The history of data science and machine learning can be traced back to the 1950s when the field of artificial intelligence (AI) was first established. Early research in AI focused on creating algorithms and models that could mimic human intelligence and decision-making.

In the 1960s and 1970s, researchers began developing more sophisticated algorithms for machine learning, such as decision trees and Bayesian networks. These early machine-learning techniques were used primarily for classification and prediction tasks.

In the 1980s and 1990s, the field of data science began to evolve as a result of advances in computer technology and the increasing availability of large datasets. This led to the development of new machine learning algorithms and methods, such as neural networks and support vector machines (SVMs). These methods were used to solve a wider range of problems, including image and speech recognition.

The field of data science and machine learning continued to evolve in the 2000s and 2010s, with the advent of big data and the increasing availability of powerful computing resources. This has led to the development of new techniques such as deep learning and reinforcement learning, which have been used to achieve breakthroughs in areas such as natural language processing and computer vision.

Today, data science and machine learning are widely used in a variety of industries and applications, including finance, healthcare, transportation, and manufacturing. They are also being used to solve complex problems such as climate change and disease diagnosis. Data science and machine learning are continuously evolving and researchers are working on new ways to make them more accurate, efficient and reliable.

I think it was started in 1958 from Kaplan-Meier's famous paper