Key Responsibilities of Professional Data Scientists
- Writing and Checking Code
- Application of Machine Learning
- Handling and Managing Data
- Team Collaboration and Communication
- Thinking from a Business Perspective
While the profession is incredibly diverse and has applications throughout most major industries, there are a few core aspects shared among most data science jobs. Essentially, data scientists provide value to their employer by helping them leverage the power of information through applied mathematics, statistics and computer science. There are many different kinds of career opportunities in the field, but there are a few skills that are useful just about anywhere.
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1. Writing and Checking Code
Early data scientists weren’t necessarily bound to computers in their activities or studies, but modern ones certainly are. A diverse base of fundamental programming skills is practically essential for people looking to enter the profession, although some companies position data scientists alongside developers to maximize both skill sets. Python is a particularly common language in data science, and a lot of work also involves C++ or Java.
2. Application of Machine Learning
Machine learning has quickly risen to dominate discussions and developments in applied data science. This term describes the practice of designing and training software programs to learn from their environment over time, allowing them to evolve and adapt to better perform their function. In data science, this type of learning is usually accomplished by priming programs with large sets of data. Some industry experts believe that machine-assisted programs will grow significantly in the years ahead, according to Forbes.
3. Handling and Managing Data
Data scientists typically spend a lot of their time working with data, which includes gathering, analyzing, cleaning and storing it in a communal digital environment. There are a lot of potential mistakes that can jeopardize the data or results obtained from its analysis, so scientists absolutely need to know how to navigate these systems successfully. Depending on their specific responsibilities, data scientists may also frequently apply statistical or mathematics skills while working with data.
4. Team Collaboration and Communication
Data science professionals usually work as part of a team within a larger organization. Even though they spend a lot of time working on a computer, people skills can be just as important for establishing a successful career. Basic communications skills are essential, as well as the ability to comfortably present information to an audience, collaborate on projects and educate others.
5. Thinking from a Business Perspective
Students of data science know that most of the career paths ahead of them are in business and commercial industries. Even though they are focused on the actual management and analysis of data, they always have to keep in mind that they are doing this to create value for their employer. This means they need to think like their company executives so they can understand company priorities, identify opportunities and filter out things that aren’t important.
Data science is a rapidly growing profession that has taken many industries by storm, with applications for everything from customer service to city planning. The core aspects of most data science jobs are really only a foundation for a much broader and specialized skill-set tailored for the industry or position in question.