How do data analytics relate to data science? While both terms are sometimes used interchangeably, there are distinct differences between the two disciplines. While data science and data analytics have overlapping areas, a breakdown between the two highlights areas where they differ.
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Data science focuses on obtaining usable information from massive sets of both structured and unstructured data. One way of looking at it is getting answers for things the user doesn’t realize the user doesn’t know. It’s a form of solving a mystery in which the user doesn’t realize is a mystery — until answers are provided to questions that weren’t asked.
How are those answers found? There are a variety of factors, including elements of predictive analytics, machine learning, AI segmentation, as well as statistics and basic computer science. An article in Forbes explains the way data scientists operate is to find avenues to focus upon by placing more attention on locating the right question to ask, rather than attempting to focus on finding specific answers. They take the macro view, as opposed to the micro view.
Data analytics operates within the parameters of processing analysis on known datasets. The data analyst focuses on capturing data in raw form, processing it further to distill it into a form that can be organized. Basically, the data analyst looks to answer questions that need answers — but it is important to emphasize the data analyst actually has questions that need answers from the very start of having that data processed.
Wide Focus Versus Narrow Focus
Data science can be thought of as a “wide focus” for the data. If one views raw data in terms of photography, data science looks for a panoramic overview and data analytics looks to come in for a much more narrow focus. Data analytics is actually within the parameters of data science.
Data science isn’t worried about answering specific questions. What it does is take all this massive raw data and coming at it from different angles. Doing this can reveal important insights that would not have been noticed through a more narrow focus.
The Future of Data
In order to see where one potential future of data science and data analysis might lead, simply look at where the current state of data science is. A Forbes article revealed that data science careers are currently at an all-time high, with an increase of over 75% in job postings for data scientists and analysts. While many of the jobs currently being held by data scientists are being automated, there are still several areas available for future data scientists to explore, ranging from industry specialists to data engineers.
In the end, whether an individual is a data analyst or a data scientist, the relation between the two is both complex and symbiotic. The data scientist needs data analytics to provide the overview data necessary to consider new ways of looking at data and the data analyst needs the questions raised by data scientists to be answered in order to assist businesses and organizations in their operations.