Applied Programming II

• Weekly hours
• Learning Outcomes
• Objectives
• Contents
• Activities
• Teaching methodology
• Evaluation methodology
• Bibliography

Teachers

Person in charge

• Alexis Molina Martinez de los Reyes ( )
• Gabriel Valiente Feruglio ( )

Weekly hours

Learning Outcomes

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 iterative algorithms and their implementation in a modern programming language, and apply them to solve problems.
   Related competences: K3, K4, S7, S8, C6,
2. Understand recursive algorithms and their implementation in a modern programming language, and apply them to solve problems.
   Related competences: K3, K4, S7, S8, C6,
3. Compare solutions regarding time and memory use and choose the most appropriate solution.
   Related competences: K3, K4, S7, S8, C6,

Contents

1. Introduction
   Introduction. Recapitulation of Applied Programming 1. Introduction to Python. Functions, scoping, and abstraction. Structured types and mutability.
2. Recursion
   Recursion. Recursive design. Simple recursion. Multiple recursion. Tail recursion. Tail recursion elimination. Examples.
3. Object-oriented programming
   Introduction to object-oriented programming. Abstract data types, classes, and methods. Encapsulation, inheritance, shadowing. Examples.
4. Algorithm analysis
   Algorithm analysis. Cost in time and space. Worst, best, and average case. Asymptotic notation. Analysis of the cost of iterative algorithms. Analysis of the cost of recursive algorithms. Recurrences. Solution methods. Examples.
5. Divide and conquer
   Divide and conquer. Principles: partition into subproblems, recombination of solutions. Examples.
6. Dynamic programming
   Dynamic programming. Principles: partition into overlapping subproblems, memoization, tabulation. Top-down dynamic programming. Bottom-up dynamic programming. Examples.

Activities

Activity Evaluation act

Teaching methodology

Evaluation methodology

Bibliography

Basic:

• Introduction to computation and programming using Python : with application to understanding data - Guttag, John, The MIT Press, 2021. ISBN: 9780262542364
  https://discovery.upc.edu/permalink/34CSUC_UPC/8e3cvp/alma991005204079706711

Complementary:

• Introduction to algorithms - Cormen, Thomas H, The MIT Press , cop. 2009. ISBN: 978-0-262-03384-8
  https://discovery.upc.edu/permalink/34CSUC_UPC/8e3cvp/alma991003632289706711