Data science involves people with diverse roles, such as project managers, developers, analysts, and others. Learn how to find the data science role that works best for you in this video.
- [Presenter] Data science is fundamentally a team sport. There are so many different skills and so many different elements involved in a data science project that you're going to need people from all sorts of different backgrounds with different techniques to contribute to the overall success of the project. I want to talk about a few of these important roles. The first one is the data engineers. These are the developers, and the system architects, the people who focus on the hardware and the software that make data science possible. They provide the foundation for all of the other analyses. They focus on the speed, and the reliability, and the availability of the work that you do. Next are machine learning specialists. These are people who have extensive work in computer science and in mathematics. They work in deep learning. They work in artificial intelligence. And they're the ones who have the intimate understanding of the algorithms and understand exactly how they're working with the data to produce the results that you're looking for. And then, an entirely different vein are people who are researchers, and by that I mean topical researchers. They focus on domain-specific research like, for instance, physics and genetics are common, so is astrophysics, so is medicine, so is psychology, and these kinds of researchers, while they connect with data science, they are usually better versed in the design of research within their particular field and doing common statistical analyses, that's where their expertise lies, but they connect with data science in that they're trying to find the answers to some of these big-picture questions that data scientists can also contribute to. Also, any business doing its job has analysts. These are people who do the day-to-day data tasks that are necessary for any business to run efficiently. Those include things like web analytics, and S-Q-L, that's SQL or Structured Query Language, data visualizations, and the reports that go into business intelligence. These allow people to make decisions. It's for good business decision-making that lets you see how you're performing, where you need to reorient, and how you can better reach your goals. Then there are the managers. These are the people who manage the entire data science project, and they're in charge of doing a couple of very important things. One is they need to frame the business-relevant questions and solutions. So, they're the ones who have the big picture. They know what they're trying to accomplish with that. And then, they need to keep people on track and moving towards it. And, to do that, they don't necessarily need to know how to do a neural network, they don't need to make the data visualization, but they need to speak data so they can understand how the data relates to the question they're trying to answer, and they can help take the information that the other people are getting and putting it together into a cohesive whole. Now, there are people who are entrepreneurs. And, in this case, you might have a data-based startup. The trick here is you often need all of the skills, including the business acumen, to make the business run well. You also need some great creativity in planning your projects and the execution that get you towards your entrepreneurial goals. And then there's the unicorn, also known as the rock star, or the ninja. This is a full-stack data scientist who can do it all, and do it at absolute peak performance. Well, it's a nice thing to have, on the other hand, that thing is very rare which is why we call them the unicorn. Also, you don't want to rely on one person for everything. Aside from the fact that they're hard to find, and sometimes hard to keep, you're only getting a single perspective or approach to your business questions, and you usually need something more diverse than that. And what suggests is the common approach to getting all the skills you need for a data project, and that is by team, and you can get a unicorn by team where you can get the people who have all the necessary skills, from the foundational data engineer, to the machine learning specialist, to the analyst, to the managers, all working together to get the insight from your data and help your project reach its greatest potential in moving your organization towards its own goals.
- Assess the skills required for a career in data science.
- Evaluate different sources of data, including metrics and APIs.
- Explore data through graphs and statistics.
- Discover how data scientists use programming languages such as R, Python, and SQL.
- Assess the role of mathematics, such as algebra, in data science.
- Assess the role of applied statistics, such as confidence intervals, in data science.
- Assess the role of machine learning, such as artificial neural networks, in data science.
- Define the components of effective data visualization.