It is natural for terms to be misunderstood in today’s rapidly changing world, especially when technologies evolve at a dizzying pace. People work with a massive amount of complex data, and this is why in the world of Big Data, some get certain concepts, jobs, and positions mixed up.
One of the primary confusions in this area is the difference between ‘Data Analytics’ and ‘Data Science.’ These two fields are quite similar yet have many uncommon things.
Although both work very closely at the periphery of mathematics, statistics, and programming, the purpose they serve is different. Hence the professionals required in these two need to have a different set of skills. It is essential for those looking forward to specializing in the Big Data sector to know what sort of knowledge and skill they must acquire if they opt for Data Analytics or Data Science. So if you are looking forward to making your career in any of these domains, pay close attention to this write-up.
Differences Between Data Science And Data Analytics
Several experts have tried for decades to limit the field of action of one discipline or another, with varying degrees of success. However, since the term Data Science began to be used in 1996 based on an article by Gregory Piatetsy Shapiro, definitions have come a long way.
What Is Data Science?
Nowadays, Data Science is considered a part of Big Data whose purpose is to extract and interpret the information derived from the enormous amount of data that a business collects, either by its activity or by its actions with third parties. To achieve this aim, data scientists are responsible for designing and implementing mathematical algorithms based on statistics, Machine learning, and other methodologies that allow businesses to have tools that provide them with an argument to act in one way or another according to the circumstances. Moreover, it is not only regarding collecting the data providers’ information and being able to make use of it, but the Data Scientists are also responsible for correcting the visualization of the detected patterns. This helps make them more transparent and readable by those accountable for decision-making.
When we consider Data Analytics, reference is usually made to a more specific application of Data Science. This is why in different industries in which data analysts have been incorporated, their role has been to search various sources of raw information to find trends and matrices that serve the businesses to make better decisions and obtain better results. In such cases, we should not confuse the work with the Business Intelligence area, which handles much smaller data, and hence, its capacity for analysis and prediction is more limited.
For this reason, the primary difference between Data Science and Data Analytics is found in the approach of each branch of Big Data. While the first is geared towards discovery, and its sights are much broader, the second is more focused on the operation of the various businesses in which it is applied and seeks solutions to existing issues.
Therefore, while the Data Scientist is in charge of trying to predict the future from previous patterns provided by the data, the Data Analyst is responsible for extracting the most relevant information derived from them. In some way, if any of these professionals ask themselves questions to try to glimpse what will happen in the upcoming years, the second is in charge of answering the questions already on the table.
What Are the Applications of Each Discipline?
Against their background, another primary difference between the two disciplines is their applications in different industries. Without deepening, Data Science has had a massive impact on search engines since its application in the algorithms provides better results in the queries made by users and does so in the shortest time possible. In the same way, it has been in the development of recommendation systems where data scientists have had an enormous impact. According to the previously-viewed content, as in the case of Netflix, or the products purchased as in the case of Amazon, these systems provide their customers with much more accurate suggestions, which significantly enhances the user experience.
Regarding Data Analytics, its application is more common in other areas such as health since it allows health centers to serve patients more efficiently. Data Analytics in other sectors, such as energy management, is also widespread. According to the analysis of this data, it is possible to optimize the use of resources in various places and even opt for the automation of different services to avoid unnecessary expenses. The hospitality industry also demands an analyst profile since it allows them to understand travelers’ preferences better and offer them substitutes that suit their tastes.
As demonstrated, there are several factors to consider if you are looking forward to entering the Big Data industry. Data Science and Data Analytics are very similar disciplines, but at the same time, they are so different that we understand it is challenging which path to choose. Now that you know the differences between these concepts, it may be more precise what path you are interested in taking to solve challenges with data. At Trigent, we strive to deliver a world-class BI and analytics experience to all our users. If you are looking for data analytics services contact us now!