Data science is hailed as the new currency of business, giving companies a competitive edge by enabling them to rapidly transform data into information. Data science is the application of a variety of methods such as statistical methods, statistical techniques, and software systems that allow companies to analyze and extract data from both structured and unstructured data sources.

Essentially, any information that can be recorded in a digital format can be considered data. This includes handwritten ledgers, books that are found on shelves as well as the ones and zeroes being stored on your computer screen and converted into pixels various shades and densities.

The aim of data science is to discover that knowledge, turn it into insights and finally make use of those insights to drive decision-making and innovating. This article will help you transform raw data into valuable data and how to develop an data-science project.

This is a challenging process and requires expertise in a variety of areas such as business intelligence, analytics, programming (ideally using Python) databases management systems, SQL querying, visualization and machine learning. It requires a thorough understanding of the domain you are working in, and an ability to communicate your knowledge to team members and the stakeholders.

Examples of successful data science projects include building recommendations for movies using patient records to find patterns and predict illnesses using social media analysis to understand customer opinions and predicting stock prices, or identifying crime patterns for police. The ultimate goal of a data science project is to use those insights to drive scalability and better business decisions for the organization.