Data science focuses on the inference and exploration of data to discover underlying patterns or provide solutions to problems. For one to succeed in the data science field, one must possess both computer and statistical skills.
These skills are important in providing solutions to complex problems. The data science discipline uses algorithmic and mathematical techniques to come up with solutions for analytical business problems.
Data scientists leverage raw data to unravel insights that might be useful in business operations.
How does one become a data scientist?
Requirements for Data Scientists
Just like other professions, the most successful data scientists start preparing themselves early in their lives. Before joining a university, they become proficient with programming languages such as R, Java, and Python. These languages are common in this field.
After that, they join a university and take an undergraduate degree with majors in mathematics, information technology, computer science, or statistics. One of the most preferred courses for data scientists is a Bachelor of Science in Management Information Systems.
After the degree, they can start working in an entry-level position or get an internship to gain the required experience.
Some key roles in data science teams include;
Data engineers are tasked with providing the required data or making it accessible to other team members. Their main job revolves around coding, developing, and designing applications to capture and clean data.
They are also responsible for making sure that there is consistency in the datasets used by the entire team.
Data architects are responsible for designing and maintaining data science facilities and applications architectures.
They create all the data models used in the team, manage them, and provide the required systems for data storage. They also design workflow processes followed by everyone in the team.
Data architects work closely with data engineers on large amounts of data as well as their sources to make sure that they are properly merged. These two roles are important when it comes to the success of the entire data science team.
Data developers’ main roles involve the process of designing, coding, and developing applications for large data analytics. These applications are used to support business or scientific processes in an organization.
Due to this role, data science teams can deploy models or rather use models that are in production. Data developers are supposed to be good at several things such as software development and data science at large.
Some teams refer to data developers as machine learning engineers. It is just the same role and both of them act as the bridge between software development and data science.
A data analyst is referred to as a business analyst in some data science teams. They are responsible for analyzing large sets of data to extract useful information. This information might be about the performance of a company, service, or even a system.
The extracted information is then converted into a format that is easy to understand for every other person in an organization. In other words, data analysts look at a problem, then shape it into a format that other team members can explore to come up with solutions.
Data scientists are among the most important people in a data science team. This does not mean that they are more important than the others – they all work as a team. Their role revolves around finding data sources and interpreting them.
They also merge the sources of data and create visualizations that are easy for everyone in the team to understand. Data scientists build models using machine learning to help extract insights that can be used in an organization.
However, data scientists are not software engineers. They might be good at machine learning programming languages but do not use their knowledge for software development. Data science team leaders need to differentiate the two roles.
In conclusion, we also have a process master and subject matter experts in data science teams. The process master is tasked with helping all other team members work towards achieving the required results for their organization.
On the other hand, subject matter experts are analytics experts who make sure that the insights generated are actionable.
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