Data science and big data are the two terms which are playing a very prominent role in the world. Well, many of them are still not aware of this topic Data Science, nowadays it is a very boom in the market where everyone wants to upgrade their skills to stay pertinent in their respective career field and industry needs to know about it.

If you know or not a lot is happening across the entire world
To be honest data science kingdom has been dominated as overly technical and terrifying by some selected data science sorcerer. 

Basic data science isn’t that tough to understand. It is simply known to be a lot of techniques and methodologies to derive valuable insights from raw data. 
In our daily lifestyle that is from the physical world to the digital world, whatever we action do it generates data. The information shower from our mobile devices or other online interaction.

This flood of information is the main role of making data science, this data science is also an art of wrangling data to predict our future behavior patterns where we can get provided actionable information from these vast and untapped data resources.

Data Science:

Before getting into this point first we should know one thing that data science and data engineering are separate and distinct domains of expertise. Data science is an arithmetic science of getting meaningful insights from raw data and then those data are effectively communicated in a way to get those insights to generate value. On the other hand, it is a domain of engineering where it looks after the maintenance and building of systems that overcome the processing of data with bottlenecks and handling problems that consume a lot of space to store data and varieties of data.

Types of data science:

• In this type of data, the data is stored in a relational database management system is referred to as structured 
• Data which is generally generated from activities of human and does not fit into a structured database format, it is referred to as unstructured 
• Data which neither fits into a structured database system nor structured by tags where it useful in creating order and hierarchy in the data is referred as Semi-structured

There is a kind of belief amongst people that large organizations are the only ones that have funds and can implement data science methodologies to improve their business, but it is not true. The rapid growth of data has increased in demand for insights, and this demand is implanted in many aspects of our modern culture.

Following is the list of subject matter experts who use data science in their respective industries:

• Engineers: They use data science/ machine learning to optimize energy efficiency in modern building design
• Clinical data scientists: They work on the health care informatics to predict future health problems in at-risk patients
• Data Journalists: Journalists use this data science to get the latest breaking news stories 
• Data Scientist in crime analysis: Using this data science they predict, prevent the criminal activities

Conclusion: Undoubtedly, the future belongs to data scientists and it is predicted that there will be a need for data scientists to implement more and more data to make strong business decisions. Well, this Data science is going to change the way we look at the world with data science all around us. Hence it is assumed that a scientist should be highly skilled and motivated to solve the problems.