Data science is hailed as the new technology that will give firms an edge in competition by allowing them to quickly transform data into insights. It involves the application to a variety of methods, techniques, statistical techniques and software systems to aid companies in analyzing and extracting information from both structured and unstructured data sources.
In essence, any data that can be stored in digital format could be considered to be data. This includes handwritten ledgers, books sitting on store shelves but also the zeroes and ones being stored on your computer’s screen, and converted into pixels different shades and densities.
The aim of data science is to extract the knowledge, transform it into insights and ultimately utilize those insights for the development of new ideas and decisions. This article explains the process of turning raw data into valuable data and how to construct an effective data science project.
It is a complex procedure and requires expertise in a variety of areas such as business intelligence, analytics programming (ideally with Python) and database management systems, SQL querying, visualization and machine learning. It also requires a deep understanding of the area in which you are working, and a strong ability to communicate these insights to other team members and business stakeholders.
Examples of successful data science projects include the development of the movie recommendation system and analyzing the patient’s records to discover patterns and predict disease and analyzing social media to understand customer opinions and predicting stock prices, or identifying crime patterns for law enforcement. The goal of any data science project is to make use of those insights to make better business decisions and increase an increase in scalability within the organization.
