Data Science in Research, Business and Society
AbstractThis course will provide students with a substantive discussion of applications and opportunities for data science in addressing real world problems coupled with an overview of field-specific research approaches for addressing these problems.
There are two sides to data. On the one hand, researchers and analysts can look to data as a source of insight and an object that can be manipulated to produce outcomes that can then be interpreted or acted upon. On the other hand, people produce data in the course of living without much ability to control what effect the traces we produce might have on our lives and futures.
The goal of this course is to relate the technical content of other courses to critical concerns about data and data science approaches. Students will learn different ways people might think about data in business, research and society at large.
The topics and approaches covered in this course include but are not limited to:
- Domain-specific approaches to asking questions along with the reasons for why questions might need to be asked differently
- Translating technical concepts to real-world concerns through research-based language
- Knowledge claims in different research traditions
- Empirical methodologies in different research traditions
- Ethical implications of data-driven practices
Formal prerequisitesThe course is mandatory for first semester BSc in Data Science students.
Please note that this course is only available for students enrolled in the BSc in Data Science.
Intended learning outcomes
After the course, the student should be able to:
- Account for different definitions of data, different data types and different research approaches that generate it
- Identify the knowledge claims underlying different interpretations of data
- Explain the difference between quantitative and qualitative approaches to data generation
- Examine the implications of data collection for research, business and society
- Discuss different debates about the implications of data for people in organizations and society
- Reflect on the ethical implications of data collection and processing in different contexts
The course is built around four modules exploring approaches to data science from different perspectives. Students will engage in weekly group activities producing content for a data journal. A selection of entries to the data journal will become the basis for their exam.
Mandatory activitiesCompleting a data journal with weekly assigned entries is the mandatory activity for this course. The data journal will be handed in at the end of each module to receive feedback. Students who have not handed in weekly assignments can hand them in all together in week 13 of the course to qualify for the exam.
The student will receive the grade NA (not approved) at the ordinary exam, if the mandatory activities are not approved and the student will use an exam attempt.
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: 20%
- Lectures: 25%
- Exercises: 20%
- Project work, supervision included: 25%
- Exam with preparation: 10%
Ordinary examExam type:
C: Submission of written work, Internal (7-point scale)
C11: Submission of written work
Students will produce an individual report on a specific real world application of data science approaches, chosen from a pre-given list of domains and supplementary literature.