Join Barton Poulson for an in-depth discussion in this video Roles, part of Data Science Foundations: Fundamentals.
- [Voiceover] Many different roles are involved in data science, and each of these roles brings different critical skills and insights to the process. We can begin with the engineers and the developers. These are people who focus on the hardware, and the software to support the data science. It's the backend that makes data science work possible. This includes titles like, data engineers and developers, and database administrators. Next, there are big data specialists.
These are people with heavy computer science and mathematics backgrounds who engage, for instance, in a lot of machine learning, after that are researchers. Now, these are people who focus on domain-specific research, and these people often have much more expertise in the statistical element of data science, and less of an emphasis on the backend, on the hardware, or the computer science part of it. Next, are analysts. Now, these are people who focus on day-to-day data tasks, that can include things like web analytics, or working with a SQL database, or data visualizations.
Next, is the business person. This is the person who manages the data science project, and probably, most importantly, frames a business-relevant question that the project is designed to answer, and helps understand, interpret, and implement the solutions. Now, while this person's primary focus is on business, and possibly management, they still must speak data. They need to understand how the data is used, what it's able to tell, what it's strength, and what it's limitations are, in order to be able to do their job well.
Next, we have entrepreneurs, often involved in what are called data-base startups. They often need substantial creativity in the planning and execution of their data science projects, and the business relevance of those projects. Finally, there's the person we can call the full-stack data scientist, and this is a person, who at least in theory is able to do every element of data science at peak performance. Now, these people are very rare, which is why they are also called by the nickname unicorns, 'cause a mythical creature with a magic abilities, it essentially doesn't exist, but where you can get a full-stack data scientist, you're able to make enormous progress, and work very efficiently towards answering important questions.
So, what are our conclusions here? First, data science involves diverse roles. There's a lot of people involved in it, and they all have different things they need to do to contribute to the success of the project. Second, each of these roles involves different goals, or emphases, and different skills. The things that a database administrator does are not the same thing that an entrepreneur in a data start up do, not the same thing that a manager in a large database organization would do.
And finally, each of these people operates in a different context, that means they have again, different goals, motivations, methods, constraints, and meanings implied in their own work.
- The demand for data science
- Roles and careers
- Ethical issues in data science
- Sourcing data
- Exploring data through graphs and statistics
- Programming with R, Python, and SQL
- Data science in math and statistics
- Data science and machine learning
- Communicating with data