Fall 2018
Data is growing faster than ever before, more data has been created in the past two years than in the entire previous history. By the year 2020, about 1.7 megabytes of new information will be created every second for every human being on the planet. Our accumulated data will grow from 4.4 zettabytes today to around 44 zettabytes, or 44 trillion gigabytes. The number of devices is also quickly growing. By 2020, we will have over 6.1 billion smartphone users globally and there will be over 50 billion smart connected devices in the world, all developed to collect and share data. The operation of these large volumes of data in order to get their insights in real time presents new challenges and opportunities for existing parallel data processing platforms cloud computing infrastructures.
This course introduces cloud computing and big data, and demonstrates the core tools used to wrangle and analyze big data on the cloud. With no prior experience, you will have the opportunity to walk through hands-on examples with Hadoop and Spark frameworks, two of the most common in the industry, and manage elastic processing environments using Amazon Web Services. You will also explore the basics of cloud services and cloud deployment models. You will become acquainted with commonly used industry terms, typical business scenarios and applications for the cloud, and benefits and limitations inherent in the new paradigm that is the cloud.
Main course site: https://www.ucm.es/imllorente/cloud-computing-and-big-data
Extreme scale data science at the convergence of big data and massively parallel computing is enabling simulation, modelling and real-time analysis of complex natural and social phenomena at unprecedented scales. The aim of the projects is to gain practical experience into this interplay by applying parallel computation principles in solving a data-intensive problem.
These final projects solve a data-intensive problem with parallel processing on the AWS cloud. They have identified a ata science problem, analysed its compute scaling requirements, collected the data, designed and implemented a parallel software, and demonstrated scaled performance of an end-to-end application.
Presented on 10 May 2018
| Group Number | Project Title | Team | Website |
|---|---|---|---|
| 1 | Accidentalidad de la ciudad de Madrid | Arturo Aguirre Calvo, Adrián Fernández De La Torre, Adrián Burillo Elmaleh, Ismael Setti Alonso, Arturo Pinar Adán | GitHub, Website |
| 2 | Advertising improvements on Twitch platform | Nestor Cabrero Martín, Raúl Fernández Guardia,Sergio Gavilán Fernández,Celia Martínez Graves, Rodrigo Manuel Pérez Ruiz | GitHub, Website |
| 3 | Heart Meter | Álvaro Ruiz Calzada, Mihaita Sorinel Tudor, Carlos Moisés Gil Solanas, Pablo Castaño Santiago | GitHub, Website |
| 4 | Shall I Buy? | Alberto Casado Trapote, Diego Martínez Simarro, Gonzalo Sanz Lastra, Héctor Marcos Rabadán | GitHub, Website |
| 5 | Applications of Big Data techniques in particle physics | Julia May, Viviana Sandagorda Guaman, Daniel Pascual Sentíes, Manuel Hernández Nájera-Alesón | GitHub, Website |
| 6 | Route Migration | Federico Saez Lomban, Jennifer Marmolejos Urbáez, Ernesto Pérez Montalvo | GitHub, [Website] |
| 7 | Kickstarter Analytics | Joaquín Barrio Lottmann, Alejandro Mendoza Silva, Pablo Miranda Torres, Pablo de Torre Barrio | GitHub, Website |
| 8 | Estudio del mercado de criptomonedas | Esteban Rueda Martinez, Yevheniy Kushch, Alejandro López-Tello Mora | GitHub, Website |
| 9 | Steam´s games analysis for companies | Alberto Gutierrez Gallego , Daniel Parra Rodriguez, Celia Calvo Gonzalez, Raquel Blanco Morago | GitHub, Website |