Data Science combines different fields of work in statistics and computation in order to interpret data for the purpose of decision making.
The incorporation of technology in our everyday lives has been made possible by the availability of data in enormous amounts. Data is drawn from different sectors and platforms including cell phones, social media, e-commerce sites, healthcare surveys, internet searches, etc. The increase in the amount of data available opened the door to a new field of study called Big Data which refers to the huge amount of information available that can be tapped into to produce even better tools of operations in all sectors including transportation, finance, manufacturing, and regulation.
Data science incorporates tools from multi 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, and some programming.
Data mining applies algorithms in the complex data set to reveal patterns which are then used to extract useable and relevant data from the set. Statistical measures like predictive analytics utilize this extracted data to gauge events that are likely to happen in the future based on what the data shows happened in the past.
Machine learning is an artificial intelligence tool that processes mass quantities of data that a human would be unable to process in a lifetime. Machine learning perfects the decision model presented under predictive analytics by matching the likelihood of an event happening to what actually happened at the predicted time.
Under analytics, the data analyst collects and processes the structured data from the machine learning stage using algorithms. S/he interprets, converts, and summarizes the data to a cohesive language that the decision-making team can understand.