Big Data Processes
AbstractThe goal of the course is to make students able to manage and use data sets, e.g. by learning about tools for data interpretation and visualization, and to reason about the use of data in larger contexts.
Organizations increasingly employ processes for collecting, generating, storing, governing and analyzing large amounts of data. Such Big Data Processes, based on the discovery of meaningful patterns and insights in large datasets, can be used to explain and predict complex phenomena.
In this class we will engage hands-on with all of the stages of a typical big data project, around a specific case. This includes the collection and generation of data, as well as its visualization and analysis for critical insights. We reflect on the technological and societal implications at every stage of the process. This includes discussions of how to derive value from big data processes as well as ethical and legal issues such as for instance the use of personal data.
This course is available to all DIM students. Non-DIM students should have basic literacy in a programming language (for instance R or Python), corresponding to an introductory course in programming or equivalent.
Intended learning outcomes
After the course, the student should be able to:
- Identify and describe technological and societal trends around Big Data
- Analyze how organizations can derive and maintain value from critical insights
- Design Big Data Processes to address specific research questions and organizational issues
- Conduct and report analytical insights gained from working with a case project, through visualization and metrical outputs
- Reflect on and discuss individual, organizational and societal implications of Big Data processes
Ordinary examExam type:
D: Submission of written work with following oral, External (7-point scale)
D2G: Submission for groups with following oral exam supplemented by the submission. Shared responsibility for the report.