Programación Aplicada I

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6
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Obligatoria
Requisitos
Esta asignatura no tiene requisitos
Departamento
CS
Web
http://www.cs.upc.edu/~ap1
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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 small and medium complexity.
The course covers basic concepts and techniques for building programs in imperative languages.
At the end of the course, students:

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

  • Lluis Padro Cirera ( )

Horas semanales

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

Resultados de aprendizaje

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 but satisfying the following conditions may opt to reevaluation:
- Have attended and delivered both EP & EF examens, plus at least 50% of the problem tests (NP)
- Have obtained an average of at least 3.5
- Reevaluation will consists of an additional exam, the grade of which will replace EF score.

Bibliografía

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