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Data science is being hailed as a new currency for business. It gives businesses a competitive advantage because it allows them to quickly transform data into information. Data science is the use of a range of techniques including statistical methods, statistical techniques, as well as 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 ledgers and books on shelves at stores as well as the pixels on your computer’s screen that are encoded in different colors and densities.
The purpose of data science is to extract the knowledge, transform it into insights and finally make use of the insights to guide innovation and decision-making. This article explores how to transform raw data into valuable information and how to design an data-science project.
This is a complicated process that requires skills in a variety of areas, including business intelligence and analytics programming (ideally using Python) as well as database management systems, SQL querying, visualization and machine learning. It also requires a comprehensive knowledge of the field within which you are working and the ability to communicate your findings to your team members and business stakeholders.
Examples of successful projects in data science include constructing an online movie recommendation system, analysing patient records to detect patterns and predict the severity of diseases studying social media to understand customer opinions and predicting stock prices, or identifying patterns of crime for police. The goal of every data science project is to utilize those insights to make better business decisions, and also to drive growth in the company.