With origins in statistics, computer science, and mathematics, the history of data science is an interesting journey spanning many decades. An outline of the major advancements that have influenced the field is provided here:
Early Foundations (Before 1960s)
Statistics and Mathematics: The fields of statistics and mathematics, which have been utilized for millennia to analyze and understand data, are the foundations of data science.
Mechanical Calculators: During the late 1800s and early 1900s, mathematical and statistical calculations were performed using mechanical calculators.
Source: History of Data
The Rise of Computers (1960s-1970s)
The advent of computers: More sophisticated data processing and analysis were made possible by mid-1900s computer development.
Data processing: Businesses began using computers in the 1960s and 1970s to process data, which set the stage for data management and analytics.
Database Development (1970s–1980s)
Relational Databases: The development of relational databases, such as IBM’s System R and Oracle, in the 1970s changed the retrieval and storing of data.
SQL: Large dataset management and querying became simpler with the introduction of SQL (Structured Query Language).
Business intelligence’s emergence (1980s–1990s)
Business Intelligence Tools: Business intelligence and data warehousing tools were developed to assist firms in analyzing historical data and producing reports.
Data Warehousing: Made popular in the 1980s, this idea allowed data to be integrated for analysis from many sources.
The Age of Big Data (2000s)
Big Data Technologies: As a result of the exponential growth in data creation brought about by the Internet, mobile devices, and the Internet of Things, big data technologies such as Hadoop and NoSQL databases have been developed.
Data mining: Methods for identifying patterns in huge datasets have advanced in sophistication.
Source: History of Data Science
Data Science as a Discipline (2000s-2010s)
Formalization of Data Science: As “data science” grew in prominence, specialized degree programs and professional positions were created to codify the topic.
Machine Learning: The core of data science has been the development of machine learning, which has fueled increases in processing power and data accessibility.
Modern Era (2010s-Present)
AI and Deep Learning: As these fields have grown, data science’s capabilities have also expanded, opening the door to more sophisticated models and applications.
Cloud computing: A wider range of people may now access sophisticated computer resources and tools thanks to cloud platforms, which have democratized access to these resources.
Source: Brief History
Ethics and Privacy: As the field of data science has expanded, so too have worries about ethical issues and data privacy, leading to the creation of new rules and best practices.
The field of data science is seeing significant evolution due to the growing use of data in decision-making across many industries and technological improvements.