Students who complete this course successfully should be able to :
- Know a set of non-conventional threads to enable a scalable data management, as well as the use of parallel algorithms and statistical modeling, with and without the use of the cloud ;
- Be proficient in an ecosystem of tools and platforms to allow them, in the face of a concrete problem, to determine the solution to be applied and the tools to be used in storage, exploration and analysis of large volumes of data.

1. Introduction
History and context. Overview of Big Data technology . Science data . Search, indexing and memory

2. Large scale data handling
Large Scale Storage System. MapReduce and Hadoop . Relation to current databases, streams , algorithms, extensions and languages. Parallel query processing and computational analysis of statistics. Key - value storage ; Comparing SQL databases and non- SQL

3. Communication of results
Visualization of computational results. Sources of data, privacy, ethics and governance

4. Special Topics
Analysis of graphs : structure , crossings , computational analysis, PageRank, recursive queries, semantic web, advertising and recommendation systems on the internet .