Data science jobs are hot right now. We are generating more data than ever, and artificial intelligence gives businesses the ability to slice and dice that data to give them actionable business intelligence. Between 2011 and 2012 job listings for data scientists rose 15,000%, and while the growth has slowed, these jobs are still in increasingly high demand.
By this year there will be more than 2.7 million job openings for data scientists. People who take these jobs will need to know things like Python, PyTorch, R programming language, Hadoop, Apache Spark, and more. They will fall into three main categories:
- Data Engineers will create and maintain the framework in which data is brought in
- Software Engineers will design the software to fit an individual business’ needs
- AI Hardware Specialists will tell the algorithms how to analyze the data to get the answers they are looking for
The role of a data scientist may sound shiny and brand new, but the theories behind data science have been around hundreds of years. In the 1740s, Bayes’ Theorem laid out the parameters for using data to improve knowledge. By the 1950s this theorem was used to demonstrate that data could be used to improve knowledge through volume; more data meant better knowledge. These theories have been used to program and train artificial intelligence through the decades.
The term “data science” first appeared in 1996, but already the concept of such a field was well-established. In 2013 IBM revealed that 90% of the world’s data had been created in the previous two years, and as technology advances so does the volume of data produced and collected. These massive volumes of data are too difficult for ordinary humans to examine and extrapolate from, but artificial intelligence algorithms make quick work of it in the right framework. Learn more about the future of data science and the people behind it from the infographic below.
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Last Updated on February 3, 2021.
