"Data science uses techniques such as machine learning and artificial intelligence to predict future patterns and behaviors"

Data science incorporates tools from multiple disciplines to gather a data set, process, and derive insights from the data set, extract meaningful data from the set, and interpret it for decision-making purposes. The disciplinary areas that make up the data science field include mining, statistics, machine learning, analytics, programming, Big data analysis, data driven theory, operations research, random processes, social network analysis, financial technology, quantum computing, intelligent computing, cloud computing, optimization theory, decision- making theory, computer simulation technology, health informatics, medical big data, and health management. Data science combines aspects of different fields with the aid of computation to interpret data for decision-making purposes. Data mining applies algorithms to the complex data set to reveal patterns that are then used to extract useful and relevant data from the set. Machine learning is an artificial intelligence tool that processes mass quantities of data that a human would be unable to process in a lifetime. Data Analytics, the data analyst collects and processes the structured data from the machine learning stage using algorithms. The analyst interprets, converts, and summarizes the data into a cohesive language that the decision-making team can understand. Data science is applied to practically all contexts and, as the data scientist's role evolves, the field will expand to encompass data architecture, data engineering.