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Data science is perhaps the only field where you use 150-year-old techniques and call yourself cutting edge! But what a wonderful journey the field saw in 150 years from regression to deep learning.
Data Science has blown up the minds of people with the kind of development and pace its’ growing on… There’s no say what it can achieve in the coming future. But for now, let us go on a journey of Data Science and see how it’s become an integral part of our lives over the past years –
Statistics can be dated back from ancient civilizations while its mathematical-science origin can be traced back as early as the 17th Century when Probability theory was developed.
Over the coming years with the advancement of Computers, Mathematics and Statistics, industries had progressed leaps and bounds to make the smartest and scientifically superior computers in the world. (But why we need computers?? – Simple, it’s to work faster)
It was in 1956, the term ‘artificial intelligence’ was coined by a young computer scientist, John McCarthy.
In 1962, John W. Tukey writes “The Future of Data Analysis” wherein he brought the relationship between statistics and analysis…
In the late 1960s, computer scientists worked on Machine Vision Learning and developing machine learning in robots and then boom…
WABOT-1, the first ‘intelligent’ humanoid robot, was built in Japan in 1972.
In 1977, the International Agency of Statistical Computing (IASC) was able to link Statistical Modelling with new edge computing. This was the first time when we were able to convert and extract the data into structured information and knowledge.
From the mid-1970s to the mid-1990s, computer scientists dealt with an acute shortage of funding for AI research. These years were then known as the ‘AI Winters’.
In 1994, a new form of marketing began to appear called “Database Marketing”. This was used for collecting and crunching the user’s data to find patterns and predict the most likely product they would next opt for.
In 1996, the International Federation of Classification Societies (IFCS) met in Kobe, Japan. For the first time, the term “data science” was included in the title of the conference in “Data science, classification, and related methods”.
In 1999, Jacob Zahavi commented on how companies in the coming time were going to require high-end tools to handle the massive volumes of data available.
In 2001, William S. Cleveland published “Data Science: An Action Plan for Expanding the Technical Areas of the Field of Statistics” to expand the technical field of statistics. It glazed the importance of the range and expertise of Data Analysts.
In 2002, a Data Science Journal was published focusing on Data Systems, it’s Internet linkages, applications and legal issues.
In 2005, The National Science Board published “Long-lived Digital Data Collections: Enabling Research and Education in the 21st Century” which was a support in the industry to promote Data Scientists.
In 2006, Hadoop 0.1.0, an open-source, non-relational database, was released. Hadoop was the software version of MapReduce – a technique used to process, handle and store large application volumes of databases. This was an initiation for the rise of Big Data.
In 2008, the title, “Data Scientist” became a buzzword, and eventually a part of the language.
In 2009, ImageNet was created which soon became the catalyst for the AI boom of the 21st century.
Also, after nearly three years, a team led by AT&T Research engineers won the $1 million Netflix Prize for devising the best way to improve the company’s movie recommendation algorithm, generating an average of 30 billion predictions per day.
Strikingly in 2011, job listings for Data Scientists increased by 15,000%. By then, Data Science had proven itself as a biggie in the corporate world.
In 2012, Harvard Business Review published an article “Data Scientist: The Sexiest Job of the 21st Century. Also, the Google Brain team, led by Andrew Ng and Jeff Dean, created a neural network that learnt to recognize cats by watching YouTube videos.
In 2013, we came to know that 90% of the existing Data came in the last 2 years only. Y’all know that by using Deep Learning techniques, Google’s speech recognition and Google Voice experienced a dramatic performance jump of 49 per cent in Google usage.
In 2015, Bloomberg’s Jack Clark wrote that it had been a landmark year for Artificial Intelligence (AI). Within Google, the total of software projects using AI increased as 2,700 projects over the year.
In 2016, we saw the rise of AI – Driven-Chat bots being implemented. (Close to a sci-fi movie) with 24×7 support.
In 2017, Data Scientists felt that the concepts they have been researching all along can also be put use in a comical yet informative way. WalletHub harnessed data of about a hundred cities and weighed in 29 factors like affordability, the number of events, shopping spots etc.. to determine the best city to spend time for Christmas in the USA. Contrary to the popular belief though, Chicago emerged as the winner.
In 2018, Google has open-sourced Bidirectional Encoder Representations from Transformers (BERT), its state-of-the-art training technique for natural language processing (NLP) applications.
Every second even now, there is rapid progress continuously happening in the field of Data Science. Also, the rise in numbers of jobs for Data Scientists in the industry is at its peak. So, this is your chance to be a part of the new era and be at the top of the market.
INSOFE is a place where we are continuously exploring disruptive technologies. To join our PGP Course in Data Science visit INSOFE.