Algoritmos, Estructuras de Datos y Bases de Datos

Usted está aquí

Inicio » Estudios » Másteres » Máster en Ciencia de Datos » Plan de estudios » Asignaturas » Algoritmos, Estructuras de Datos y Bases de Datos
Créditos
6
Tipos
Obligatoria
Requisitos
Esta asignatura no tiene requisitos, pero tiene capacidades previas
Departamento
CS;ESSI
Web
https://learnsql.fib.upc.es/moodle/
This is a fundamental course that covers basic concepts on algorithms, data structures and databases. Even if the objectives of the course are common, this course spans two different tracks: one for students who have a major in Computer Science and another track for the rest.

The methodology is different for both tracks. For the former, it is a project-oriented course focusing on dataOps / MLOps, while for the latter it covers basic material on algorithms, data structures and databases in a guided, yet autonomous learning manner that will allow them to develop complex data systems

Profesorado

Responsable

  • Anna Queralt Calafat ( )
  • Maria Josefina Sierra Santibañez ( )

Otros

  • Marc Maynou Yelamos ( )
  • Oscar Romero Moral ( )

Horas semanales

Teoría
1
Problemas
0
Laboratorio
3
Aprendizaje dirigido
0
Aprendizaje autónomo
7.11

Competencias

Competencias Transversales

Uso solvente de los recursos de información

  • CT4 - Gestionar la adquisicion, la estructuracion, el analisis y la visualizacion de datos e informacion en el ambito de la especialidad y valorar de forma critica los resultados de esta gestion.

Lengua extranjera

  • CT5 - Conocer una tercera lengua, preferentemente el inglés, con un nivel adecuado oral y escrito y en consonancia con las necesidades que tendrán los titulados y tituladas.

Básicas

  • CB6 - Que los estudiantes sepan aplicar los conocimientos adquiridos y su capacidad de resolución de problemas en entornos nuevos o poco conocidos dentro de contextos más amplios (o multidisciplinares) relacionados con su área de estudio.
  • CB9 - Que los estudiantes posean las habilidades de aprendizaje que les permitan continuar estudiando de un modo que habrá de ser en gran medida autodirigido o autónomo.

Competencias Técnicas Genéricas

Genéricas

  • CG1 - Identificar y aplicar los métodos y procesos de gestión de datos más adecuados para gestionar el ciclo de vida de los datos, incluyendo datos estructurados y no estructurados

Competencias Técnicas

Específicas

  • CE1 - Desarrollar algoritmos eficientes basados en el conocimiento y comprensión de la teoría de la complejidad computacional y las principales estructuras de datos dentro del ámbito de ciencia de datos
  • CE2 - Aplicar los fundamentos de la gestión y procesamiento de datos en un problema de ciencia de datos

Objetivos

  1. To specify a computational problem and justify the correctness and termination of an iterative or recursive algorithm that solves this problem.
    Competencias relacionadas: CT4, CT5, CE1,
  2. To carry out asymptotic analyses of the worst case running time of iterative and recursive algorithms.
    Competencias relacionadas: CT4, CT5, CE1,
  3. To review some simple data structures: stacks, queues, lists, and trees
    Competencias relacionadas: CT4, CT5, CE1,
  4. To know what a priority queue is, be able to use it to solve
    computational problems, and understand and analyse the main data
    structures and algorithms that are used to implement it.
    Competencias relacionadas: CT4, CT5, CE1,
  5. To know what ordered and unordered dictionaries are, be able to
    use them to solve computational problems, and understand and analyse
    the main data structures and algorithms that are used to implement
    them.
    Competencias relacionadas: CT4, CT5, CE1,
  6. To know the main data structures and algorithms that can be used
    to represent graphs and solve classic graph problems such as traversals,
    topological ordering and shortest paths, analyse their asymptotic
    running time and be able to use them to solve computational problems.
    Competencias relacionadas: CT4, CT5, CE1,
  7. To know what a decision problem is, the definitions of the
    P and NP classes of problems, what a polynomial-time reduction is
    and the associated notion of NP-completeness.
    Competencias relacionadas: CT4, CT5, CE1,
  8. Describe what is a database and a database management system
    Competencias relacionadas: CT4, CT5, CE2,
  9. Effectively use the standard Structured Query Language (SQL) to query relational databases
    Competencias relacionadas: CT4, CT5, CE2,
  10. Explain the relational data model, including its data structures, the relational algebra and integrity constraints
    Competencias relacionadas: CT4, CT5, CE2,
  11. Given a set of informational requirements, model the logic schema of a relational database
    Competencias relacionadas: CT4, CT5, CE2,
  12. Identify the main objectives of a database management system query optimizer
    Competencias relacionadas: CT4, CT5, CE2,
  13. Apply data structures, algorithms and database queries to solve a problem in a realistic situation
    Competencias relacionadas: CT5, CG1, CE1, CE2, CB6, CB9,

