I would argue that computational social science necessitates collaboration, and indeed is tamed by it. A collaborative approach provides the necessary structure, goals, and a critical approach to research methods. In response to the question of what computational social science has helped me achieve, it may seem obvious to mention the concrete projects, the outputs, the measurable outcomes. However, for me computational social science has achieved something more substantial and enduring—a new way of working, a new way of thinking, and a new kind of enthusiasm for research.
It’s an exciting time to be in social science. Social media, digital identities and the world of big data has opened up new ways for social scientists to study and examine social phenomenon.
Some examples include using online search patterns to predict the spread of disease, tracking near real-time Twitter data to understand political movements or using location data to understand interpersonal interactions.
The move to a digital world has created a innovative new area of social science called computational social science (CSS).
Being a data scientist with a sociological background is extremely valuable in trying to answer research questions to advance contemporary humanity. It goes beyond programming skills or just applying algorithms to data.
Teaching and learning resources from the 2018 Summer Institute for Computational Social Science have been made free to access online, allowing more people to explore in depth the field of computational social science.
"Positions in data science require a unique set of job skills that many professionals simply don’t possess. The level of programming knowledge, understanding of statistics and business sense make for a difficult position to fill. Because of this, many businesses find it difficult to hire appropriately for the position of data scientist." Kayla Matthews gives pointers on ways that companies, looking for data scientists, could stand out in this demanding market for data engineers.