Big Data vs Data Science vs Data Analytics

In today’s world there is data everywhere. The amount of existing digital data is doubling every two years and keeps growing at a rapid rate. Data is changing the way we live. According to IBM, even back in 2012 we has 2.5 billion gigabytes (GB) of data generated daily.

Data is growing faster than even before, according to an article by Forbes. Experts estimate that about 1.7 megabytes of new information for every individual on the planet will be created every second by the year 2020.

This is what we can expect for the future ahead. This means that it is very important to get familiar at least with the basics of the field. For this reason we will explain in this post the differences between Big Data, Data Science, and Data Analytics based on their definitions, skills you need to become a professional in the field, and where they are used.

What These Concepts Are

Data Science: This is a field comprising everything related to data preparation, cleansing, and analysis. It can be dealing with both structured and unstructured data. Data Science is made up of a combination of capturing data, mathematics, statistics, problem-solving, and programming. It is much based on the ability to look at things differently. On short, Data Science comprises all the techniques used to extract information and insights from data.

Big Data: Big Data is related to humongous volumes of data not possible to be effectively processed with the traditional applications. Big Data processing begins with the non aggregated raw data. Due to its large volume, this raw data is often not possible to be stored in a single computer’s memory.

A buzzword that is used to describe immense volumes of data, both unstructured and structured, The Big Data label is used to describe immense volumes of information, both structured and unstructured produced by a business on a day-to-day basis. A Big Data tool can be used to extract insights leading to strategic business moves and better decisions.

According to Gartner’s definition of Big Data this is high variety, high velocity and high volume information assets that require innovative and cost-effective ways of information processing in order to achieve enhanced decision making, insight, and process automation.

Data Analytics: This is the science of examining raw data in order to make conclusions about that information. Data Analytics is based on applying a mechanical or an algorithmic process in order to extract useful insights. For instance, this algorithm may involve running through a number of data sets in order to find meaningful correlations.

Data Analytics is used in several industries to enable companies verifying existing models and theories and making better decisions. Its focus lies in inference, a process of deriving conclusions based solely on what is already known by the researcher.

Each Field’s Applications

Read more on Scribble&Scroll Blog.