Contenidos

  1. Specification and Analysis of Algorithms
    Justification of correctness and termination of iterative and recursive
    algorithms.
  2. Asymptotic Analysis
    Asymptotic analysis: Order of growth, Big-O, Omega and Theta notations, asymptotic analysis of iterative algorithms, introduction to the asymptotic analysis of recursive algorithms (recurrences and the master method).
  3. Review of Object Oriented Programming
    Review of Fundamental Concepts of Object Oriented Programming.
  4. Basic Data Structures
    Basic Data Structures: stack, queue, linked-list and tree.
    Abstract Data Types, examples of use, and implementations.
  5. Priority Queue
    Priority Queue: ADT (operations) and examples of use.
    Implementation with heaps and asymptotic analysis. Heapsort algorithm.
  6. Ordered Dictionary
    Ordered Dictionary: ADT (operations) and examples of use. Implementation with binary search trees and AVL trees, and asymptotic analysis.
  7. Unordered Dictionary
    Unordered Dictionary: ADT (operations) and examples of use. Implementation with hash tables and asymptotic analysis.
  8. Graph
    Graph: Adjacency list and adjacency matrix representation. Traversal algorithms:
    depth-first search (DFS) and breadth-first search (BFS). Topological sort, shortest
    paths, and minimum spanning tree algorithms.
  9. Introduction to Complexity and Intractability
    Decision problems. P and NP classes of problems. Polynomial-time reduction and NP-completeness.
  10. Introduction to databases and database management systems
    Main concepts on databases and database management systems. Relational database management systems.
  11. SQL: Data-definition language and data-manipulation language
    Introduction to the SQL language.
  12. The relational model
    Data structures and integrity constraints.
  13. The relational algebra
    The relational algebra operators and how to build data pipes with them. Notion of semantic and syntactic optimization.
  14. Logical design of relational databases
    Design the logical schema of a database.
  15. Notions of physical design and physical database optimization
    Notions of query optimizer, access plan and cost model.
  16. Data Science-related advanced topics
    Students with a major in Computer Science will investigate on advanced topics specific for data science projects. For example, data
    quality, entity resolution, data integration, etc.

Actividades

Actividad Acto evaluativo


Basic concepts on Algorithms, Data Structures, and Databases

Motivation and main concepts on algorithmics, data structures, and database management systems.
Objetivos: 8 1
Contenidos:
Teoría
7.5h
Problemas
0h
Laboratorio
0h
Aprendizaje dirigido
0h
Aprendizaje autónomo
0h

Partial Exam

For students with a minor in Computer Science, this exam evaluates their knowledge on fundamental concepts of algorithms, data structures and databases.
Objetivos: 4 9 2 3 8 10 11 12 1
Semana: 8
Teoría
3h
Problemas
0h
Laboratorio
0h
Aprendizaje dirigido
0h
Aprendizaje autónomo
6h

