Have you ever wondered when the term ‘data-science’ became so important to the industry worldwide? or what are some technological advancements that triggered the big-bang of data science? We are about to find out.

Data Science is a very fancy term these days and everyone seems to have their own strong opinion of the subject matter. There are even designations such as ‘Data Scientist’, ‘Data Analyst’, ‘Machine Learning Engineer’ etc. in this field that are very well-paid jobs. if you are wondering what data science is, here is a simple explanation-

Data science is a field that combines statistical and computational methods to extract insights and knowledge from data.

Key Components of Data Science: Data Collection, Data Cleaning and Preparation, Exploratory Data Analysis, Machine Learning, Data Visualization. If you want to know more about these terms, please leave a comment. It is not in purview of this article but will surely cover it in a separate post.

Coming back to the main topic-

In the 1960s, the term “data science” was coined by statistician John Tukey, who used it to describe the process of extracting insights from data. However, it wasn’t until the 1990s that data science began to emerge as a distinct field, thanks in part to the development of machine learning algorithms and the increasing availability of digital data.

But if you ask ChatGPT, as most people are doing these days, it may throw you different answer on different days. Believe me, I got the below response that ‘data science’ term was coined in 2008, few days back and later got a different response.

So why should we trust ChatGPT, right? so I dig deeper.

For Data science to thrive, execution of complex machine learning algorithms is of utmost importance and to do that we need higher computation power. So, I traced back to the time when it becomes computationally possible to execute large machine learning problems.

“In 2009, Nvidia was involved in so called the inflection point of data science, the ‘Big Bang’. The GPU Technology Conference (GTC) was started in 2009 by Nvidia to foster a new approach to high performance computing using massively parallel processing GPUs. GTC has become the epicenter of GPU deep learning — the new computing model that sparked the big bang of modern AI.” ^(#1)

Researchers and enthusiasts flogged in numbers to learn and test the GPUs for complex data science problems. In 2012, a team led by George E. Dahl won the “Merck Molecular Activity Challenge” using multi-task deep neural networks to predict the biomolecular target of one drug. In 2014, Hochreiter’s group used deep learning to detect off-target and toxic effects of environmental chemicals in nutrients, household products and drugs and won the “Tox21 Data Challenge” of NIH, FDA and NCATS. ^(#3,4,5)

All these trigger points shows that hype surrounding data science was there 2009 onwards. Obviously one could argue we need more evidence, but these trigger points are valid. Leave your comments too on what you think.

In recent years, internet penetration increased, and data availability ballooned. In a competitive market, everyone needed an edge to get past their competitor and data seems to have the answer. Only thing is, the data needed special processing and understanding in many cases to make true sense out of it. That is where ‘Data Scientist’, ‘Data Analyst’, ‘Machine Learning Engineer’ etc. come into play.

As data continues to grow in both size and complexity, the demand for skilled data scientists will only continue to increase. Whether you’re just starting out in the field or you’re a seasoned professional, there’s never been a better time to pursue a career in data science.

Where do you see application of data science or Machine learning in your day-to-day life?

Leave your comment about the article and suggestion for next topic also.

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References:

1. The Intelligent Industrial Revolution | NVIDIA Blog
2. GPU- Graphics Processing Units
3. Merck Molecular Activity Challenge”. kaggle.com. Archived from the original on 2020-07-16. Retrieved 2020-07-16.
4.  “Multi-task Neural Networks for QSAR Predictions | Data Science Association”. www.datascienceassn.org. Archived from the original on 30 April 2017. Retrieved 14 June 2017.
5. “NCATS Announces Tox21 Data Challenge Winners”. Archived from the original on 2015-09-08. Retrieved 2015-03-05.