Data science is hailed as the new currency of business, giving businesses an edge in the market by enabling them to swiftly transform data into insights. It involves the use to a variety of methods, techniques, statistical techniques and software systems that aid companies in analyzing and extracting knowledge from structured and unstructured data sources.
In essence, any information that can be stored in digital format can be considered data. This includes handwritten ledgers as well as books that are found on shelves but also the ones and zeroes being stored on your computer’s screen and encoded into pixels in various shades and densities.
The goal of data science is to discover the knowledge, transform it into insights and finally make use of those insights to drive the development of new ideas and decisions. This article explains how to transform raw data into valuable information and how to design an data-science project.
This is a challenging 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 requires a thorough understanding of the industry that you work in, and an ability to communicate these insights to other team members and other stakeholders.
Successful data science projects can include the creation of a movie recommendations system, studying patient files to find patterns and predict illness and analyzing social networks to learn about customer sentiments as well as predicting the price of stocks or identifying crime patterns to assist law enforcement. The goal of every data science project is to apply these insights to make better business decisions, and also to drive growth in the company.