Computer Science for Data Science

Students in the minor track will put in practice all the contents of the course by solving exercises related to algorithms, data structures, and databases. Students in the major track will undertake a project spanning all main phases of a data science. As result, they are asked to develop a quality realistic end-to-end system architecture for a data science project.
Objetivos: 4 9 5 7 2 3 6 10 11 12 13 1
Contenidos:
Teoría
0h
Problemas
0h
Laboratorio
40.5h
Aprendizaje dirigido
0h
Aprendizaje autónomo
84h

Final Exam

For students with a minor in Computer Science, this exam evaluates their knowledge on fundamental concepts of algorithms, data structures and databases
Objetivos: 4 9 5 7 2 3 6 8 10 11 12 1
Semana: 14
Teoría
3h
Problemas
0h
Laboratorio
0h
Aprendizaje dirigido
0h
Aprendizaje autónomo
6h

Metodología docente

The students are divided into two tracks: one for students with a minor in computer science (track 1) and another one for students with a major in computer science (track 2).

Students in track 1 will study fundamental concepts on algorithms, data structures and databases.

The Databases part for track 1 students is organized in theory sessions where the main concepts on relational databases, SQL, Relational Algebra and optimizations are presented. Examples and exercises will be introduced to practice and discuss alternative solutions during the class. Additional material to facilitate understanding theoretical concepts will be provided.
Between sessions, students will have to solve some mandatory exercises to guarantee a smooth learning process. These exercises are individual and correspond to the DBEx part of the final grade in the evaluation.

The Algorithms and Data Structures part for track 1 consists of theory, problems and programming sessions. During the 'theory sessions' the main concepts, data structures, algorithms and results will be presented and illustrated with examples. Additional material to read and problems to facilitate understanding theoretical concepts will be provided.
Exercises on theoretical aspects of the course contents, such as justifying correctness and temination, asymptotic analysis of worst case running time, application of data structure operations or algorithms to particular problem instances, or demonstration of results on data structures and algorithms will be proposed. These exercises will be solved in groups of two or three students during the 'problems sessions', to promote discussion and team work. The lecturer will answer questions and suggest reading course material relevant to the problems. Assessment of written solutions submitted by each group corresponds to the ADSEx grade in the evaluation.
Programming problems requiring the implementation and use of the main data structures presented in the course in Python will be proposed. Students should solve these problems individually during the 'programming sessions' and afterwards as homework. Assessment of programming problems corresponds to the ADSPr grade in the evaluation. and it will be carried out through short meetings requiring demonstration of some of the solutions submitted, explanation, and slight modifications.

Students in track 2 will carry out the DS-EtE project and, for them, this course is a project course. There, students must create an end-to-end system architecture to ingest, store, process, learn models and deploy such system for a realistic project with realistic data. The students must develop good practices developing such architecture (what nowadays is known as DataOps / MLOps).

This course has a strong self-learning component and shares the same objectives in both tracks. The lecturers will supervise the students progress during the semester to guarantee a proper progress.

Método de evaluación

Let DBM = Databases midterm exam grade,
DBF = Databases final exam grade,
DBEx = Database exercises to be solved during the course,
ADSM = Algorithms and Data Structures midterm exam grade,
ADSF = Algorithms and Data Structures final exam grade and
ADSEx = Algorithms and Data Structures exercises to be solved in groups during the course,
ADSPr = Programming problems to be solved individually during the course,
DS-EtE = DS-EtE project grade

Then,

1) If the student followed track 1 (see methodology) the mark is calculated as follows:
BD = MAX (0.2*DBEx + 0.8*DBM, DBF),
ADS = ADSPr*0.3 + ADSEx*0.1 + MAX(0.2*ADSM + 0.4*ADSF, 0.6*ADSF)


ADSDB (final course mark) = 0.4*BD + 0.6*ADS

2) If the student followed track 2 (see methodology) the mark is calculated as follows:

ADSDB (final course mark) = DS-EtE

Bibliografía

Básica:

Complementaria:

Web links

Capacidades previas

This course assumes basic competences in algorithms, data structures and databases. The course is structured to cope with different backgrounds and learning needs but basic knowledge on Computer Science principles is assumed: notions of computer architecture, basic programming constructs and data structures.