Data Science is a field that uses concepts of mathematics, statistics and computer science to develop scientific methods, algorithms and processes that extract useful insights from large volumes of data. These insights are then used for innovation and growth of businesses/institutions. To understand why businesses are investing heavily in data science these days, let's look at an example. For illustration, let's look at how data science can be used in a popular supermarket. To understand where to invest or cut costs, the first thing that would need to be done is data collection. Data collected would include customer invoices, foot traffic in the store at different times, products in demand, sections of the store most popular with customers, information regarding coupon usage etc. Using this data, machine learning algorithms can be used to derive key insights. The supermarket can then decide to invest more in strategic advertising, placement of popular products to direct more foot traffic in sections with lower foot traffic, hold promotions/special buys during holidays to up the sales etc.

Data science mainly requires domain expertise, programming skills and statistical analysis. Data scientists develop machine learning algorithms, which create mathematical models, to analyze big data and discover hidden patterns and insights. They are also involved in developing systems/applications that can use artificial intelligence to perform complex tasks. Different techniques used in data science are linear regression, decision trees, clustering, support vector machines just to name a few. Two main programming languages used are R and Python.