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Excerpt from course description

Understanding Organizations and Leadership Through Advances in Computational Social Science

Introduction

The world produced 120 zettabytes of data in 2023. On WhatsApp, around 100 billion messages are sent daily. It would take 100s of millions of years to download all the data available on the internet. This digital revolution provides social scientists and organizations with an enormous opportunity. The availability of large-scale human data, combined with computational advances, such as social networks, large language models, and artificial intelligence can greatly inform our understanding of social behavior and organizational systems.

Welcome to the young field of Computational Social Science (CSS). CSS harnesses large datasets and novel data sources, computational advances, and social scientific theories to reveal novel insights into individual and group behavior. With rapid advancements in data availability and computation, we are no longer beholden to small samples, basic study designs, and simple analyses. New methods and techniques allow us to study and understand topics like organizations, leadership, and psychological phenomena from a brand-new light.

You will learn about the tools researchers and organizations can use to access and collect large-scale data, such as APIs, data scrapping, and human computation, as well as the recent computational methods to understand this data, such as social networks, natural language processing, and machine learning. A series of guided in-class tutorials will also teach you the basics underlying these skills and tools so that you can apply these methods outside of the course. Throughout the course, we will consider the ethical concerns accompanying the digital revolution, as well as appreciate the existing limits of human and machine computation.

Taken together, the course will set you apart from other graduates by placing you at the forefront of the digital revolution, giving you the cutting-edge skills needed to understand human behavior, transform organizations, and solve institutional problems.

The course is open to students at any level—it is not a data science or computer science course.

I look forward to diving into the exciting world of CSS with you!

Prerequisite: None. Coding or data analysis experience is not required. Though you will learn how to use existing tools, the course is not a data science or computer science course.

Course content

The course covers the following topics                                  

  • The structure and dynamics of social systems.
  • Social cognitive processes, including phenomena like social contagion and wisdom of crowds.
  • Understanding organizations and groups in terms of modern computational methods, including social networks, natural language processing, machine learning, and artificial intelligence. 
  • Novel sources of data (e.g., geo-location, sensors) in organizations.
  • The ethics of data privacy in organizations and beyond.

Disclaimer

This is an excerpt from the complete course description for the course. If you are an active student at BI, you can find the complete course descriptions with information on eg. learning goals, learning process, curriculum and exam at portal.bi.no. We reserve the right to make changes to this description.