Big Data

Course Identification

  Course code: ICTS 6361 (Master level course)

  Course title: Special Topics (1): Big Data

  Credit units: 3 units

  Prerequisite:

 Course Instructor: Dr. Eng. Rebhi Baraka (rbaraka@iugaza.edu.ps)

 Course description:

The course aims to introduce the key concepts and technical notions behind big data, covering fundamental topics such as Big Data characteristics, motivations and drivers for Big Data Adoption, Big Data infrastructure such as cloud computing, Big Data storage issues and techniques, Big Data processing techniques and Big Data analysis vs analytics. It also aims to provide students with hands-on and practical skills for working with Big Data tools and frameworks. Additionally, the course aims to expose the students to recent research topics/issues on Big Data and carry on research and presentation on these issues.

 

Objectives:

  • Introduce the student to fundamental concepts, technologies and tools of Big Data.
  • Provide the student with practical skills on tools and technologies related to Big Data collection, processing, analysis and summarization.
  • Familiarize the student with latest developments and advancements related to Big Data.
  • Provide the students with research knowledge to the state of the art and open research issues with Big Data particularly Big Data analysis as a means for addressing research questions.

Course outline

  • Big Data overview
  • Infrastructure for Big Data
  • Handling and processing Big Data
  • Big Data Analysis
  • Tools for Big Data Analytics
  • Research issues in Big Data

Methodology

Lectures, presentations, paper reviews, case studies, programming assignments, class discussions, reading assignments, research reporting and examinations.

Literature

The field of Big Data is moving fast and the literature becomes obsolete/old soon. We will depend on online resources a lot, but some fundamentals can still be found on the following books:

  • Kuan-Ching Li, Hai Jiang, Laurence T. Yang, and Alfredo Cuzzocrea. Big Data: Algorithms, Analytics, and Applications. Chapman & Hall/CRC Big Data Series, 2015.
  • Thomas Erl, Wajid Khattak, and Dr. Paul Buhler. Big Data Fundamentals: Concepts, Drivers & Techniques. The Prentice Hall Service Technology Series, 2016.
  • Mohammed Guller. Big Data Analytics with Spark A Practitioner’s Guide to Using Spark for Large Scale Data Analysis. Apress, 2015.

Grading

  • One Exam 40%
  • Research paper 20%
  • Programming assignments 20%
  • Paper reviews, written assignments, reading assignments and participation 20%

Notes:

  • Attendance in this course is obligatory. An absence of more than 15% of the lectures will result in dropping you automatically from the course.
  • I have the right to modify course contents as well as course requirements and grading as necessary.