Digital Research Methods and Big Data Analytics (BDS200)

Content

This course introduces students to essential digital research methods, approaches and tools that are applicable to business context. It further provides students not only with an understanding of the key stages of the digital research project design, but also emphasizes the need to critically reflect upon the assumptions inherent within such endeavors. Concerning the data collection process per se, both qualitative and quantitative approaches are discussed, including naturalistic approaches, digital experiments and surveys, and the collection of various forms of non-reactive data. What follows is a lengthy discussion of how, depending on theoretical perspective, methodological framework, purpose of research, and digital medium, digital datasets can be analyzed. In this light, the course provides students with the practical experience of applying the machine learning approach to analyze quantitative BD (e.g., through decision trees in SPSS) and qualitative BD (e.g., through content analysis in Leximancer) so as to transform large volumes of data into meaningful information. Finally, having considered the speed of development of the Big Data domain and associated phenomena, such as social media, the ethical modernday considerations and problems are about to be examined.

Learning outcome

By the end of the course, the participants will gain the following knowledge, skills and general competencies:

Knowledge

The candidates have knowledge about:

• the subject’s history, philosophical underpinnings, social science research traditions, and their place in a datadriven business world.
• terminology, fundamental theories, tools and instruments, as well as methodological approaches that are associated with digital research methods.
• recognize selected strategies for analyzing large volumes of qualitative and quantitative data (Big Data).
• ethical modern-day considerations and pitfalls associated with the application of Big Data in the business context.

Skills

The candidates will be able:

• to apply acquired knowledge to design digital research projects, and further analyze and interpret acquired information, and report the findings coming from conducted digital research investigations.
• to find, evaluate and refer to available and rapidly developing information base associated with digital research methods and Big Data analytics.
• to use selected machine learning analytical approaches to transform large quantities of data into information that can aid in optimizing one’s business models and decision-making processes.

General competences

The candidates will be able:

• to identify and formulate research problems that can be addressed through the implementation of digital research methods.
• to plan and carry out diverse digital work tasks and projects that extend over time, that are executed alone and/or as a part of a group, and which are in line with existing ethical requirements and guidelines.
• to exchange views and experiences with professionals on the application of digital research methods and Big Data topics, and through this contribute to the development of good practice.
• to contribute to creative thinking and innovative processes in business context that are anchored in digital research methods and Big Data analytics.

Required prerequisite knowledge

BHO160 Introduction to Mathematics and Statistics, BRH220 Research methods in the social sciences

Exam

Coursework requirements

Compusory exercise 1, Compusory exercise 2

Two mandatory written assignments must be submitted and passed (grading scale: Approved/ Not approved) in order to be admitted to the final exam.

Course teacher(s)

Course coordinator:

Sigbjørn Barlaup Pedersen

Course coordinator:

Lukasz Andrzej Derdowski

Method of work

The course consists of a combination of lectures, group and plenary discussions, compulsory assignments, lab sessions with relevant software, and self-study. Students are expected to prepare prior the classes by reading the part of the syllabus that is relevant for each lecture.

Open for

Course assessment

There must be an early dialogue between the course supervisor, the student union representative and the students. The purpose is feedback from the students for changes and adjustments in the course for the current semester.In addition, a digital subject evaluation must be carried out at least every three years. Its purpose is to gather the students experiences with the course.

Literature