Data Science is an inter-disciplinary field which uses scientific methods, processes, algorithms and systems to extract knowledge and insights from structured and unstructured data.
It is a blend of various tools, algorithms, artificial intelligence and machine learning principles with the goal to discover hidden patterns from the raw data.

How data science is different from what statisticians have been doing for years?

The role of Data Analyst is to explain what is going on by analysing the history of the data. On the other hand Data Scientist not only does the exploratory analysis to discover insights from it, but also uses various advanced machine learning algorithms to identify the occurrence of a particular event  in the future. 

The main purpose of data science is to make decisions and predictions using predictive casual analytics, prescriptive analytics and machine learning.

• Predictive casual analytics-  The predictive casual analytics is used if you want a model which can predict the possibilities of a particular event in the future. Say, if you are providing money on credit, then the probability of customers making future credit payments on time is a matter of concern for you. Here, you can build a model which can perform predictive analysis on the payment history of the customer to predict if the future payments will be on time or not.

• Prescriptive analytics-  Prescriptive analytics is used if you want a model which has the intelligence of taking its own decisions and the ability to modify it dynamic parameters. The best example for this is Google's self driving car. The data gathered by vehicles can be used to train self driving cars. You can run algorithms on this data to bring intelligence to it. This will enable your car to take decisions like when to turn, which path to take etc.

• Machine Learning for making predictions- Machine learning algorithms are the best if you need to build a model to determine the future trend.

 Example:  A fraud detection model can be trained using a historical record of fraudulent purchases.

• Machine learning for pattern discovery- Pattern discovery is used when you don't have any parameters using which you can make predictions. You need to find out the hidden patterns within the dataset using which you can make a meaningful predictions.  

Example: Let's say you are working in a telephone company and you need to establish a network by putting towers in a region. Then, you can use the clustering technique to find those tower locations which will ensure that all the users receive optimum signal strength.