TECHNOLOGIES FOR BIG DATA MANAGEMENT AND ANALYTICS

Course Information
TitleΤΕΧΝΟΛΟΓΙΕΣ ΕΠΕΞΕΡΓΑΣΙΑΣ ΚΑΙ ΑΝΑΛΥΣΗΣ ΜΕΓΑΛΩΝ ΔΕΔΟΜΕΝΩΝ / TECHNOLOGIES FOR BIG DATA MANAGEMENT AND ANALYTICS
CodeIS-27
FacultySciences
SchoolInformatics
Cycle / Level2nd / Postgraduate
Teaching PeriodWinter
CoordinatorApostolos Papadopoulos
CommonNo
StatusActive
Course ID600000820

Programme of Study: PPS School of Informatics (2014-today)

Registered students: 5
OrientationAttendance TypeSemesterYearECTS
TECΗNOLOGIES GNŌSĪS DEDOMENŌN KAI LOGISMIKOUElective Courses belonging to the selected specialization117.5
TECΗNOLOGIES PLĪROFORIAS KAI EPIKOINŌNIŌN STĪN EKPAIDEUSĪElective Courses117.5
PSĪFIAKA MESA- YPOLOGISTIKĪ NOĪMOSYNĪElective Courses117.5
DIKTYAKA SYSTĪMATAElective Courses117.5

Class Information
Academic Year2015 – 2016
Class PeriodWinter
Faculty Instructors
Weekly Hours3
Class ID
600011284
Type of the Course
  • Scientific Area
  • Skills Development
Course Category
Specific Foundation / Core
Mode of Delivery
  • Face to face
Digital Course Content
Language of Instruction
  • Greek (Instruction, Examination)
  • English (Instruction, Examination)
Learning Outcomes
1. Students will get important knowledge in big data management and analytics 2. They will work in teams 3. They will be more confident by presenting their work in class 4. They will get in contact with modern big data analytics techniques with a lot of applications in Industry.
General Competences
  • Apply knowledge in practice
  • Retrieve, analyse and synthesise data and information, with the use of necessary technologies
  • Work autonomously
  • Work in teams
  • Generate new research ideas
Course Content (Syllabus)
Introduction to Big Data Management and Analytics - Hadoop: basic and advanved topics - The Hadoop ecosystem: HDFS, Hbase, Pig, Hive - NoSQL databases - Theoretical issues in MapReduce - The Scala programming language - The Spark platform: basic and advanced issues - Streaming, SQL, Machine Learning, GraphΧ: the basic libraries - Data exploration using SparkR - Algorithm design in Spark - Graph databases - Other systems: Giraph, GraphLab, Hama, BlinlkDB
Keywords
big data, data management, data mining from big data, big data analytics
Educational Material Types
  • Slide presentations
  • Book
Use of Information and Communication Technologies
Use of ICT
  • Use of ICT in Course Teaching
  • Use of ICT in Laboratory Teaching
  • Use of ICT in Communication with Students
  • Use of ICT in Student Assessment
Course Organization
ActivitiesWorkloadECTSIndividualTeamworkErasmus
Lectures391.3
Reading Assigment1003.3
Project551.8
Written assigments321.1
Total2267.5
Student Assessment
Student Assessment methods
  • Written Exam with Extended Answer Questions (Summative)
  • Written Assignment (Formative, Summative)
  • Performance / Staging (Formative, Summative)
  • Written Exam with Problem Solving (Summative)
  • Report (Formative, Summative)
Bibliography
Additional bibliography for study
H. Karau, A. Konwinski, P. Wendell, M. Zaharia: Learning Spark, O' Reilly, 2015. N. Lynch: Distributed algorithms, Morgan Kaufmann, 1996. I. Robinson, J. Webber, E. Eifrem: Graph databases, O' Reilly, 2013. S. Ryza, U. Laserson, S Owen, J. Wills: Advanced analytics with Spark, O'Reilly, 2015. R. Schutt, C. O'Neil: Doing Data Science, O' Reilly, 2014. C.A. Varela, G. Agha: Programming distributed computing systems: a foundational approach, The MIT Press, 2013.
Last Update
16-10-2015