Are you a… Data Scientist, Data Engineer or Data Analyst?
Big data and data science are relatively new fields, but we can already see how it’s transforming our daily lives, organizations and roles. As a result, we can find great job opportunities for various data-related professions. In fact, data professionals are in the Top 10 most-wanted job careers at the moment.
Today we study the three key roles which are Data Scientist, Data Analyst and Data Engineer. Of course, there is room for more than one kind of person to be called a data professional, but let’s focus today on these three roles and how they are changing business.
What is a Data Scientist?
A data scientist is a person who does data science. They can work in any industry sector and company that uses data and processes it. As you can imagine, this means that they can work, basically, everywhere.
Data scientists play two main roles in their organization’s data and intelligence processes. First, they help visualize the data infrastructure, such as the data warehouse, the data pipeline, and the data marts. Second, they work on the problems, creating new models or improving the existing ones, analyzing data, writing programs, and building dashboards. They may work closely with business analysts and/or data engineers.
In this field, they will often work with business managers, who ask questions from data analysts. The main difference is that data scientists use the data sets produced by data analysts. They also sometimes collaborate with statisticians on more complicated problems, which means that data scientists do not have to know it all. While they often use statistical methods, statisticians know which methods are more appropriate for certain types of problems.
Another usual collaboration for data scientists is with data engineers. Data engineers, as we will discuss later, are the people who build and maintain the data science infrastructure and data marts.
What is a Data Analyst?
Data analysts, like any other kind of analyst, examine and interpret data. Today, however, they usually do this with a combination of software and business acumen; we tend to think of them as a specific set of tools and techniques. The tools and techniques that make up this discipline are useful for solving problems, but what makes us call someone a “data analyst” is that we see these tools and methods being used.
This art is a practice of thinking about those tools in a specific way. Different types of problems require different ways of thinking, and so do different types of analysts.
This kind of data expert is a special kind of generalist. Most people may be specialists in some fields, but not experts in everything. A data analyst knows how to handle information in general. The skills that make an analyst valuable (pattern recognition, communication, analytical thinking) are the same skills that make someone good at everything else. A professional’s value is based on more than just the tools they use — they’re thinking people, not just data-driven systems. All these qualities will be more valuable in the future than in the past. But this role is not new; it has always been important to find ways to give meaning to things.
What is a Data Engineer?
Data engineers work on a variety of data-related projects. They undertake projects ranging from implementing data cleanup processes on existing systems to deploying entirely new data architectures.
People in this discipline are often the bridge between data scientists and developers. They understand the ins and outs of how data is stored, accessed, and processed, and become a great resource for data scientists, bridging the gap between data analysts and programmers. Their main goal is to design and implement data pipelines. A data pipeline is a designed set of processes and tools used to process and analyze data. They are often part of an organization’s data science or business intelligence teams, although they can be just as valued in positions like data architect or data analyst.
Data engineers usually need a bachelor’s degree in computer science, statistics, or related disciplines. While a computer science degree is not required, it is helpful because it provides fundamental knowledge of programming, data structures, algorithms, and computer architecture. Commonly used by large companies, they can be useful in some areas that require special processing of data.
The field of data is rapidly expanding, and data science is such a broad field that job titles are often misleading. Even if we only focus on data scientists, there are endless possibilities. These specialists have a mastery of statistics, computer science, and mathematics. However, because it is a relatively young field, there is no set procedure for becoming a data science expert, so communication skills are also required. Anyone who wants to become a data scientist usually starts with a degree in computer science or statistics.
Now it’s your turn! Are you building a career in data? Don’t you know where to start? If you wish to learn more about this field, follow us on Medium, our web and our social media (Twitter and LinkedIn).
