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
The course consists of lectures and exercises.
The lectures will focus on gaining a theoretical understanding of Big Data processes by reading and discussing relevant literature. In addition, you will be introduced to examples of analytics and visualization to illustrate some of the concepts and techniques. Moreover, we will also reflect upon the epistemological, ethical, and political premises and consequences of Big Data practices in different sectors. Throughout the course, you will work in groups, applying these theories and techniques, on a project that will serve as the basis for your final report.
To develop relevant skills for the project work, you will work in small groups on hands-on tasks during the exercise sessions. Each session will help illustrate particular methods and theories in practice, for instance with regards to analytics (e.g., exploratory data analysis, classification, predictive analytics), specific application domains (e.g., finance, transportation), or particular challenges within Big Data processes (e.g., handling of personal data, societal impact assessments and maintenance). 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:
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.
Reports are handed in by groups of 3-5 students. Reports have a length of 10 pages + 2 pages per group member (not including figures).
Students are evaluated on the basis of demonstration of fulfillment of the intended learning objectives for the course. The evaluation is based on the report together with an oral examination based on the report and the course syllabus.
Individual exam : Individual student presentation followed by an individual dialogue. The student is examined while the rest of the group is outside the room.