Data Science Vs Data Engineering

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Data Science Vs Data Engineering Introduction The current century is the century of data. Since the onset of internet-based technologies there has been massive consumption and generation of data. This opportunity of storage, transfer and retrieval of data has helped in creation of several tools, technologies as well as newer disciplines for its study. Two such disciplines that we are going to discuss today is Data Science and Data Engineering. Data Science – The term data science was used since 1960’s as a substitute for computer science. Although, in 2001 the word Data Science was presented as an independent discipline. Since then, researchers and scientists have tried to explain the meaning based on their own understanding and work. Hence…show more content…
Data Science – As we have already seen in the definition, data science is an intersection of multi-disciplinary activity. The major focus or purpose of data science is to extract meaningful, valuable and actionable insights from raw data using different analytical and computational techniques. The end goal still remains in efficient decision making and domain specific value addition, cost saving, increase in revenues etc. They accomplish this by using statistical methods, computational algorithms which may or not involve Machine Learning or Artificial Intelligence, which is imbibed with the domain or business expertise for extracting the best possible business outcome from the data. In short, the end product of data science is a data product. A data scientist is required to be a statistician, mathematician and an efficient programmer at the same time. An example of data product can be a recommendation engine like YouTube recommended video list, e-mail filters for identifying the spam and non-spam…show more content…
Both address distinct problem area and requires specialized skillsets and approach for dealing with day to day problem. While Data Engineering may not involve Machine learning and statistical model, they need to transform the data so that data scientists may develop machine learning models on top of it. Although data scientists may develop core algorithm for analyzing and visualizing the data, yet the are completely dependent on data engineers for their requirement for processed and enriched data. Both fields have plenty of opportunity and scope of work, with increasing data and advent of IoT and Big data technologies there will be a massive requirement of data scientists and data engineers in almost every IT based organization. For those interested in these areas, it's not too late to
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