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DESCRIPTION:Registration is now open. Please\, bring your own laptop. All 
 the PATC courses at BSC are free of charge.\n\nCourse Convener:  Maria-Ri
 bera Sancho\n\nObjectives: The course brings together key information tech
 nologies used in manipulating\, storing\, and analysing data including:\n\
 n\n	the basic tools for statistical analysis\n	techniques for parallel pro
 cessing\n	tools for access to unstructured data\n	storage solutions\n\n\nL
 earning outcomes: Students will be introduced to systems that can accept\,
  store\, and analyse large volumes of unstructured data. The learned skill
 s can be used in data intensive application areas.\n\nLevel: For trainees 
 with some theoretical and practical knowledge\n\nAgenda: \n\nDay 1 (Feb 3)
 \n\n9:30 – 13:00 Introduction to Big Data (David Carrera\, Data Centric
  Computing Group Manager\, BSC)\n\nThe goal of this session is to introduc
 e the students in the technologies associated with Big Data: data challeng
 es\, cloud computing\, processing\, and internet of things. An overview of
  the technologies will be provided\, both from a technical and from a busi
 ness model point of view.\n\n\n11:00 - 11:30 Coffee break \n13:00 – 14:0
 0 Lunch Break\n14:00 – 16:00 Practical Data Analytics for Solving Real W
 orld Problems (José Carlos Carrasco Jiménez\, Researcher\, BSC) \nData a
 nalytics has changed the way we make decisions. We see the benefits and th
 e advances in many fields that go from financial to medical and industrial
  applications due to the integration of advanced data analytics. In this c
 ourse we will propose practical tips gained through our experience at BSC 
 in big data analytics projects. We will also discover how to overcome some
  of the most challenging tasks in practical data analytics.\n16:00 – 16:
 30 Coffee break \n16:30 – 18:00 Hands-on (José Carlos Carrasco Jiménez
 \, Researcher\, BSC)\nThis session will focus on several key methods and a
 lgorithms (both serial and parallel) that enable to discover global proper
 ties on data while dealing with Big Data: \nNetwork Science\nMulti Constra
 ined and Multi-Objective Optimization\nExamples using the above approaches
  and some hands-on exercise\n\n \n\nDay 2 (Feb 4) \n\n9:30 – 13:00 Big 
 Data Management (Albert Abelló\, UPC\, inLab FIB)\nBig Data has many defi
 nitions and facets\, we'll pay attention to the problems we have to face t
 o store it and how we can process it. More specifically\, we'll focus on t
 he Apache Hadoop ecosystem and its two basic components\, namely HBase 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 r
 elational model has dominated data storage systems since the mid 1970s. Ho
 wever\, the changing storage needs over the past decade have given rise to
  new models for storing data\, collectively known as NoSQL. In this presen
 tation\, we will focus on two of the most common types of NoSQL databases:
  document-oriented databases and graph databases and explain the use cases
  suitable for each of them.\n16:00 - 16:30 Coffee break \n16:30 - 18:00 Mu
 ltidisciplinary research and data analytics: Smart Cities (Maria Cristina 
 Marinescu\, Computer Applications in Science&amp\;Engineering\, BSC) \n\n
 A huge quantity of data is produced in cities from many types of sources: 
 IoT\, social network\, other text sources\, images\, etc. Data integratio
 n is the first and more difficult step to ensure data quality and be able 
 to then analyze these data and get insight hat may help improve quality o
 f life\, sustainability\, and resilience of the urban fabrics. This sessio
 n focuses on the variety aspect of big data\, and modeling as a way to cap
 ture common sense and enable semantic reasoning.\n\nDay 3 (Feb 5)\n\n9:30 
 – 13:00 Data Analytics with Apache Spark (Josep Lluis Berral\, Computer 
 Sciences - Data Centric Computing\, BSC) \n11:00 - 11:30 Coffee break \nAp
 ache Spark has become a consolidated 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\, Pyt
