Programación Aplicada I

Créditos
6
Tipos
Obligatoria
Requisitos
Esta asignatura no tiene requisitos , pero tiene capacidades previas
Departamento
CS
Web
http://www.cs.upc.edu/~ap1
Mail
ap1@cs.upc.edu
The course presents the elements of a computer programming language and the basic algorithmic foundations for working with scalar and structured data. During the course students will acquire the knowledge to deal with programming problems of low and medium complexity.
The course covers basic concepts and techniques for building programs in imperative languages.


1. will know the basic constituents of imperative languages: variables, types, expressions, conditionals and iterations.
2. will be able to use and design procedures (actions and functions) to encapsulate sub-problem solutions.
3. will be able to use the descending design methodology to give solutions to problems of medium difficulty.
4. will be able to use vectors and dictionaries to store structured information and as a basis for the design of data structures.
5. will know and be able to reproduce and properly use some fundamental algorithms, such as dichotomous search or elementary vector sorting algorithms.

The programming language used in this couse is Python, although the emphasis is not on learning the details of the language but on solving algorithmic problems and building structured programs.

Profesorado

Responsable

Horas semanales

Teoría
2
Problemas
2
Laboratorio
0
Aprendizaje dirigido
0
Aprendizaje autónomo
6

Competencias

Resultados de aprendizaje

Conocimientos

  • K3 - Identificar los fundamentos matemáticos, las teorías informáticas, los esquemas algorítmicos y los principios de organización de la información aplicables al modelado de sistemas biológicos y a la resolución eficiente de problemas bioinformáticos mediante el diseño de herramientas computacionales.
  • K4 - Integrar los conceptos ofrecidos por los lenguajes de programación de mayor uso en el ámbito de las Ciencias de la Vida para modelar y optimizar estructuras de datos y construir algoritmos eficientes, relacionándolos entre sí y con sus casos de aplicación.

    • Habilidades

  • S7 - Implementar métodos de programación y análisis de datos orientados a partir de la elaboración de hipótesis de trabajo, dentro del área de estudio.
  • S8 - Enfrentarse a la toma de decisiones, y defenderlas con argumentos, en la resolución de problemas de las áreas de biología, así como, dentro de los ámbitos adecuados, las ciencias de la salud, las ciencias de la computación y las ciencias experimentales.

    • Competencias

  • C6 - Detectar deficiencias en el propio conocimiento y superarlas mediante la reflexión crítica y la elección de la mejor actuación para ampliar este conocimiento.

    • Objetivos

    1. Understand how to build a program and use tools the necessary tools: console, editor and compiler.
      Competencias relacionadas: K4, C6,
    2. Understand the syntax and semantics of basic expressions and instructions in an imperative programming language (Python).
      Competencias relacionadas: K4, C6,
    3. Understand the concepts of function, procedure and parameter passing, to be able to
      use functions and procedures to develop programs.
      Competencias relacionadas: K4, C6,
    4. Understand lists, dictionaries, and sets, and identify how to use the appropriately to solve problems
      Competencias relacionadas: K3, K4, S7, C6,
    5. Compare solutions regarding time and memory use and choose the most appropriate solutions for simple cases.
      Competencias relacionadas: K3, S7, S8, C6,
    6. Understand search and traversal schemas and appropriately apply them to solve problems.
      Competencias relacionadas: K3, S7, C6,
    7. Understand binary search, and basic sort algorithms (insertion, selection, bubblesort, mergesort)
      Competencias relacionadas: K3, S8, C6,

    Contenidos

    1. Basic Programming concepts
      Introduction: algorithm, program
      - variable, expression,
      - assignment. I/O. Conditional
      - Iiterative statements.

