BEGIN:VCALENDAR VERSION:2.0 PRODID:icalendar-ruby CALSCALE:GREGORIAN BEGIN:VEVENT DTSTAMP:20260629T214453Z UID:f843c202-37af-4c39-a710-e1ed518f8b5d DTSTART:20160208T080000Z DTEND:20160211T170000Z DESCRIPTION:NOTICE: From 14th of January all aplicants will be put on waiti ng list and places cannot be confirmed. If any places become available\, w e will inform you on a first come - first served basis.\n\nPlease do not f orget to bring your laptop on the couorse.\n\nCOURSE INRORMATION:\n\nCours e Convener: Maria-Ribera Sancho\n\nObjectives: The course brings together key information technologies used in manipulating\, storing\, and analysin g data including:\n\nthe basic tools for statistical analysis\,\n techniqu es for parallel processing\,\n tools for access to unstructured data\,\n s torage solutions.\nLearning outcomes: Students will be introduced to syste ms that can accept\, store\, and analyse large volumes of unstructured dat a. The learned skills can be used in data intensive application areas.\n\n Level: For trainees with some theoretical and practical knowledge\n\nOUTLI NE:\n\nDay 1 08/02:  Introduction (Vassil Alexandrov)Session 1: 9:30am – 1pm\n\nData Science current trends session will focus on results of th e latest key studies both and Europe and the USA an in the area of Data Sc ience and outline the major trends\, findings and recommendations.\nCoffee break 11:00- 11:30\n\nData Science definitions and mathematical foundatio ns introduction. \nWhile tackling Big Data problems in many cases element ary or standard statistical approaches fail. New research methods are requ ired to be developed to tackle such problems. Therefore this session will focus key research methods and approaches for Data Science\, ranging from theory creating and theory testing approaches to conceptual-analytical app roaches and experimental ones\, that are able to lead to discovering globa l properties on data  These will be mainly deterministic and hybrid (stoc hastic/deterministic) methods and algorithms.\n Session 2: 2pm – 6pm\n\ nThis session will focus on several key methods and algorithms (both seria l and parallel) that enable to discover global properties on data while d ealing with Big Data:\n Network Science\n Multi Constrained and Multi-Obj ective Optimization\n Examples of using the above approaches\n \n Example s using the above approaches and some hands-on exercise\nCoffee break 16:0 0 – 16:30\n\nSocial Simulation Applications (Josep Casanovas)\n Day 2 0 9/02: Session 1: 9:30am – 1pm Data sharing (Anna Queralt)\n\nIn this ses sion we will provide an overview on current Open Data and data sharing app roaches.\nUsually\, when talking about Big Data\, the emphasis is put on h ow to efficiently store and analyse huge amounts of data. However\, only w hen data from independent sources is combined it is possible to gain insig hts that would be impossible to obtain by analysing each dataset separatel y. Thus\, it is essential that data\, either public or private\, is shared so that researchers\, students\, app developers or citizens in general ca n extract as much value as possible from it.Coffee break 11:00- 11:30\n\nH ands-on exercise\n Session 2: 2pm – 5pm Data analytics with Apache Spar k - part 1 (Mario Macias)\n\nIn the recent years\, Apache Spark has emerge d as one of the most promising technologies for large-scale data processin g in a fast and general way\, with “programmer-friendly” interfaces an d official bindings for many of the most used languages (Java\, Scala\, Py thon and R)\, extensive documentation and development tools. In addition\, overcomes other MapReduce engines by 10x to 100x in terms of performance. This course introduces Apache Spark\, as well as some of its core librari es for data manipulation\, machine learning\, graph analytics\, etc.\n\nIn troduction to the core concepts of Apache Spark: RDDs and Basic Data Acces s.\n Hands on: get the most frequent term from a text.\n Processing semi-s tructured data with Spark SQL.\n Hands on: statistical processing from Dat a Sheets.\nCoffee break 16:00 - 16:30\n\nMultidisciplinary research and da ta analytics: Smart Cities (Maria Cristina Marinescu)\n Day 3 10/02Sessio n 1: 9:30am – 1pm Data analytics with Apache Spark - part 2 (Mario Macia s)\n\nMachine learning with Spark ML.\nCoffee break 11:00- 11:30\n\nHands on: clustering images according to their tags.\n Session 2: 2pm – 6pm ( Jordi Torres)\n\nHello World in TensorFlow\nIf you want to learn how to st art to program Deep Neural Networks\, working with TensorFlow is an excell ent way to start. TensorFlow is a machine learning library\, which aims to bring large-scale\, distributed machine learning and deep learning to eve ryone\, open-sourced last November by Google. This tutorial will takes you through the TensorFlow programming model one step at a time.\n\nHands-on exercises: beginning with basic machine learning models before moving on t o a deep neural network\, you will try out programming concepts as you lea rn them.\nCoffee break 16:00 – 16:30\n \n Day 4 11/02: Session 1: 9:30 am – 1pm (Alberto Abello)\n\nBig Data Management\n Big Data has many def initions and facets\, we'll pay attention to the problems we have to face to store it and how we can process it. More specifically\, we'll focus on the Apache Hadoop ecosystem and its two basic components\, namely HBase an d MapReduce engine.\nCoffee break 11:00- 11:30\n\nHands-on exercise\n  \n Session 2: 2pm – 6pm  Big (Javier Espinosa)\n\nData Visualisation\nData visualizations are everywhere and are more important than ever. From crea ting a visual representation of data points as part of an executive presen tation\, to showcasing progress\, or visualizing concepts for customer seg ments\, data visualizations are a critical and valuable tool in many diffe rent situations. When it comes to big data\, weak tools with basic feature s do not cut it so specific techniques should be applied. This course will address different techniques for visualizing big data collections includi ng a vision of the visualization process as a complex and greedy task and then as out of the box solution that can help to analyse and interpret big data collection.Coffee break 16:00 – 16:30\n\nHands-on exercise\nEND of COURSE\n\nhttps://events.prace-ri.eu/event/430/ SUMMARY:Big Data Analytics @ BSC URL;VALUE=URI:https://events.prace-ri.eu/event/430/ END:VEVENT END:VCALENDAR