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The data science revolution

What is the data science revolution?
(click) (gentle music) Data science is an interdisciplinary field at the forefront of tackling data-related challenges of the future. The field is evolving in response to the technological, cultural, and organisational changes that surround us. Data science is increasingly relevant across diverse industries, from finance and retail to civil services and environmental protection. Data science creates value and informs organisational decisions by making sense of large and unstructured datasets, using big data to identify opportunities for product and process optimization and building models to test the effectiveness of different courses of action.
Data science adapts techniques and theories from diverse fields involving a wide range of skills that likely include but are not always limited to analytics, statistics, big data, business, mathematics, computer science, and many more.
Just as a traditional scientist might use tools such as test tubes, flasks, and a Bunsen burner, a data scientist also relies on a basic set of tools. Data science tools include databases, software, and programming languages. These basic tools have been in use since the early days of cleaning datasets and applying statistical methods. As the field has advanced, so have the tools. Data science has now evolved to encompass data analysis, predictive analytics, data, mining, business intelligence, machine learning, deep learning, and big data. The individual components that make up the field of data science, statistics, software development, evidence-based problem solving, and so on descend from diverse and well-established fields.
Before data science existed, mathematical and statistical concepts and scientific methodologies had been evolving for centuries. Early computational analysis began to really gain traction in the 1970s and 1980s when the processing power and potential of computers were beginning to be recognised for scientific and research purposes. In 1985, CF Jeff Wu used the term data science for the first time as an alternative name for statistics in a lecture given to the Chinese Academy of Sciences. It didn’t take long for technology to get faster, smaller, and more affordable. As the cost of hard drives fell, many organisations began to store large amounts of data. When the internet was embraced by the mainstream, the era of big data truly took hold.
This led to a significant rise in unstructured data. The desire to harness and analyse this data called for new technologies and expertise. The field of data science emerged gradually through all of these changes. Data science is an applied branch of statistics. The exact relationship between data science and statistics is hotly debated. Some experts believe the two terms can be used interchangeably, while others describe statistics as nonessential to data science because of the emphasis on prediction and problem solving that is unique to digital data. As a specialist, we must try to take a stance between these two extreme views. We will consider the concepts from traditional statistics, emerging technologies, and the growing data-intensive environment holistically.

As you learned from the video, data science encompasses mining unstructured data, modelling and analysing structured data, and transforming it into usable information to help organisations impact change. It enables organisations to reveal relationships and dependencies within data or predict future outcomes and behaviours of a system.

Data science was first recognised as an independent discipline in a 2001 paper by William S. Cleveland and, by 2012, Harvard Business Review had crowned data science ‘the sexiest job of the 21st century’. [1] In the last decade, data science has quickly grown to become one of the fastest-growing professional fields.

The factors contributing to the rapid growth of this field and increased demand for data scientists include the:

  • emergence and ongoing evolution of big data, AI, cloud computing, and internet of things (IoT)
  • increasing quantity of data, especially unstructured data
  • introduction of General Data Protection Regulation (GDPR) on data mining
  • explosion of sources
  • increased complexity.

The field of data science will continue to evolve and expand. New possibilities are on the horizon with the integration of even more sophisticated artificial intelligence and machine-learning processes. This background and context make the data science career pathway more aspirational.

Next, let’s learn about what it takes to be a data scientist.


  1. Davenport TH, Patil DJ. Data Scientist: The Sexiest Job of the 21st Century [Article]. Harvard Business Review; 2012 Oct. Available from:
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Introduction to Data Science for Business

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