Big Data vs. Data Science vs. Data Analytics

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 were 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 know the basics of the field at least. After all, here is where our future lies.

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

1. Big Data

Definition – What does Big Data mean?

Big Data is a term used to describe a collection of data that is huge in size and yet growing exponentially with time. In short such data is so large and complex that none of the traditional data management tools are able to store it or process it efficiently.

How does it work?

Big data refers to a process that is used when traditional data mining and handling techniques cannot uncover the insights and meaning of the underlying data. Data that is unstructured or time-sensitive or simply very large cannot be processed by relational database engines.

Data in its raw form has no value. Data needs to be processed in order to be of value. However, herein lies the inherent problem of big data. Is processing data from native object format to a usable insight worth the massive capital cost of doing so? Or is there just too much data with unknown values to justify the gamble of processing it with big data tools? Most of us would agree that being able to predict the weather would have value, the question is whether that value could outweigh the costs of crunching all the real-time data into a weather report that could be counted on.

2. Data Science

Definition of Data Science

Data science is a “concept to unify statistics, data analysis, machine learning, and their related methods” in order to “understand and analyze actual phenomena” with data. It employs techniques and theories drawn from many fields within the context of mathematics, statistics, information science, and computer science.

Where it is implemented.

Dealing with unstructured and structured data, Data Science is a field that comprises everything that related to data cleansing, preparation, and analysis. In simple terms, it is the umbrella of techniques used when trying to extract insights and information from data.

Data science is a multi-disciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from data in various forms, both structured and unstructured, similar to data mining.

3. Data analytics

What Is Data Analytics?

Making sense of Big Data is the domain of Data Analytics. There are various tools and techniques which are deployed in order to collect, transform, cleanse, classify, and convert data into easily understandable data visualization and reporting formats.

Data Analytics refers to the set of quantitative and qualitative approaches to deriving valuable insights from data. It involves many processes that include extracting data and categorizing it in order to derive various patterns, relations, connections, and other such valuable insights from it. Today, almost every organization has morphed itself into a data-driven organization, and this means that they are deploying an approach to collect more data that is related to their customers, markets, and business processes. This data is then categorized, stored, and analyzed to make sense out of it and derive valuable insights from it.

What is the use of Data Analytics?

Data analytics is the science of analyzing raw data in order to make conclusions about that  information.

The techniques and processes of data analytics have been automated into mechanical processes and algorithms that work over raw data for human consumption. Data analytics help a business to optimize its performance.

• Comparison between Big Data vs. Data Science vs. Data
  Analytics.

Basis of differentiation	Big Data	Data Science	Data Analytics
Definition	Unprocessed data sets of humongous volumes	Science of cleaning, preparing and aligning the data for analysis using statistical and mathematical models	It is related to examining raw data which is required to provide conclusive information
Applications	Financial services	Delivery of better search results on the internet	Gaining efficiency in the Healthcare
	Fraud analytics	Digital advertisements from display banners to finding the appropriate prospects	Optimization of buying experience through mobile and social media data analysis.
	Communication industry to retain and expand the consumer base	The recommender system to help in the user experience	Collection of data in the gaming industry
	Brick-and-mortar and online retailer for better customer service		Energy management
	In order to become a big data professional, the following skills are required:
	• Analytical skills
	• Creativity
	• Mathematics and                   statistical skills
	• Basic computation                 knowledge
	• Computer science and          business skills
Skill requirements	In order to become a big data professional, the following skills are required:	• Education with a Master’s degree in               either data analysis, statistics, or                     mathematics	Following skills are necessary to become a data analyst:
	• Analytical skills	• Knowledge of SAS or R or SPSS	• Programming skills
	• Creativity	• Knowledge in coding on Python and               Hadoop	• Statistical and mathematics skills
	• Mathematics and                  statistical skills	• Working efficiency with unstructured data	• Machine learning skills and certificates
	• Basic computation                knowledge		• Data visualization skills
	• Computer science and          business skills		• Analytical skills
			• Data wrangling skills

Conclusion:

In any stint of big data vs. data science vs. data analytics, one thing is common for sure and that is data. So, all the professionals from these varied fields belong to data mining, pre-processing, and analyzing the data to provide information about the behavior, attitude, and perception of the consumers that helps the businesses to work more efficiently and effectively. While big data is associated with large volumes of data, data analytics is used to process the data to extract information and valuable knowledge with the help of a tool known as data science.