Københavns Erhvervsakademi

en

Big data (EN)

2019/2020
Engelsk titel
Big Data (EN)
Uddannelse
Software udvikling
Uddannelsestype
Fuldtidsuddannelse
Niveau
Professionsbachelor (top-up)
Semester
6. semester
Fagets/modulets varighed
16 uger
Ects
10
Udd. element
Valgfag
Sprog
Engelsk
Opstart
Forår
Studiested
Lygten 37, København NV
Fagkode
9942256
Fag- /modulansvarlig
Andrea Corradini ‏
Christian Ole Kirschberg ‏
Constantin Alexandru Gheorghiasa ‏
  • Formål

    The course is designed to provide students with a comprehensive understanding of big data tools and techniques, related issues and the different kinds of big data ecosystems that can be used to support advanced data analytics. While the course considers Big Data management frameworks in general, it focuses on the Hadoop open source distributed data storage and processing platform and its underpinning sub-systems. Moreover, the course aims at providing students with a critical awareness of how big-data systems support data driven decision-making.

    Course Aims
    • To provide students with in depth knowledge of Big Data, the related relevant concepts and the technologies involved
    • To provide students with a comprehensive understanding of an open source software framework for distributed data storage and processing
    • To allow students to develop practical solutions to big data problems
    • To provide students with the basic capability to integrate and deploy Big Data management systems in the context of enterprises

    Viden

    At the end of the course, students will have comprehensive knowledge and critical understanding of:
    • the theories, models and frameworks underpinning the concept of Big Data
    • how to apply the standard Big Data techniques to design and implement IT systems that support business analytics
    • an open source software framework for distributed data storage and distributed processing, and its practical application
    • the issues involved in the deployment of distributed data processing pipelines
    • Big Data techniques in the wider context such as with respect to enterprise deployment and data security

    Færdigheder

    At the end of the course, students will have acquired the skills to:
    • design and implement IT systems that support business analytics using a Big Data ecosystem
    • master an open source software framework for distributed data storage and distributed processing
    • deploy distributed data processing pipelines

    Kompetencer

    At the end of the course, students will have acquired the competencies to:
    • analyze a development request with a view to constructing a Big Data ecosystem
    • select and apply suitable technologies for the development of IT systems that support business analytics

  • Undervisningsform og udfoldelse af læringsmål
    The teaching is organised as a variation between class teaching, guest lecturing, company visits, group project work and individual work. The learning is most often problem-based and cross-disciplinary and always practise-oriented. In addition to learning the subject, the student will gain the competences to work individually and in collaboration with others.

    The common aim of the activities is always to set clear intended learning objectives.
  • Prøve

    Læringsmålene for prøven er identiske med fagets/fagenes læringsmål

    Faget prøves
    Faget/modulet prøves selvstændigt
    Prøveform
    Mundtlig prøve
    Individuel eller gruppeprøve
    Individuel
    Anvendt sprog til prøven
    Engelsk
    Varighed
    Prøven startes med en fremlæggelse på 10 minutter. Derefter eksamineres den studerende 20 minutter inkl. votering.
    Bedømmelsesform
    7-trins skala
    Bedømmer(e)
    Intern censur
    Kriterier for prøvevurdering
    Der gives én samlet karakter ud fra en helhedsvurdering af fremlæggelsen og den efterfølgende eksamination.
68
timers undervisning
206
timers forberedelse
Tallene viser omfanget af arbejdsbelastningen relateret til faget fordelt på forskellige studieaktiviteter.

På faget Big data (EN) modtager du 68 timers undervisning, hvilket svarer til 90 lektioner (1 lektion = 45 min.) og 25% af din samlede arbejdsbelastning på faget.

Undervisningen vil primært bestå af følgende aktiviteter: klasseundervisning.
Forberedelsen vil primært bestå af følgende aktiviteter: læsning af egne noter, læsning af pensum, prøve, forberedelse til prøve.

Læs om KEAs studieaktivitetsmodel

*KEA kan fravige det angivne timetal, hvis det er begrundet i særlige forhold.