 hon and R)\, extensive documentation and development tools. This course in
 troduces Apache Spark\, as well as some of its core libraries for data man
 ipulation\, machine learning\, data streams and graph analytics.\n13:00 
 – 14:00 Lunch Break\n14:00 – 15:30 Data Analytics with Apache Spark. P
 art 2 (Josep Lluis Berral\, Computer Sciences - Data Centric Computing\, 
 BSC)  \n16:00 – 16:15 Coffee break \n15:30 – 17:00 European project on
  Big Data \n\nDay 4 (Feb 6) \n\n9:30 – 13:00 Practical Introduction to 
 Python Deep Learning  (Jordi Torres\, Emerging Technologies for Artificia
 l Intelligence Group Manager - Computer Sciences\, BSC)\nArtificial Intell
 igence is changing our lives\, and solutions based on Deep Learning are le
 ading this transformation. Deep Learning is now of major interest to compa
 nies and research centers\, since it can be applied to many areas of activ
 ity. But getting started in this technology is not an easy task. The purpo
 se of this short course is to gradually start the student off to the basic
 s of Python Deep Learning\, in a practical way through a guided\, hands-on
  learning without becoming too technical\, ensuring that the student learn
  enough of the basics to get literate in Deep Learning. Using the Keras AP
 I of TensorFlow library allows the development of Deep Learning models and
  abstracts much of the mathematical complexity involved in its implementat
 ion. The course content will be as follows:\n\nPART 1: INTRODUCTION\n1. Wh
 at is Deep Learning?\n2. Work environment\n3. Python and its libraries\n\n
 PART 2: FUNDAMENTALS OF DEEP LEARNING\n4. Densely connected neural network
 s.\n5. Neural networks in Keras\n6. How a neural network is trained\n7. Pa
 rameters and hyperparameters in neural networks\n8. Convolutional neural n
 etworks.\n\nPART 3: DEEP LEARNING TECHNIQUES\n9. Stages of a Deep Learning
  project\n10. Data to train neural networks\n11. Data Augmentation and Tra
 nsfer Learning\n12. Advanced neural network architectures\n\nPART 4: GENER
 ATIVE DEEP LEARNING\n\n13. Recurrent neural networks\n14. Generative Adver
 sarial Networks\n\nImportant prerequisites to enroll in this course: It is
  assumed that the student has a basic knowledge of Python prior to startin
 g the course.\n\n11:00 - 11:30 Coffee break \n13:00 – 14:00 Lunch Break\
 n14:00 - 16:00 From Data Mining to Data Science (Tomàs Aluja\, UPC – Ba
 rcelona Tech)\nData contains information. We will try to contextualize the
  flow of apparently “new” concepts such as data mining\, business inte
 lligence\, big data\, data science and how they relate to the old school o
 f exploratory statistics. We will also introduce an overview of the main s
 teps of a data mining problem\, and we will illustrate them through sound 
 examples of application.\n16:00 - 16:30 Coffee break \n16:30 – 18:00 Dat
 a analytics in societal challenges modeling: smart mobility and other rela
 ted fields (Dra. Mari Paz Linares i Jamie Arjona (UPC\, inLab FIB) \nInter
 net of Things\, Big Data\, Smart cities or Industry 4.0 are concepts that 
 have raised in the last years with promises of solving daily human issues.
  In this session we will present how a combination of Internet of Things a
 nd Big Data can attack certain challenges and alleviate them.\n\nDay 5 (Fe
 b 7) \n\n9:30 – 13:00 Data Visualization Therory (Luz Calvo\, User Exper
 ience And Interaction Designer\, BSC and Juan Felipe Gomez Celis\, FrontEn
 d Developer\, BSC)\nTherory \n\n\n\n	Basic concepts\n	Human perception\n	D
 esign\n	Colour\n	Audience / Validation / Bad practices\n	Visualisation des
 ign process\n\n\n11:00 - 11:30 Coffee break \n\nTools for data visualizati
 on \n– Tableau\n– Data Wrapper\n– RawGraphs\n– Flourish\n– Carto
 \n\nData visualisation with d3.js\n\n\nEND of COURSE\n\n \n\n \nhttps://
 events.prace-ri.eu/event/910/
SUMMARY:Big Data Analytics @ BSC
URL;VALUE=URI:https://events.prace-ri.eu/event/910/
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