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Big-Data Analytics

What Will Learn?

  • Course Aims
    - Students will gain knowledge on analyzing Big Data. It serves as an introductory course for graduate students who are expecting to face Big Data storage, processing, analysis, visualization, and application issues on both workplaces and research environments. - Gain knowledge on this fast-changing technological direction. Big Data Analytics is probably the fastest evolving issue in the IT world now. New tools and algorithms are being created and adopted swiftly. Get insight on what tools, algorithms, and platforms to use on which types of real world use cases. - Get hands-on experience on Analytics, Mobile, Social and Security issues on Big Data through homeworks and final project - Final Project Reports will be published as Proceedings and Final Project Software will become Open Source.
  • Course Goals
  • Decent Work and Economic Growth
  • Industry, Innovation and Infrastructure
  • Sustainable Cities and Communities

Requirements

PHM113 AND

Description

  • English Description

    Definition and taxonomy. Challenges, trends, Applications. Big data technologies and tools. The Hadoop ecosystem. The map-reduce paradigm. Big data storage and analytics. Big data analytics machine learning algorithms. Graph analytics. Big data visualization.
  • Arabic Description

    Definition and taxonomy. Challenges, trends, Applications. Big data technologies and tools. The Hadoop ecosystem. The map-reduce paradigm. Big data storage and analytics. Big data analytics machine learning algorithms. Graph analytics. Big data visualization.
  • Department
    Computer and Systems Engineering
  • Credit Hours
    3
  • Grades
    Total ( 100 ) = Midterm (25) + tr.Major Assessment (30 = tr.Industry 0% , tr.Project 15% , tr.Self_learning 5% , tr.Seminar 15% ) + tr.Minor Assessment (5) + Exam Grade (40)
  • Hours
    Lecture Hours: 2, Tutorial Hours: 2, Lab Hours: 0
  • Required SWL
    125
  • Equivalent ECTS
    5
  • 1. Boris lublinsky, Kevin t. Smith, Alexey Yakubovich, “Professional Hadoop Solutions”, st Edition, Wrox, 2013.
  • 2. Chris Eaton, Dirk Deroos et. al., “Understanding Big data”, Indian Edition, McGraw Hill, 2015.
  • 3. Tom White, “HADOOP: The definitive Guide”, 3rd Edition, O Reilly, 2012.
  • 4. Vignesh Prajapati, “Big Data Analytics with R and Hadoop”, 1st Edition, Packet Publishing Limited, 2013. - Boris lublinsky, Kevin t. Smith, Alexey Yakubovich, Professional Hadoop Solutions, st Edition, Wrox, 2013.