Data Science is a multidisciplinary field of study that includes activities like data management, data collection, data analysis, and data visualization. It incorporates skills from a broad range of disciplines that include computer science, statistics, mathematics, and visual design.
Since the beginning of the 21st century, data science has been used in almost every field of industry to extract insights from data that may be leveraged in business decision-making and product development. These applications are largely related to fields of study rooted in data science, including:
- Data Management
- Data Security
- Data Validation
- Data Cleaning
- Data Modelling
- Data Integration
- Data Use
- Data Visualization
- Data Analytics
- Data Mining
- Business Intelligence and Strategy
- Artificial Intelligence
- Cloud and Distributed Computing
Languages and Tools
- R (ggplot2)
- Jupyter Notebooks
Many statisticians have argued that data science is not a new field, but rather another name for statistics. Considering this perspective, the history of data science would date as far back as the 5th century B.C., demonstrated by the Athenians who estimated the height of ladders needed to scale the walls of Platea by counting the bricks of the wall vertically in several areas, then multiplying the most frequent count by the height of a brick.
In 1662, John Graunt produced Natural and Political Observations Made Upon the Bills of Mortality in which he estimated the population of London by using annual funeral records, familial death rates, and average family size.
Without the correlation to statistics involved, many consider John Tukey to be the inventor of data science where in March 1962 he published The Future of Data Analysis where he described a field he called “data analysis,” which resembles modern data science. With advents in data processing and storage, applications of data science have accelerated in both complexity and popularity.
Some of the fundamental concepts and tools of data science are explored below: