What is data science?

What is data science?

Hey!!! Interested in learning data science? At today’s date as the world is increasingly accepting technology day by day and applying it into its daily tasks,letting it handle their data and records, a need for a subject which researches on the ways to manage enormous amounts of data has arised. This is where data science comes into play, data science has evolved into a separate field generating great career opportunities.   

Data science has multiple fields, including statistics, artificial intelligence and data analytics, for the extraction of  value from data. People practicing data science are called data scientists, and they apply many skills for the analysis of data collected from the various devices and the internet.

Data science comprises preparation of data to analyze, which includes  aggregation and manipulation of the data to perform advanced data analytics.analysis applications and data scientists can then be able to review the outcomes for uncovering patterns and to enable the business leaders to create precise insights.


Data science is a great field out there today. companies already have a treasury of data. As technology enables creating and storing amounts of data. A study says that 90% of the data globally was created in the past 2 years. For example, Facebook users are uploading 10 million of media in a single hour.But, this collected information  is more often sitting in the  databases basically worthless.

The precious data collected and stored by such technologies bring evolving benefits to organizations globally—but only if we knew it. That is where data science comes to the play.

Data science discovers the trends and creates insights which can be used to make good decisions and create innovative products and services. And most importantly, it enables modes like machine learning (ML) to wrangle and study huge amounts of data fetched, rather than only relying upon analysts to discover information and stats from the data.

Data is the hometurf of innovation, but its value comes from the information data scientists can utilize upon.


organizations use data science to turn data into a set of refined products and services. Use of Data science and machine learning includes:

  1. Determine customer blend of data  by analysis of data collected from BPO’s, so the sales and marketing teams utilize the info collected.
  2. Improving the efficiency by analyzing data traffic pattern and other factors so the organizations can improve their delivery speed and reduce productions costs
  3. Improving medical diagnosis by analytical test data and generating data reports so doctors can detect diseases as early as possible and start the treatment on the patient.
  4. Optimization of the supply chain by stating the errors occurring in the process                         
  5. Detecting  fraud in monetary transactions by recognizing malicious actions.
  6. Profitability in sales by recommending customers suggestions based upon their past purchases.

Organizations  have prioritized data science and are investing in it greatly. A recent survey ranked analytics and business intelligence as the top differentiating technology for their organizations. The survey sees these technologies as a strategy for their companies, and are interested in investing in it.


Building, evaluation and  deployment of machine learning modes can be a complicated process. Therefore, we are seeing an increase in the number of data science tools. Data scientists can use different types of tools, but a common one is open source notebooks, which consists of web applications programming code, visualization of the data, and looking at the results all in the same interface.

While deciding which tool is the right option, it’s important to ask the following things: which programming languages do your data scientists use? What procedures or processes do they prefer?  For example, some organizations have database -agnostic services that are using open-source libraries.Other people prefer machine learning algorithms.