The big data world has a wide variety of problems, causing some natural differentiation in the specific roles that a data scientist may undertake. Also, the profession has not been properly defined yet, so depending on various aspects of one’s background, such as education, the data scientist role can be further differentiated. Based on some research that was done on the topic by a group of scientists.
there are four types of data scientists: data developers, data researchers, data creatives, and data businesspeople. Often encountered among the most experienced professionals of the field is a fifth type, a mixed/generic combination of these. While there is a certain overlap among all of these categories (e.g., they are all familiar with data analysis methodologies, big data technology, and the data science process), they are generally quite different from one another in several ways. Let’s examine each one of them in more detail.
There are five different types of data scientists:
- Data developers
- Data researchers
- Data creatives
- Data businesspeople
- Mixed/generic
Data Developers
Data developers usually focus on the more technical issues of data management and data analysis. In other words, their day-to-day work involves getting the data from various sources and organizing it in large databases, querying those databases for meaningful results, and analyzing the results to derive useful information from them.
Data developers tend to be programmers with strong coding and machine learning skills since these are the skills that are most essential for this particular specialty. Their business or statistics skills may be relatively immature, depending on their education and work experience. Data developers are ideal for certain parts of the data science work, the bigger picture of which we will examine later on once the specific parts become clear. Data developers may not produce the most robust analyses, which is why they usually team up with other data professionals and designers. Still, they provide value for the companies for which they work, and they can always develop the skills they lack through courses, workshops, etc. data science course
Data Researchers
Data researchers usually come from the academic world, demonstrating a strong background in statistics or any of the sciences that employ statistics (e.g., social sciences). They also tend to have PhDs in a significantly higher proportion than any other types of data scientists. Business skills are usually not their strong suit, but they are excellent analysts. This particular attribute of theirs is great in cases where a lot of groundbreaking work needs to take place.
Data researchers are often a very good asset for larger organizations as part of a data science team along with other professionals who complement this type of data scientist by contributing programming and business skills, things that are essential for the creation of useful data products.
Data Creatives
Data creatives usually have considerable academic experience and are exceptionally good at big data technologies (i.e., software designed for big data governance and analysis), machine learning, and programming. They tend to be devoted users of open source software and boast a broad-based skill-set. This enables data creatives to move with little effort from one role to another, acting as the Swiss Army knives of the data science field. Not the most business savvy of professionals, they are good at doing the day-to-day work of a data scientist but may require help in making others see its value.
Data Businesspeople
Data businesspeople are usually the senior data scientists who lead data science teams (which they sometimes build from scratch). They are adept in business skills and are great project managers. Their focus is mainly on increasing the revenue of a company, and they are concerned with the bigger picture. Nevertheless, they can also be down-to-earth since they have substantial technical expertise.
Data businesspeople tend to be found in larger organizations or their start-ups. They are great at dealing with other professionals, particularly businesspeople, and often have extensive experience in every aspect of the data science process. This kind of data scientist usually has other data scientists and data professionals working for him and has a project management role in the data science projects in which he is involved. Learn more data science online training
Mixed/Generic Type
Mixed/generic data scientists are like data businesspeople but without the broad experience or the intense business focus. They are more balanced than the other types of data scientists and are more likely to grow into the higher echelons of the field faster than the first three types. Their skill-set includes programming, statistics, and business skills, and they are very flexible, much like the data creatives but with a better understanding of the business world. Most new data scientists who study data science at a younger age tend to be of this type since they develop their skills in a more holistic manner Mixed/generic data scientists are good for any kind of company, can work very well independently as well as part of a team, and are quite enthusiastic about the field.
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