivered, as well as for the consequences derived from the breach of the rules of authorship.

Absent mark

Those who do not attend the objective test in the official evaluation period or who do not submit any of the compulsory practices will have the status of "Absent" (No presentado, NP).

Second chance evaluation

The marks of the "Laboratory practices", as well as the block of "Problem solvig" will be kept for the second opportunity.
Only laboratory practices classified as FAIL or ABSENT at the first opportunity may be delivered at the second opportunity, always according to the statement proposed for it. Regarding the evaluation criteria, the second opportunity will remain the same as the first.

Part-time enrollment

Students enrolled part-time will have to submit the evaluable activities under the specific conditions and deadlines that will be established. It will be the duty of the student to communicate their situation to the teaching staff.

Advanced  Opportunity in December

The evaluation of this opportunity will be based exclusively on a written test.

* 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.