The History and Evolution of Data Science: From the 1960s to the AI Era
Data science is an interdisciplinary field that combines techniques from statistics, mathematics, computer science, and domain knowledge to extract meaningful insights and knowledge from data. It involves the entire process of working with data, including:
1. Data Collection & Management – Gathering raw data from multiple sources (databases, sensors, web, experiments, etc.) and preparing it for analysis.
2. Data Cleaning & Processing – Handling missing values, errors, inconsistencies, and transforming the data into usable formats.
3. Exploratory Data Analysis (EDA) – Summarizing, visualizing, and understanding patterns, trends, and anomalies in the data.
4. Statistical Analysis & Machine Learning – Applying mathematical models and algorithms to make predictions, classify outcomes, find relationships, or detect hidden structures.
5. Interpretation & Communication – Translating results into actionable insights through reports, dashboards, or visualizations that support decision-making.
At its core, data science is about turning raw data into knowledge and actionable decisions, often using advanced computational tools and programming languages (like Python or R).
Let’s look at when data science started in terms of history and evolution:
Early Foundations (1960s–1980s)
The term “data science” first appeared in the 1960s, when statisticians began talking about using computers for data analysis.
In 1962, John Tukey (a famous statistician) published The Future of Data Analysis, hinting that statistics was evolving into something new.
During the 1970s and 1980s, the rise of databases and information systems laid the groundwork for modern data handling.
The Rise of Big Data & Computing (1990s)
In the 1990s, with the growth of the internet and digital storage, organizations began collecting massive amounts of data.
In 1997, C.F. Jeff Wu (a statistician) proposed renaming statistics to “data science.”
Around the same time, computer science advances (databases, machine learning, algorithms) merged with applied statistics.
Data Science as a Discipline (2000s)
The term “data science” became more common in the early 2000s.
In 2001, William S. Cleveland proposed expanding statistics into a new field called data science, blending statistical theory with computing.
Universities started offering data science courses and programs.
Modern Era (2010s–Now)
With the explosion of big data, AI, and cloud computing, data science became one of the most in-demand fields.
By the 2010s, companies like Google, Facebook, and Amazon were using data science at massive scales.
In 2012, Harvard Business Review called “data scientist” the sexiest job of the 21st century.
✅ In summary:
Term first appeared: 1960s
Adopted in academia: 1990s–2000s
Became mainstream: 2010s
Reference: OpenAI, 2025. ChatGPT version 5, accessed on September 27 th , 2025, generated responses that contributed to the content of this blog.