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, and limitations, at every relevant 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:
- Analyse and discuss technological and societal trends around Big Data
- Analyse and discuss how organizations can derive value from critical insights
- Design a process and develop a model to derive insights from case related data
- Conduct and report analytical insights gained from working with a case project, through visualization and metrical outputs
- Analyse and discuss individual and societal implications of Big Data processes
The course consists of lectures and exercises.
The lectures will focus on gaining a theoretical and methodological understanding of Big Data processes by reading and discussing relevant literature. In addition, you will be introduced to demonstrations and worked examples of analytics and visualization. Moreover, we will also reflect upon the epistemological, ethical, and political premises and consequences of Big Data practices in different societal sectors. Throughout the course, you will work in groups applying these techniques to a project that will serve as the basis for your final report.To acquire the skills required to work on their project, during the exercise sessions, you will work in small groups on hands-on tasks. The problems can revolve around a specific type of analytics or visualization (e.g., exploratory data analysis, classification, predictive analytics), a specific application domain (e.g., financials, transportation), or a particular challenge of Big Data processes (e.g., handling of personal data). Furthermore, you will practice communicating and presenting your results and reflections during the exercises as preparation for the final report.
The course literature is published in the course page in LearnIT.
Student Activity BudgetEstimated distribution of learning activities for the typical student
- Preparation for lectures and exercises: 40%
- Lectures: 20%
- Exercises: 20%
- Exam with preparation: 20%
Ordinary examExam type:
C: Submission of written work, external (7-trinsskala)
CG: Submission of written work for groups.
Reports are handed in by groups and students are evaluated on the basis of demonstration of fulfillment of the intended learning objectives for the course.
The hand in should be 15 pages + 2 pages per group member. Each group should have 3-5 members, and a description of each student's contribution to the project work and the report should be clearly stated in the report.
- Groups: 3-5 members
Time and dateOrdinary Exam - submission Fri, 29 May 2020, 08:00 - 14:00
Reexam - submission Wed, 8 Jul 2020, 08:00 - 14:00