In such a fast-paced world, it is not surprising that we sometimes confuse certain technical terms, especially when they are developing at such breakneck speed and new areas of science emerge overnight. This is the reason why in the big data world, in which enormous and complex amounts of information are dealt with, some people still confuse certain concepts, tasks and roles that occur in this emerging and growing discipline.

One of the biggest confusions in this area is the difference between data analysis and data science, two very closely related but distinctly different areas.

Although both lie at the intersection of math, statistics and development, their services have significantly different tangents, which means that the profiles of the professionals working in the two areas are also very different. It is important that anyone who wants to specialize in big data knows what kind of knowledge and skills they need to acquire if they choose either data analysis or data science.

The differences between data science and data analysis

For decades, experts have tried to narrow down the field of activity of one discipline or another, but they have not always been successful. However, since 1996, when the term “data science” began to circulate, definitions have come a long way and it seems we can now clarify the scope of both areas.

What is data science?

Data science is currently viewed as a branch of big data. Their goal is to extract and interpret information from the vast amount of data that is being collected by a given company, be it for its own use or for operations it may be performing with third parties. To achieve this, data scientists are familiar with the design and implementation of mathematical algorithms based on statistics, machine learning and other methods. This enables companies to employ tools that allow them to act one way or another depending on the circumstances and schedule. It is also not just about extracting information from the data collected and being able to use it. Data scientists are also charged with ensuring that the patterns they identify are visualized correctly so that they are clear and understandable to those who make decisions based on that data.

What about the data analysis?

In the data analysis , however, it is usually to be specific and precise application of data science. Therefore, in industries that have built in data analytics, the role of analysts has been to look for raw sources of information to find trends and metrics that will help companies make more accurate decisions and get better results. In this case we have to be careful not to confuse their work with that of someone in business intelligence who works with a much smaller amount of data, which means that their capacity for both analysis and prediction is more limited.

So the main difference between data science and data analytics is the big data branch, on which both areas focus: while the former is on the path to discovery with far-reaching goals, the latter is more focused on the process of different companies providing solutions for existing ones Apply and search for problems.

So, while data scientists are masters at predicting the future by basing their predictions on patterns from the past seen in the data, data analysts extract the most relevant information from the same datasets. You could say that the former asks questions to find out what will happen in the next few years, while the latter is responsible for answering questions that are already on the table.

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