Corporate 'data processing' departments once employed programmers, often working in COBOL, whose main job was writing and running scheduled overnight batch jobs that produced management's printed reports by the next morning. Self-service business-intelligence dashboards and ad hoc query tools let managers pull their own numbers on demand, closing this specialty out almost entirely by the 2000s.
Mainframe batch-report programmer no longer exists as a living trade. Here is what erased it, and which profession took on the work.
1900–1935
Statisticians formalize inference
Karl Pearson founded the journal Biometrika in 1901 to give the young discipline of mathematical statistics a home. William Sealy Gosset, working as a statistician for the Guinness brewery, published the t-test in 1908 under the pseudonym 'Student' because Guinness barred employees from publishing under their own names. Ronald Fisher's 1925 book 'Statistical Methods for Research Workers,' written while analyzing decades of crop trials at Rothamsted Experimental Station, introduced analysis of variance and rigorous experimental design. Jerzy Neyman and Egon Pearson completed the toolkit in 1933 with a formal framework for hypothesis testing — the statistical machinery data science would later inherit almost intact.
What else was happening then
1925
Fisher publishes 'Statistical Methods for Research Workers'
Ronald Fisher, analyzing decades of crop-yield data at Rothamsted Experimental Station, publishes the book that establishes analysis of variance and modern experimental design as standard statistical practice.
1933
Neyman and Pearson formalize hypothesis testing
Jerzy Neyman and Egon Pearson's paper 'On the Problem of the Most Efficient Tests of Statistical Hypotheses' gives statistics its modern framework for deciding, with quantified error rates, whether a result is significant.
1962
Tukey argues data analysis is its own science
John Tukey's paper 'The Future of Data Analysis' argues that analyzing real data deserves recognition as a distinct science with its own methods, not a minor branch of mathematical statistics.
1977
Tukey's 'Exploratory Data Analysis' popularizes the box plot
Tukey's book gives practitioners hands-on tools — the box plot among them — for understanding a dataset's shape by eye before fitting any model to it.
Where that work lives now
Finds patterns and builds predictive models from data — a 2008 job title built on three centuries of counting, testing and visualizing evidence.
📊 Data Scientist →Corporate 'data processing' departments once employed programmers, often working in COBOL, whose main job was writing and running scheduled overnight batch jobs that produced management's printed reports by the next morning. Self-service business-intelligence dashboards and ad hoc query tools let managers pull their own numbers on demand, closing this specialty out almost entirely by the 2000s.
1900–1935
Statisticians formalize inference
Karl Pearson founded the journal Biometrika in 1901 to give the young discipline of mathematical statistics a home. William Sealy Gosset, working as a statistician for the Guinness brewery, published the t-test in 1908 under the pseudonym 'Student' because Guinness barred employees from publishing under their own names. Ronald Fisher's 1925 book 'Statistical Methods for Research Workers,' written while analyzing decades of crop trials at Rothamsted Experimental Station, introduced analysis of variance and rigorous experimental design. Jerzy Neyman and Egon Pearson completed the toolkit in 1933 with a formal framework for hypothesis testing — the statistical machinery clinical data science would later inherit almost intact.
What else was happening then
1925
Fisher publishes 'Statistical Methods for Research Workers'
Ronald Fisher, analyzing decades of crop-yield data at Rothamsted Experimental Station, publishes the book that establishes analysis of variance and modern experimental design as standard statistical practice.
1933
Neyman and Pearson formalize hypothesis testing
Jerzy Neyman and Egon Pearson's paper 'On the Problem of the Most Efficient Tests of Statistical Hypotheses' gives statistics its modern framework for deciding, with quantified error rates, whether a result is significant.
1962
Tukey argues data analysis is its own science
John Tukey's paper 'The Future of Data Analysis' argues that analyzing real data deserves recognition as a distinct science with its own methods, not a minor branch of mathematical statistics.
1977
Tukey's 'Exploratory Data Analysis' popularizes the box plot
Tukey's book gives practitioners hands-on tools — the box plot among them — for understanding a dataset's shape by eye before fitting any model to it.
Where that work lives now
Uses clinical, trial and health-system data to generate reliable evidence for safer care, research and operational decisions.
🧫 Clinical Data Scientist →