Data Science Master’s Degree ProgramMaster’s degree in Big Data and Data Science: Applied to trade, business and finance
Universidad Complutense de Madrid
What will we learn?
Module I: Relational databases
The database, what is it and how do you manage one? A introduction to this basic tool in Big Data from design to its modeling and architecture. Learning of the Structured Query Language (SQL) and the tools such as Server Management Studio.
Module II: Programming languages
The student advances in the subject of statistical and predictive analysis of the Big Data expert. This module proposes a full immersion to the foundations of programming, without a need for previous knowledge.
Module III: NoSQL databases
How to use and model the NoSQL databases of structured storage. Different models NoSQL, what are they used for and when to do so, operative installation of MongoDB models; Find and its functions; projection of fields in results and CRUD operations.
Module IV: Business Intelligence
Preparation of the analytic skills of the student and his capability to handle himself in a business environment. Advance analytic and visualization of data are the main focus of this module.
Module V: Foundation in statistics
Statistics provide conclusions from the extracted data. An introduction to this great tool that the Big Data expert will have to learn, putting special attention to descriptive statistics and inferences, which allows the student to identify properties from the whole from a small sample.
Module VI: Big Data Technologies
Big Data takes a huge step with a full integration of the Internet of Things (IoT). Models of connectivity with other sources of data through message brokers and hubs: Introduction to the Spark system to express computing patterns and to Hadoo, which manages big volumes of data. Introduction to Data Visualization.
Module VII: Hadoop and Spark
The student will acquire a panoramic vision of HDFS, its architecture and its use through a command line. This is the fundamental storing system in the world of Big Data today, for this reason it is essential for the student to understand it and experiment.
Module VIII: Data Mining and Predictive Modeling
The value of Big Data within the business would not be understood without its predictive models. But it is essential to clean the macrodata from any bias. The student advances in the knowledge and practice of the data mining tools, linear regression algorithms and logistics, unsupervised classification, cluster analysis, scorecard…
Module IX: Machine Learning
Introduction to automatic learning techniques, an area which represents a big opportunity to manage, automatize and enrich data intelligence. Learning about decision trees, random forests, KNN algorithm, neuronal networks and deep learning.
Module X: Deep Learning
A Machine Learning process happens using an artificial neural network that is composed of a number of hierarchy levels. This module is separated into 4 chapters: Neural Networks, Convolutional Networks, Recurrent Networks y Autoencoders. A deeper dive into the subject is encouraged.
Module XI: Text mining
Processing texts as analysis of unstructured or semi structured information. The data extraction from written sources is one of the fastest evolving fields thanks to tools such as R language. Practice in techniques of mood analysis, thematic models or opinion mining.
Module XII: Social Media and Big Data
The opinions found in social media offer a valuable information for businesses. Free software such as Paiek makes it easy to mine the data in social media, allowing for a classification a measurement of the agents in each platform.
Module XIII: Scala
Scala is the programming language focused to objects, similar to Java, with characteristics of a functional language. Spark, one of the platforms which are used for data processing in Big Data is made with Scala
Module XIV: Advance visualization and visualization tools
The Big Data expert will only be able to make the best out of his work if he learns how to communicate it. This module is dedicated to visualization tools: map design with R, interactive representation with Shiny, graph grammar with Ggplot2, introduction to D3 and Tableau.
Module XV: Data Science applied to business and entrepreneurship in Big Data businesses
The students will be trained in real life business situations: creation of scientific teams and organization of the data intelligence project. Finally, they will develop a startup that uses Big Data as their value proposition.
Module XVI: End of master’s degree final project
To take in all the acquired knowledge, the student will design an integral strategy of data intelligence for an organization, using as many of the tools and processes in which the he was trained
360º master’s degree: Knowledge that positions you in the frontline of professional Big Data; training that opens the door to the best organization; and experience which allows you to take responsibilities.
***NTIC Master reserves the right to modify, delete and actualize the program.