Copenhagen School of Design and Technology

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Big Data (EN)

2018/2019
Danish title
Big data (EN)
Study programme
Software Development
Type of education
Full time education
Level of education
Academy Profession
Semester
6. semester
Duration of the subject/module
16 weeks
Ects
10
Programme elements
Elective
Language
English
Start time
Spring
Location
Lygten 37, København NV
Subject number
9942256
Responsible for the subject(s)/modul(es)
Andrea Corradini
Christian Ole Kirschberg
Constantin Alexandru Gheorghiasa
  • Content and learning outcomes

    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

    Knowledge

    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

    Skills

    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

    Competences

    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

  • Type of instruction
    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.
  • Exam

    The learning outcomes of the exam are identical with the learning outcomes of the subject(s)/modul(es)

    Exam in one or more subjects
    Subject/module is tested standalone
    Type of exam
    Oral examination
    Individual exam or group exam
    Individual
    Exam languages
    English
    Duration
    The student must give a 10-minute presentation, followed by a 20-minute examination of the student, including grading.
    Type of evaluation
    7-point grading scale
    Examiners
    Internal censure
    Exam criteria
    One aggregate grade is awarded based on an overall assessment of the presentation and the
    following examination.
68
hours of teaching
206
hours of preparation
The figure shows the extent of workload related to the subject divided into different study activities.

In the subject Big Data (EN) you will receive 68 hours of instruction, which corresponds to 90 lessons (1 lesson = 45 min.) and 25% of your total workload for the subject.

The teaching primarily consists of the following activities: classroom teaching.
The preparation primarily consists of the following activities: reading your own notes, reading the curriculum, exam, preparation for the exam.

Read about KEAs Study Activity Model

*KEA can deviate from the number of hours if this is justified by special circumstances