HomeBIG DATADifference Between Data Science, Big Data and Data Analytics

Difference Between Data Science, Big Data and Data Analytics

Do you know what Data Science is? What about Big Data and Data Analytics? Surely you have heard that “data is the new oil”. In a very summarized way, we can say that a data is a value assigned to something. They can be qualitative, quantitative, for humans, for machines… we are immersed in a universe with the most varied data!

In this scenario, data opens the door to digital transformation. They generate insights, optimize processes and provide inputs for strategic decisions. Therefore, taking advantage of the moment to specialize in the area is a valuable opportunity for those who wish to stand out in the market and chart a successful career.

It is common to have doubts when choosing a specialization. In this article, we comment on the particularities and differences between Data Science, Big Data and Data Analytics, with the aim of providing adequate information for your choice to be clearer and more appropriate to your profile. 

What Is Big Data?

To know a little more about the concept of Big Data , it is important, first of all, to understand what data is. How are they generated? How do they influence our lives? How to best use this information?

Nowadays, almost everything we do generates data. At some stage, delivery or handling of information this issue may appear. Whether in banks, search engines, e-commerce, telephone services and many others, some data is recorded.

In this context, the concept of Big Data arises. Briefly, Big Data is related to the ability to describe the huge volume of data generated, in addition to its correct storage and analysis.

The Big Data differential is the possibility of crossing the data through the most varied sources, with the objective of obtaining valuable information and insights. That is, such analysis generates value for business, which makes it so essential in the market today.

In the universe of Big Data, we have two types of data structuring. Are they:

Structured Data

It is data that has an organized structure. They are separated by categories, clusters and definitions. They can inform contacts, sales, location, profiles, among many others.

They are usually found in databases, where the information is well defined. Some examples are CRM software, HR systems or financial systems.

Unstructured Data

They are the most complex to work with. There is no organized structure, it takes one person to read and prepare the information.

An example is data from social media such as YouTube, Instagram, news sites, etc., which deal with different types of data simultaneously. It takes human intervention to interpret interactions that robots cannot detect, such as irony in comments, sarcasm or an awareness of context.

What Is Data Science?

Data science or Data Science is the analysis of data in a more technical way. It requires in-depth knowledge of programming, analysis of unstructured data, and knowledge of specific technologies such as Python, SAS, Java, Perl, as well as platforms such as Hadoop and SQL.

Acts to find patterns in the collected data. They are more complex analyzes and methods, which involve statistics, mathematics, in addition to requiring the professional’s ability to solve problems. It thus allows extracting information from the collected data, providing guidelines for strategic action.

What Is Data Analytics?

Data Analytics is the science that intelligently examines raw data collected in Big Data and Business Intelligence tools. Through it it is possible to draw conclusions and various information about the analyzed data.

To work with data analysis, having the ability to transform data into clear and easy-to-understand information is essential. It is not enough to understand the calculations and graphics, it is necessary to be creative to present the results.

What Are The Differences Between The Three Terms?

The volume of data is the main difference between Big Data and Data Science. The first concept has a huge amount of data, which arrives very quickly and is of all types and origins.

The difference between Data Science and Data Analytics is basically in relation to technical knowledge. While the data scientist understands programming and knows how to use specific platforms, the data analyst needs to have a very developed analytical capacity and affinity with numbers and statistics. That is, knowing programming languages ​​helps, but it is not the focus of a Data Analytics specialist’s work.

How To Choose The Best Option To Specialize?

There is no right way to choose which option to specialize in. It is an individual choice, based on the profile and personal and professional goals. The important thing is to reflect on your creativity, analytical capacity, ability to solve problems and propose solutions, in addition to your affinity with programming and exact areas, such as mathematics and statistics.

If your desire is to follow a more analytical career, treating data and transforming them into reports, a Data Analytics course may be ideal. But if your interest is greater in artificial intelligence, programming, algorithms and other technology-related topics, the Data Science course is the most recommended.

But one thing is certain: Big Data is a booming field. The market is looking for qualified professionals to work with data and the time to qualify is now. For those who want to innovate and get ahead of competitors, a specialization in the area in a renowned location with trained professionals is one of the best ways to take your career to another level and set out in search of professional success.

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