Syllabus for Big Data Analytics

Big data analytics

  • 15 credits
  • Course code: 2IS077
  • Education cycle: Second cycle
  • Main field(s) of study and in-depth level: Information Systems A1N
  • Grading system: Fail (U), Pass (G), Pass with distinction (VG)
  • Established: 2019-10-24
  • Established by: The Department Board
  • Applies from: Autumn 2020
  • Entry requirements: 90 credits in information systems or the equivalent
  • Responsible department: Department of Informatics and Media

Learning outcomes

Regarding knowledge and understanding, the student should be able to

  • Define key concepts and identify technologies in the field of Big Data,
  • Define, categorize and describe the different forms of Big Data and how they can be analysed,
  • Explain the challenges of analysing Big Data,
  • Explain digital methods and technologies for Big Data analytics, such as statistical analysis, text mining, and machine learning,
  • Describe the ethics, governance, and sustainability challenges relating to Big Data.

Regarding competence and skills, students should be able to

  • Design an approach for analysing Big Data based upon particular needs, including selecting appropriate digital methods, technologies, and governance strategy for storage and processing data,
  • Conduct an analysis of Big Data using the appropriate digital methods at scale,
  • Use appropriate digital methods to interpret and share results of Big Data analyses.

Regarding judgement and approach the student should be able to

  • Critically evaluate and discuss the implications of using different digital methods for Big Data analytics,
  • Evaluate what combinations of Big Data technologies can be used for a given set of requirements,
  • Discuss the implications of digitization and the adoption of Big Data in mainstream society.

Content

Big Data is a fast-evolving field where employers are increasingly desiring skilled strategists and practitioners in the area. It is often said that data is "the new Oil". For many organisations, this analogy may be true - data often needs to be sought out, with great effort required to find it and pre-process it for ready consumption. For others, data can be considered "the new Carbon" - it already exists ubiquitously as an abundant by-product of ongoing processes, much like CO2 in our atmosphere. It is already there to be captured, cleaned, and consumed. Certainly, the scale and diversity of data, and rate at which data is generated brings new challenges to digital organisations and in designing the supporting digital infrastructure.

This course will provide students an in-depth understanding of the methods and technologies to tackle three key characteristics of Big Data: the volume, variety, and velocity of data (the "3 V's"). The course will introduce students to (i) cloud and high-performance computing and storage infrastructure for dealing with large volumes of data; (ii) data analytics methods, such as data integration, statistical analysis and machine learning, for analysing a wide variety of data in combination; and (iii) stream processing for performing analysis on high velocity data sources. The design of digital infrastructure to address these challenges will be discussed and students will gain hands-on experience of applying data analytics methods in the context of each of the 3 V's. Finally, the effects of Big Data on society will be discussed, including ethical, sustainability, and social aspects, as well as how to address them in strategic planning and governance.

Instruction

Lectures, seminars, and laboratory exercises.

Assessment

Seminars, laboratory exercises, and assignments. Compulsory attendance is required for some elements.

If there are special reasons for doing so, an examiner may make an exception from the method of assessment indicated and allow a student to be assessed by another method. An example of special reasons might be a certificate regarding special pedagogical support from the University's disability coordinator or a decision by the department's working group for study matters.

Reading list

The reading list is missing. For further information, please contact the responsible department.

Last modified: 2022-04-26