Wall Street made a discovery in the 1980s. Physicists then excelled at solving complex financial problems. They paved the path of unprecedented profitability for their firms. This led to the rise of “quants.” It was the hottest profession of the era. Now let us have a look to the late 2000s. The era was on the brink of a big data revolution. Businesses started seeking new breed of professionals. The professionals had to unearth valuable insights from a vast amount of data. Hence, it was the dawn of data science.
Data science firmly established itself as a crucial field by 2018. It drove innovation as well as growth across industries. At one of Australia’s largest banks, a unique cohort of seven STEM doctorate candidates from top universities across the country brought diverse specialisations in diabetes research, machine learning, neuroscience and rocket engineering. It was initially scattered across various corners of the company. Gradually they converged in the bank’s big data division. It was a twist in their careers and they still joke about today.
The journey from Python to Generative AI (GenAI) shows how the skills of data scientists have evolved. Learning Python then was essential for working with data. Many libraries made it the best tool for handling, analyzing and visualizing data. In fact, it won’t be wrong to say that Python was the starting point for data scientists. It helped them in building models as well as gaining insights in guiding business decisions.
However, the field of data science is ever-evolving. The volume, variety and velocity of data are increasing at a rapid pace. Hence, the tools and techniques need to be managed and perfectly interpreted. GenAI is a revolutionary step forward. GPT, BERT and other such Generative AI models have transformed how data is being handled and understood. The models can generate human-like text, understand context and even predict future trends. All these are possible based on historical data.
