Era of Data Science

Data Science didn't appear overnight - it evolved gradually as the amount of available data, computing power, and analytical techniques all grew together over several decades.

Early Statistics and Databases (Before 1990s)

Organisations mostly relied on structured statistics and relational databases to record and report on transactions. Analysis was largely descriptive - summarising what had already happened.

Rise of Data Mining and Warehousing (1990s - 2000s)

As businesses accumulated more data, data warehousing and data mining techniques emerged to spot patterns and trends across large historical datasets.

The Big Data Era (2000s - 2010s)

The explosion of internet, mobile, and sensor data led to the "Big Data" era, characterised by the three Vs - Volume, Velocity, and Variety. New tools like Hadoop and Spark were built to process data at this scale.

The Machine Learning and AI Era (2010s - Present)

With more data and computing power available, machine learning and deep learning became practical at scale, powering recommendation systems, image recognition, natural language processing, and today's large language models.

The term "Data Scientist" itself only became widely used around 2008-2012, even though the underlying statistical techniques are much older - it's the combination with modern computing power that created the field as we know it today.

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