La asignatura “Explotación de la Información” es optativa y de duración semestral, con una carga docente de 6 créditos ECTS, repartidos entre 3 créditos de teoría y 3 de prácticas. Se imparte en el segundo semestre del tercer curso del Grado en Ingeniería Informática, y se incluye en el módulo de computación.

La asignatura no tiene dependencias con otras asignaturas del Grado en Ingeniería Informática.

Es una asignatura que muestra la importancia para una empresa de la explotación, análisis y uso adecuado de la información desestructurada como es la que aparece en la Web, en las redes sociales, en los e-mails de la empresa y los documentos internos (actualmente alrededor del 70% de la información de una empresa).

Se estudiarán los tópicos que se referencian en esta oferta de trabajo de Google:
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Big-O notations also known as "the run time characteristic of an algorithm".  You may want to refresh hash tables, heaps, binary trees, linked lists, depth-first search, recursion.

Coding: You should know at least one programming language really well, and it should preferably be C++ or Java. C# is OK too, since it's pretty similar to Java. You will be expected to write some code in at least some of your interviews. You will be expected to know a fair amount of detail about your favorite programming language.

Hashtables: Arguably the single most important data structure known to mankind. You absolutely should know how they work. Be able to implement one using only arrays in your favorite language, in about the space of one interview.

Trees: Know about trees; basic tree construction, traversal and manipulation algorithms. Familiarize yourself with binary trees, n-ary trees, and trie-trees. Be familiar with at least one type of balanced binary tree, whether it's a red/black tree, a splay tree or an AVL tree, and know how it's implemented. Understand tree traversal

Graphs: Graphs are really important at Google. There are 3 basic ways to represent a graph in memory (objects and pointers, matrix, and adjacency list); familiarize yourself with each representation and its pros & cons. You should know the basic graph traversal algorithms: breadth-first search and depth-first search. Know their computational complexity, their tradeoffs, and how to implement them in real code. If you get a chance, try to study up on fancier algorithms, such as Dijkstra and A*.

Other Data Structures: You should study up on as many other data structures and algorithms as possible. You should especially know about the most famous classes of NP-complete problems, such as traveling salesman and the knapsack problem, and be able to recognize them when an interviewer asks you them in disguise. Find out whatNP-complete means.