Essentially, Data Science is an interdisciplinary field of study that encompasses the theory and application of methods used to generate, collect, store, process, analyze, visualize, model, and serve data, and insights or predictions. Additionally, it includes the orchestration and management of those processes and the people involved. Oftentimes, Data Science is described as the intersection between Computer Science, Math & Stats, and Domain Knowledge.

Data Science includes many sub-disciplines such as computer science, mathematics, software engineering, project management, business intelligence, and traditional research. Data Science can be applied to any field of study, industry, business, or domain at varying scales and levels of complexity, as long as there is some data to play with.

Because of the breadth and depth of the field, successful teams need members with diverse skill sets and domain expertise. Three crucial components bring more people into the conversation.

• The development of low-code and no-code tools has made it easier to bring people from outside traditional STEM disciplines into the field to contribute with their unique backgrounds and expertise.
• Increased data literacy, or data fluency, is allowing for data-driven conversations to happen at all levels. Data literacy education is important to teach people how to read and interpret graphs, things to look out for, and questions to ask. When data-fluent people are embedded into the data science process, they can contribute their skill sets and enrich the conversations and outputs with their points of view which helps achieve better results for more people.
• Outreach is an important component of building a robust data team. The impacts of data models and algorithms are far-reaching, so we need to challenge traditional views of who belongs on the team.

A well-rounded team is crucial to getting good results. They will undoubtedly achieve better results than a team of computer scientists, or a team of social scientists.

Data Science and the Data Science team are ever-evolving things in organizations. They can be found in companies, universities, and even in grass-roots citizen data science collectives. There is no single skill set that makes someone a data scientist, nor is there a standard team configuration, workflow, or set of tools. If you are looking to define data science, the best thing to do is be specific about what domain or problem set you are interested in.