Applied Programming I

Credits
6
Types
Compulsory
Requirements
This subject has not requirements , but it has got previous capacities
Department
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.

Teachers

Person in charge

Weekly hours

Theory
2
Problems
2
Laboratory
0
Guided learning
0
Autonomous learning
6

Competences

Learning Outcomes

Knowledge

  • K3 - Identify the mathematical foundations, computational theories, algorithmic schemes and information organization principles applicable to the modeling of biological systems and to the efficient solution of bioinformatics problems through the design of computational tools.
  • K4 - Integrate the concepts offered by the most widely used programming languages in the field of Life Sciences to model and optimize data structures and build efficient algorithms, relating them to each other and to their application cases.

    • Skills

  • S7 - Implement programming methods and data analysis based on the development of working hypotheses within the area of study.
  • S8 - Make decisions, and defend them with arguments, in the resolution of problems in the areas of biology, as well as, within the appropriate fields, health sciences, computer sciences and experimental sciences.

    • Competences

  • C6 - Detect deficiencies in the own knowledge and overcome them through critical reflection and the choice of the best action to expand this knowledge.

    • Objectives

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

    Contents

    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

    Activities

    Activity Evaluation act


    Basic concepts of programming

    Introduction: algorithm, program, variable, expression, assignment. I/O. Conditional Iiterative statements. Solving problems with scalar data
    Objectives: 1 2
    Contents:
    Theory
    4h
    Problems
    4h
    Laboratory
    0h
    Guided learning
    0h
    Autonomous learning
    12h

    Iterative schemas

    Traversal & Search problems Invariants. Loop design
    Objectives: 2 6
    Contents:
    Theory
    4h
    Problems
    4h
    Laboratory
    0h
    Guided learning
    0h
    Autonomous learning
    12h

    Functions.

    Functions: design and parameter passing. Function design examples.
    Objectives: 3
    Contents:
    Theory
    4h
    Problems
    4h
    Laboratory
    0h
    Guided learning
    0h
    Autonomous learning
    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
    Objectives: 4
    Contents:
    Theory
    4h
    Problems
    4h
    Laboratory
    0h
    Guided learning
    0h
    Autonomous learning
    12h

    Dictionaries and Sets

    Dictionaries. Memory representation. Hashing concept. Counters. Sets. Memory representation. FrozenSets.
    Objectives: 4 5
    Contents:
    Theory
    4h
    Problems
    4h
    Laboratory
    0h
    Guided learning
    0h
    Autonomous learning
    12h

    Computational Complexity

    Basic notions of computational complexity
    Objectives: 5 6 7
    Contents:
    Theory
    2h
    Problems
    2h
    Laboratory
    0h
    Guided learning
    0h
    Autonomous learning
    6h

    Sort algorithms

    Bubble sort Insertion sort Selection sort sorting Python structures. Using lambda as key
    Objectives: 7
    Contents:
    Theory
    4h
    Problems
    4h
    Laboratory
    0h
    Guided learning
    0h
    Autonomous learning
    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
    Objectives: 4 5
    Contents:
    Theory
    4h
    Problems
    4h
    Laboratory
    0h
    Guided learning
    0h
    Autonomous learning
    12h

    Teaching methodology

    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

    Evaluation methodology

    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.

    Bibliography

    Basic

    Complementary

    Web links

    Previous capacities

    none