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Data Science vs Data Analytics – Everything You Need To Know
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Data Science vs Data Analytics – Everything You Need To Know 

At present, more than 3.7 billion humans use the internet. Moreover, we humans create 2.5 quintillion bytes of data every single day – a number that is expected to grow exponentially with each passing year.

Data never sleeps and in today’s world, without utilizing the wealth of digital information available at our fingertips, a brand or business risks missing vital insights that can help it grow, scale, evolve, and remain competitive.

Concerning the collection, understanding and handling of digital data, there are two key disciplines that currently lead the way: data science and analytics. Although these two fields cross over, and share many of the same characteristics, the two are strikingly different in many ways.

That said, to spare you any confusion and offer you a clearcut insight into these two innovative fields, here we explore data science vs data analytics in a business context, starting with an explanation of the science.

What Is Data Science?

Data science focuses on uncovering answers to the questions that we may not have realized needed answering. Experts in the field utilize techniques to drill down into complex data, combining computer science, predictive analytics, statistics, and machine learning.

At its core, it is a comprehensive field centered on sourcing innovative insights from broad sets of raw and structured digital data.

The goal is to find tangible solutions to new problems which, in turn, can help organizations take the knowledge of their operational abilities, their competitors, and their industry, to new and innovative heights.

What Is Data Analytics?

Primarily, data analytics is focused on processing and conducting critical statistical analysis on current or existing data sets. The main role of a data analyst is to create methods to capture, collect, curate process, and arrange data from different sources.

In that process, a final view of uncovering actionable insights to existing problems or challenges must be the analysts’ crucial factor in tinkering the data analytics operations.

In doing so, data analysts establish the most proficient ways to present available data, solving problems and providing actionable solutions aimed at achieving immediate results, often to the everyday operations or functionality of an organization, whether  it is utilized in small business analytics or big enterprises.

Data Science vs Data Analytics

When it comes to data science vs analytics, it’s important to not only understand the key characteristics of both fields but the elements that set them apart from one another. While people use the terms interchangeably, the two disciplines are unique. Put simply, they are not one in the same – not exactly, anyway:

Data science is an umbrella term for a more comprehensive set of fields that are focused on mining big data sets and discovering innovative new insights, trends, methods, and processes.

Data analytics is a discipline based on gaining actionable insights to assist in a business’s professional growth in an immediate sense. It is part of a wider mission and could be considered a branch of data science.

Concerning our study of “data science vs data analytics,” another notable difference between the two fields boils down to investigation. Typically, science doesn’t drill down into specific queries; instead, its committed to arranging colossal data sets to expose fresh insights. Data analysis, by its very nature, is most effective when it’s based on specific goals, providing tangible answers to questions based on existing insights. By using data analysis tools to achieve comprehensive intelligence can make crucial impact on obtaining a sustainable business development.

In summary, science sources broader insights centered on the questions that need asking and subsequently answering, while data analytics is a process dedicated to providing solutions to problems, issues, or roadblocks that are already present.

As such, these two fields are incredibly interconnected, often working in tandem to deliver the same goals: growth and improvement. While we may be talking about “data analytics vs data science,” it’s worth noting that these two fields complement one another rather than working against each other.

In our hyper-connected digital age, data is our sixth sense; by understanding both fields, you stand to improve your business in a number of vital areas, from marketing and customer service through to financial reporting and analysis, staff engagement, operational efficiency, and beyond.

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