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Data Science vs Data Analytics – Everything You Need To Know

Data science vs data analytics

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.

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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.

“Data is the new science. Big Data holds the answers.” – Angela Ahrendts, Senior VP of Retail, Apple

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.

To understand this exciting field in more depth, explore our article on best data science books.

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.

“Information is the oil of the 21st century, and analytics is the combustion engine.” – Peter Sondergaard, former Senior VP of Gartner

For further reading on the subject, here are the top 15 big data and data analytics books you need to know about.

Data Science vs Data Analytics

Data, business. science - word cloud representation

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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Drilling Down: The Core Differences And One Striking Similarity – Analytics vs Data Science

Main differences between data science vs data analytics in a visual table

Moving on in our data analytics vs data science journey, we’re going to take a look at the primary differences of each discipline in more detail, starting with the intended audience.

  • Skillset

Looking at data science vs data analytics in more depth, one element that sets the two disciplines apart is the skills or knowledge required to deliver successful results.

Concerning data analytics, a solid understanding of mathematics and statistical skills is essential, as well as programming skills and a working knowledge of online data visualization tools, and intermediate statistics. Data analysts and are well versed in SQL, they know some Regular Expressions, and can slice and dice the data.

In the field of science, a comprehensive understanding of SQL database and coding is required, in addition to a firm grasp of working with large sets of unstructured metrics, and insights. The data scientist needs more “complex” skills in data modelling, predictive analytics, programming, data acquisition, and advanced statistics. In essence, they need to have quite a bit of machine learning and engineering or programming skills which enable them to manipulate data to their own will.

  • Scope

When we use the word “scope” concerning data analytics vs data science, we’re talking big and small, or more specifically, macro and micro.

Essentially, as mentioned, science is, at its core, a macro field that is multidisciplinary, covering a wider field of data exploration, working with enormous sets of structured and unstructured data.

On the other hand, data analytics is a micro field, drilling down into specific elements of business operations with a view to documenting departmental trends and streamlining processes either over specific time periods or in real time, therefore, concentrating mostly on structured data. There are many data analytics examples that can illustrate real-life scenarios and impact on a business.

  • Exploration

While both disciplines explore a wide range of industries, niches, concepts, and activities, typically science is used in major fields of corporate analytics, search engine engineering, and autonomous fields such as artificial intelligence (AI) and machine learning (ML).

Data analytics is a concept that continues to expand and evolve, but this particular field of digital information expertise or technology is often used within the healthcare, retail, gaming, and travel industries for immediate responses to challenges and business goals.

  • Goals

Another critical element that sets analytics and data science apart is the ultimate aim or goal of each discipline.

While we’ve already alluded to this notion, it’s incredibly important and worth reiterating: the primary goal of science is to use the wealth of available digital metrics and insights to discover the questions that we need to ask to drive innovation, growth, progress, and evolution. With the main aim of using existing information to uncover patterns and visualize insights in specific areas, data analytics is geared toward sourcing actionable data based on specific aims, operations, and KPIs.

  • Big data

Differences aside, when exploring data science vs analytics, it’s important to note the similarities between the two – the biggest one being the use of big data.

At this point, you will understand that each discipline harnesses digital data in different ways to achieve varying outcomes. But despite their differences, both work with big data in ways that benefit an industry, brand, business, or organization.

Businesses that choose to leverage the full potential of big data analytics can optimize their operational margins by up to 60% – and as both fields focus on big data, the rewards of exploring science and analysis have the potential to be great.

Business, data analytics concept and illustration

Data Science And Analytics: A Force To Be Reckoned With

“Data is a precious thing and will last longer than the systems themselves.” – Tim Berners-Lee, the inventor of the World Wide Web.

If utilized to their fullest potential, both science and analytics are a force to be reckoned with – two areas that can enhance your business’s efficiency, vision, and intelligence like no other disciplines can.

When it comes to connecting with your data – using it in a way that can uncover new insights while using current insights to ensure the sustainable progress of your business – choosing the right tools or online reporting software is essential.

Boasting self-service analytics platforms in addition to a host of intuitive, insightful, and actionable data dashboards, utilizing tools that are not only accessible but will yield the results you deserve is of utmost importance for any business.

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