      Solving problems with scalar data
    2. Iterative schemas
      Traversal & Search schemas.
      Invariants
    3. Functions and Procedures
      Function design and parameter passing.
      Function design examples.
    4. Lists
      Representation of data structures with lists. Traversal and search algorithms on lists. Python memory management of Lists
    5. Matrices
      Basic algorithms on matrices: sum, symmetric, transpose, multiplication.
      Python memory management of Matrices
    6. Dictionaries
      Memory representation of dictionaries. Hashing concept.
      Mutability. Tuples.
      Counters. Sets.
      FrozenSets
    7. Computational Complexity
      Basic notions of computational complexity
    8. Sort algorithms
      Bubble sort
      Insertion sort
      Selection sort
      Mergesort
      sorting Python structures using lambda as key
    9. Standard Python Input
      readline
      readlines
      strip
      split

    Actividades

    Actividad Acto evaluativo


    Basic concepts of programming

    Introduction: algorithm, program, variable, expression, assignment. I/O. Conditional Iiterative statements. Solving problems with scalar data
    Objetivos: 1 2
    Contenidos:
    Teoría
    4h
    Problemas
    4h
    Laboratorio
    0h
    Aprendizaje dirigido
    0h
    Aprendizaje autónomo
    12h

    Iterative schemas

    Traversal & Search problems Invariants. Loop design
    Objetivos: 2 6
    Contenidos:
    Teoría
    4h
    Problemas
    4h
    Laboratorio
    0h
    Aprendizaje dirigido
    0h
    Aprendizaje autónomo
    12h

    Functions.

    Functions: design and parameter passing. Function design examples.
    Objetivos: 3
    Contenidos:
    Teoría
    4h
    Problemas
    4h
    Laboratorio
    0h
    Aprendizaje dirigido
    0h
    Aprendizaje autónomo
    12h

    Lists and Matrices

    Representation of data structures with lists. Traversal and search algorithms on lists. Python memory management of Lists Matrices. Basic algorithms on matrices: sum, symmetric, transpose, multiplication. Python memory management of Matrices
    Objetivos: 4
    Contenidos:
    Teoría
    4h
    Problemas
    4h
    Laboratorio
    0h
    Aprendizaje dirigido
    0h
    Aprendizaje autónomo
    12h

    Dictionaries and Sets

    Dictionaries. Memory representation. Hashing concept. Counters. Sets. Memory representation. FrozenSets.
    Objetivos: 4 5
    Contenidos:
    Teoría
    4h
    Problemas
    4h
    Laboratorio
    0h
    Aprendizaje dirigido
    0h
    Aprendizaje autónomo
    12h

    Computational Complexity

    Basic notions of computational complexity
    Objetivos: 5 6 7
    Contenidos:
    Teoría
    2h
    Problemas
    2h
    Laboratorio
    0h
    Aprendizaje dirigido
    0h
    Aprendizaje autónomo
    6h

    Sort algorithms

    Bubble sort Insertion sort Selection sort sorting Python structures. Using lambda as key
    Objetivos: 7
    Contenidos:
    Teoría
    4h
    Problemas
    4h
    Laboratorio
    0h
    Aprendizaje dirigido
    0h
    Aprendizaje autónomo
    12h

    Data Structure Design

    Representing complex data. lists of lists, lists of dictionaries, dictionaries with lists, embedded dictionaries. dicctionaries with structured keys (tuples/frozensets) Problem solving via appropriate data structures
    Objetivos: 4 5
    Contenidos:
    Teoría
    4h
    Problemas
    4h
    Laboratorio
    0h
    Aprendizaje dirigido
    0h
    Aprendizaje autónomo
    12h

    Metodología docente

    During theoretical sessions, the professor will expose programming concepts, combined with examples and problem solving.

    During problem-solving sessions, students will work on their own solving problems with an online platform (El Jutge), under supervison and assistance of the professor when needed

    Método de evaluación

    There will be two exams. A mid-term exam and a final exam
    In addition, there will be some evaluable problem tests taken during problem sessions, announced in advance.

    FinalScore = 0.10*NP + 0.90*max(EF, 0.4*EP+0.6*EF)

    where:
    NP : Problem score. Short problem tests taken during problem sessions
    EP: Partial exam score
    EF: Final exam score

    Students failing the course may opt to reevaluation.
    Reevaluation will consist of an additional exam, the grade of which will replace EF score.

    Bibliografía

    Básico

    Complementario

    Web links

    Capacidades previas

    none