Reviewed by Sep 28, 2020| Updated on
What is data science?
Data science offers meaningful data based on large volumes of complex data or big data. Data science, or data-driven science, blends various mathematical and analytical fields of study to analyse information for decision-making purposes.
Understanding data science
Data is collected from various sectors, networks, and websites such as cell phones, social media, e-commerce sites, healthcare surveys, and Internet searches. The rise in the amount of available data has opened the door to a new field of study focused on big data— the massive data sets that help to create better organisational resources in all sectors.
Due to advances in technology and collection techniques, the continuously increasing access to data is possible. Individuals' purchasing habits and actions can be tracked, and forecasts can be made based on the information collected. The ever increasing data, however, is unstructured and needs filtering for successful decision-making. This method is dynamic and time-consuming for enterprises; thus, data science emerged.
How is data science applied
Data science combines multidisciplinary methods for collecting, analysing and deriving information from the data set, extracting meaningful data from the collection, and interpreting it for decision-making purposes. Mining, statistics, machine learning, analytics, and programming are the disciplinary areas which make up the field of data science.
In a complex data collection, data mining applies algorithms to discover patterns and, then, used to extract useful and relevant data from the collection. Such derived data are used by statistical measures or predictive analytics to gauge things that are likely to happen in the future based on what the data indicates had happened previously.
Machine learning is an artificial intelligence technique that manages mass data amounts that would be difficult for a person to process in a lifetime. Machine learning perfects the model of judgment offered under predictive analytics by comparing the likelihood of an event occurring with what actually happened at an expected time.
The data analyst uses algorithms to collect and process structured data from the machine learning level. The analyst interprets, translates, and integrates the data into a coherent language which can be interpreted by the decision-making team. Data science is applicable to nearly all contexts and, as the role of data scientist progresses, the discipline will broaden to include data architecture, data engineering, and data management.