The Big Three – Data Science,Big Data and Data Analytics

Data science :

Data science deals with the process of extracting knowledge from large amounts of data.

There are two types of data- structured and unstructured data.Data science is the continuation of several processes like data mining, statistics, predictive analysis. Data science is a vast field which uses the theories which includes mathematics, statics and computer science.

There are various methods which are been used in data science like signal processing, data mining, machine learning etc with the immense advancement in the field of data science it has gained more importance, especially in big data.

Data science is not restricted to big data as itself is a big field because the big data solutions are more focused on organizing and pre-processing the data.

Origin of data science:

Over the years data science has become an important part of many industries like agriculture, marketing optimization, fraud detection, marketing analysis and public policy.

Data science tries to resolve many issues which are related to an individual sector and economy at large by using data preparation, statistics, predictive modelling and machine learning.

Data science has far reached implications in many fields in academic and research domains like machine translation, speech recognition, digital economy etc it has expanded its benefits even to healthcare, social science, and medical informatics.

Big Data :

Big data is sets of data that are big and complex that are traditional data processing application software are inadequate to deal with them. The processing of big data starts with raw data that is not aggregated and impossible to store in memory of a single computer. Big data refers to a large volume of data that are structured and unstructured that inundates a business on a day to day basis.

Big data has three Vs that define the properties or dimensions of big data:

  1. Volume: refers to the amount of data that are collected from various sources including business transactions, social media and information from sensor or machine to machine data.
  2. Variety: refers to the number of heterogeneous data that are both structured and unstructured, this variety of unstructured data posses issues for storing, mining and data analysis.
  3. Velocity: refers to the speed of data processing, how fast the data is been processed to meet all demands and determines the real potential in the data. The flow of certain data like social media sites, sensors etc are massive and continuous.

Benefits of big data :

Access to social data from search engines like Facebook, Twitter etc has enabled the organization to utilize the intelligence to make decisions.

The feedback systems which are enabled are been replaced with new systems that are used t read and evaluate customer responses.

Big data have also helped in the identification of early risk to the product or services.

Data Analytics:

It’s the science of examining the raw data with the conclusions about the information. It also involves an application of the algorithmic and mechanical process to derive at the conclusions.

Data Analytics are used in many industries to allow the organizations and companies to make decisions.

This ultimately leads to smart business moves and higher profits and efficient operations. In every business the data that is generated are generally increasing the rate and the growth of information is also high. It is important that these data which is collected are to be amalgamated through the business. If it gets wasted large volume of information will also be lost.

In order to process these data skilled analysts are needed but with the use of these tools for high-speed data and it helps in incorporating the data analytics at the time of decision making.

Data Science VS Data Analytics :

A data scientist is someone who can predict the future based on past patterns but a data analyst is someone who merely curates the meaningful insights from data.

Data scientist job includes estimation of unknown data but whereas the data analyst deals with the known data.

Data scientist expects to generate new questions but data analyst finds answers to the given questions from the data.

Data analyst addresses the business problem but the data scientist picks up problems which have the most business value once if it’s solved.

The role of the data analyst is to solve the problems in the business data but the data scientist builds the statistical models for the business data.