Data Science, a booming career option for present year as well as for the coming year. In the year 1962 John Tukey described a field of study as “Data Analysis,” which in the modern world is termed as “Data Science.” Subsequently, participants at the 1992 Statistics Symposium at the University of Montpellier II noted the advent of a new field based on data of diverse sources and types, integrating existing definitions and principles of statistics and data processing with computing.
Nowadays almost every company and every small enterprises are using data science techniques to make more data driven decisions. Along with Data Science, Big Data is also emerging as an essential tool for small as well as big companies. Data Scientists are responsible to break the big data and derive meaningful insights from the it. The data is transformed, cleaned, imputed to build Machine Learning model from it. The most common techniques used are Linear Regression, Logistic Regression, K-Mean, Random Forest, etc.,
With the emerging technology and the changes there is a need to understand if Automation is really taking up the place of data scientists. Either by aspiring data scientists or by data practitioner this questions often comes up that will this field kill the jobs of data scientists.
However the answer lies in the data again. To understand what is inside the data human intervention is needed and will be needed always. There are a number of amazing automated libraries to do the EDA part, data visualization, data processing. These libraries will no doubt help reduce the human input. However, will not kill the jobs. Gartner predicted that almost 40% of data scientist jobs will be automated by 2020. Thus reducing the efforts from a data scientist but not killing their demand. Automation will make things to be done in an efficient way. As mentioned by Alexander Gray, vice president of AI at IBM describes automation as a mechanization to do tedious tasks. He calls the automation tools as a “timesaving benefit.”
Again, domain knowledge plays a very important role. Domain knowledge grows over time which cannot be done by any automation libraries. The ability to understand the business problem is very much needed in any project. Automation and Data Science comes hand in hand and will only help to create more opportunities for data scientist.