Data science is being touted as a new currency for business. It gives businesses an edge over competitors by allowing them to swiftly transform data into valuable insights. Data science is the use of a range of techniques, statistical methods, and software systems that aid companies analyze and extract information from non-structured and structured data sources.

In essence, any data that can be stored in a digital format can be considered data. This includes handwritten ledgers and books that are found on shelves and also the ones and zeroes stored on your computer's screen, and converted into pixels various shades and densities.

Data science's aim is to discover knowledge, transform it into insights and then use these insights to drive the development of new ideas and decisions. This article explores how to transform raw data into valuable data and how to develop an data-science project.

This is a complex task that requires expertise in a wide range of areas such as business analytics, intelligence and programming (ideally using Python) as well as database management, SQL querying and visualization. It requires a deep understanding of the area you're working in, as well as an ability to communicate your knowledge to team members and the stakeholders.

Examples of successful data science projects include building recommendations for movies, analysing patient records to identify patterns and predict diseases, analyzing social media to learn about customer sentiment and predicting stock prices, or identifying crime patterns for police. The ultimate goal of a data science project is to use these insights to drive scalability and better decision-making within the business.