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Data Science vs. Big Data vs. Data Analytics


Data Science | Big Data | Data Analytics | Data

Data is everywhere. In fact, the amount of digital data that exists is growing at a rapid rate, doubling every two years, and changing the way we live. According to IBM, 2.5 billion gigabytes (GB) of data was generated every day in 2012.

An article by Forbes states that Data is growing faster than ever before and by the year 2020, about 1.7 megabytes of new information will be created every second for every human being on the planet.

Which makes it extremely important to at least know the basics of the field. After all, here is where our future lies.

In this article, we will differentiate between the Data Science, Big Data, and Data Analytics, based on what it is, where it is used, the skills you need to become a professional in the field, and the salary prospects in each field.

What They Are

Data Science: Dealing with unstructured and structured data, Data Science is a field that comprises of everything that related to data cleansing, preparation, and analysis.


Data Science is the combination of statistics, mathematics, programming, problem-solving, capturing data in ingenious ways, the ability to look at things differently, and the activity of cleansing, preparing and aligning the data.

In simple terms, it is the umbrella of techniques used when trying to extract insights and information from data.


Big Data: Big Data refers to humongous volumes of data that cannot be processed effectively with the traditional applications that exist. The processing of Big Data begins with the raw data that isn’t aggregated and is most often impossible to store in the memory of a single computer.


A buzzword that is used to describe immense volumes of data, both unstructured and structured, Big Data inundates a business on a day-to-day basis. Big Data is something that can be used to analyze insights which can lead to better decisions and strategic business moves.


The definition of Big Data, given by Gartner is, “Big data is high-volume, and high-velocity and/or high-variety information assets that demand cost-effective, innovative forms of information processing that enable enhanced insight, decision making, and process automation”.


Data Analytics: Data Analytics the science of examining raw data with the purpose of drawing conclusions about that information.


Data Analytics involves applying an algorithmic or mechanical process to derive insights. For example, running through a number of data sets to look for meaningful correlations between each other.


It is used in a number of industries to allow the organizations and companies to make better decisions as well as verify and disprove existing theories or models.


The focus of Data Analytics lies in inference, which is the process of deriving conclusions that are solely based on what the researcher already knows.


The Skills you Require


To become a Data Scientist:

  • Education: 88% have a Master’s Degree and 46% have PhDs

  • In-depth knowledge of SAS and/or R: For Data Science, R is generally preferred.

  • Python coding: Python is the most common coding language that is used in data science along with Java, Perl, C/C++.

  • Hadoop platform: Although not always a requirement, knowing the Hadoop platform is still preferred for the field. Having a bit of experience in Hive or Pig is also a huge selling point.

  • SQL database/coding: Though NoSQL and Hadoop have become a major part of the Data Science background, it is still preferred if you can write and execute complex queries in SQL.

  • Working with unstructured data: It is most important that a Data Scientist is able to work with unstructured data be it on social media, video feeds, or audio.

To become a Big Data professional:

  • Analytical skills: The ability to be able to make sense of the piles of data that you get. With analytical abilities, you will be able to determine which data is relevant to your solution, more like problem-solving.

  • Creativity: You need to have the ability to create new methods to gather, interpret, and analyze a data strategy. This is an extremely suitable skill to possess.

  • Mathematics and statistical skills: Good, old-fashioned “number crunching”. This is extremely necessary, be it in data science, data analytics, or big data.

  • Computer science: Computers are the workhorses behind every data strategy. Programmers will have a constant need to come up with algorithms to process data into insights.

  • Business skills: Big Data professionals will need to have an understanding of the business objectives that are in place, as well as the underlying processes that drive the growth of the business as well as its profit.

To become a Data Analyst:

  • Programming skills: Knowing programming languages are R and Python are extremely important for any data analyst.

  • Statistical skills and mathematics: Descriptive and inferential statistics and experimental designs are a must for data scientists.

  • Machine learning skills

  • Data wrangling skills: The ability to map raw data and convert it into another format that allows for a more convenient consumption of the data.

  • Communication and Data Visualization skills

  • Data Intuition: it is extremely important for professional to be able to think like a data analyst.



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