BEGIN:VCALENDAR VERSION:2.0 PRODID:icalendar-ruby CALSCALE:GREGORIAN BEGIN:VEVENT DTSTAMP:20260705T133658Z UID:93890685-7564-4d9f-8cbe-7caf4da29425 DTSTART:20210201T083000Z DTEND:20210205T153000Z DESCRIPTION:All the PATC courses at BSC are free of charge.\n\nCourse Conve ner:  Maria-Ribera Sancho\n\nObjectives: The course brings together key i nformation technologies used in manipulating\, storing\, and analysing dat a including:\n\n\n the basic tools for statistical analysis\n techniques f or parallel processing\n tools for access to unstructured data\n storage s olutions\n\n\nLearning outcomes: Students will be introduced to systems th at can accept\, store\, and analyse large volumes of unstructured data. Th e learned skills can be used in data intensive application areas.\n\nLevel : For trainees with some theoretical and practical knowledge\n\n \n\nDay 1 (Feb 1st)\n\n9:30 – 13:00 Introduction to Big Data (David Carrera\, Da ta Centric Computing Group Manager\, BSC)\nThe goal of this session is to introduce the students in the technologies associated with Big Data: data challenges\, cloud computing\, processing\, and internet of things. An ove rview of the technologies will be provided\, both from a technical and fro m a business model point of view\n11:00 - 11:30 Coffee break \n13:00 – 1 4:00 Lunch Break\n \n\n \n\n\n14:00 – 16:00 Practical Data Analytics f or Solving Real World Problems (Patricio Reyes\, Researcher\, BSC\; Maria Teresa Grifa\, Data Scientist\, Bridgestone EMA)\nData analytics has chang ed the way we make decisions. We see the benefits and the advances in many fields that go from financial to medical and industrial applications due to the integration of advanced data analytics. In this course we will prop ose practical tips gained through our experience at BSC in data analytics projects. We will also discover how to overcome some of the most challengi ng tasks in practical data analytics.\n\n16:00 – 16:30 Coffee break\n\n1 6:30 – 18:00 Hands-on (Patricio Reyes\, Researcher\, BSC\; Maria Teresa Grifa\, Data Scientist\, Bridgestone EMA)\nIn this session you will learn how to structure a data analytics project\, by following the methodology a nd the concepts introduced in the previous session. We will guide you thro ugh a step-by-step process to set up data science projects and start colla borating with the members of a team.\n\n\nDay 2 (Feb 2nd) \n\n9:30 – 13: 00 Big Data Management (Albert Abelló\, UPC\, inLab FIB)\nBig Data has ma ny definitions 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 foc us on the Apache Hadoop ecosystem and its two basic components\, namely HB ase and MapReduce engine.\n11:00 - 11:30 Coffee break \nHands-on exercise\ n13:00 – 14:00 Lunch Break\n14:00 - 16:00 NoSQL databases (Oscar Romero\ , Dept. of Service and Information System Engineering\, UPC-BarcelonaTech) \nThe relational model has dominated data storage systems since the mid 19 70s. However\, the changing storage needs over the past decade have given rise to new models for storing data\, collectively known as NoSQL. In this presentation\, we will focus on two of the most common types of NoSQL dat abases: document-oriented databases and graph databases and explain the us e cases suitable for each of them.\n16:00 - 16:30 Coffee break \n16:30 - 1 8:00 Multidisciplinary research and data analytics: Cultural Heritage (Mar ia Cristina Marinescu / Joaquim More / Artem Rashetnikov\, Computer Applic ations in Science&\;Engineering\, BSC)\nThis session will focus on Cult ural Heritage as an example of a field that can really take advantage of i ntegrating\, analyzing\, and reasoning with large amounts of data from man y heterogeneous sources. We will explain how to improve the quality and qu antity of open metadata associated with European Cultural Heritage (CH) im agery\, starting (mostly) from images of paintings and text. Our ultimate goal is to transcribe insights about culture\, symbols and traditions in a knowledge representation accessible to machine learning and artificial in telligence.\n\nDay 3 (Feb 3rd) \n\n9:30 – 13:00 Data Analytics with Apac he Spark (Josep Lluis Berral\, Computer Sciences - Data Centric Computing\ , BSC) \n11:00 - 11:30 Coffee break \nApache Spark has become a consolidat ed technology for large-scale processing in a fast and general way\, with “programmer-friendly” interfaces and official bindings for many of the most used languages (Java\, Scala\, Python and R)\, extensive documentati on and development tools. This course introduces Apache Spark\, as well as some of its core libraries for data manipulation\, machine learning\, dat a streams and graph analytics.\n13:00 – 14:00 Lunch Break\n14:00 – 15: 30 Data Analytics with Apache Spark. Part 2 (Josep Lluis Berral\, Compute r Sciences - Data Centric Computing\, BSC) \n16:00 – 16:15 Coffee break \n15:30 – 17:00 IoTwins: Modelling Mobility with Massive Amounts of Dat a (A H2020 European Project) (Eduardo Graells\, Mobility Data Scientist\, BSC) \nWhat are the decisions that people make when moving in and out of p laces? Having an answer would allow to design and build better and safer p laces for us to congregate and enjoy\, and to make efficient use of space. In IoTwins we aim to answer this question by studying how people move in the Camp Nou stadium\, through the analysis of massive amounts of data com ing from sensors and mobile platforms\, and the usage of machine learning models and agent-based simulations.\n\nDay 4 (Feb 4rth) \n\n9:30 – 13:00 Practical Introduction to programming Artificial Intelligence (Jordi To rres\, Emerging Technologies for Artificial Intelligence Group Manager - C omputer Sciences\, BSC) \n\n \n\nABSTRACT: The next generation of Artific ial Intelligence applications impose new and demanding computing infrastru ctures. How are the computer systems that support artificial intelligence? How to program it? \n\n \n\nCONTENT:\n\n \n\nArtificial Intelligence is a Supercomputing Problem\n\n \n\nProgramming Artificial Intelligence\n\n \n Getting Started with Deep Learning\n Deep Learning basic concepts \n Le arning Process of a Deep Neural Network\n\n\n \n\nScaling Artificial Inte lligence applications\n\n\n Scalable AI on Parallel and Distributed Infras tructures\n Training on Multiple GPUs\n Training on Multiple Servers\n\n\n  \n\n(*) Essential prerequisites to enroll in this course: It is assumed that the student has a basic knowledge of Python and Linux before starting the course.\n\nDay 5 (Feb 5th) \n\n9:30 – 13:00 Data Visualization Ther ory (Luz Calvo\, User Experience And Interaction Designer\, BSC and Juan F elipe Gomez Celis\, FrontEnd Developer\, BSC)\n\nData Visualization Theory (1h 30m)\n\n\n Basic concepts\n Human perception\n Design\n Colour\n Audi ence / Validation / Bad practices\n Visualisation design process\n\n\n\n[1 1:00 - 11:30 Coffee break]\n\nTools for data visualization (30m)\n\n\n Tab leau\n Data Wrapper\n RawGraphs\n Flourish\n\n\nD3.js  (1h30m)\n\n        D3.js Basics (Theory)\n\n       Case studies\n\n\nEND of COUR SE\nhttps://events.prace-ri.eu/event/1131/ SUMMARY:[ONLINE] PATC: Introduction into the Big Data Analytics @ BSC URL;VALUE=URI:https://events.prace-ri.eu/event/1131/ END:VEVENT END:VCALENDAR