Big Data (EN)2019/2020
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.Knowledge
• 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
At the end of the course, students will have comprehensive knowledge and critical understanding of:Skills
• 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
At the end of the course, students will have acquired the skills to:Competences
• 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
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 and
practical application of learning objectives
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.
The learning outcomes of the exam are identical with the learning outcomes of the subject(s)/modul(es)Exam in one or more subjectsSubject/module is tested standaloneType of examOral examinationIndividual exam or group examIndividualExam languagesEnglishDurationThe student must give a 10-minute presentation, followed by a 20-minute examination of the student, including grading.Type of evaluation7-point grading scaleExaminersInternal censureExam criteriaOne aggregate grade is awarded based on an overall assessment of the presentation and the
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