Data scientists and Data engineers should be doing the same kind of work, right? After all, they are both in the field of data thematics. That’s where most get it all wrong. There is a very clear difference between data scientists and data engineers, as this post will show you. Let’s get started.
What is the fuss all about when it comes to these two roles? In India, the global hub of analytics, the demand for professionals skilled in data analytics has grown by more than 50% over the last two years. The concept of Big Data, the hottest industry in India today, has been the driving force between these two professions. Every company wants to understand the hugely complex human behaviors and interactions, and that’s what Big Data does essentially. To properly analyze those largest data sets that show the patterns and trends relating to how humans interact and behave, the roles of both data scientists and data engineers are equally required.
The term “data scientist” was coined by Dr. DhanurjayPatil and Jeff Hammerbacher in 2008. A data scientist is a professional whose primary function is to apply machine learning (ML) and (artificial intelligence) AI marketing tools that automate certain processes within the company in solving critical business problems by turning volumes of big data into valuable and actionable insights. Data scientists use a ton of data science apps, visualization techniques, a narrative of solutions to business problems to interpret and deliver their findings.
Data engineers, on the other hand, are those skilled in the art of problem-solving. They gather, build, design and integrate data resources variants using software, then go right ahead to efficiently manage big data. To define the role of a data engineer more broadly, he/she is someone who develops, constructs, tests and maintains architectures, such as databases and large-scale processing systems.
The scope of data scientists is focused on advanced mathematics and statistical analysis of data generated by data engineers. Data Engineers are focused on building infrastructure and architecture for data generation, which is then used by data scientists.
Another aspect which differentiates data scientists from data engineers is the skill set required. The acquisition of skills like Python programming, R, Scala, Apache Spark, Hadoop, machine learning, deep learning, and statistics, combined with an ability to design new algorithms would totally come in use for data scientists. Experience in handling domains remains a must-have for efficient data science. Data scientists use Data Science Experience, Stats, Julia, Jupyter, and RStudio tools to build statistical models and, to also find data trends and patterns.
The set of tools which data engineers work with include Hive, MongoDB, Cassandra, MySQL, and Sqoop. Skills required of them are many, not limited to just programming, Hadoop, Hive, Data streaming, and SQL skills. Due to the nature of their work, they only need to know the basics of analytics.
Data engineers need a Bachelor’s degree in computer science, applied math, physics, software/computer engineering, or other related fields. For data scientists, their field requires a higher advanced level learning, with some companies leaning towards data scientists with Master’s degree and Ph.D. degrees in artificial intelligence, data science, robotics, machine learning, statistics or in other related fields.
The average starting salary of a data scientist in India ranges between 3lakh-4lakh and can go onto 12lakh- 20lakh per annum, depending on who you work for. For data engineers, they earn an average salary of Rs 12.7 lakh per year.
Irrespective of the differences between data scientists and data engineers, Adohm would always have to work with both data scientists and data engineers in building AI or Data Analysis based products or service pipelines.
By Kuldeep Chaudhary
CEO ADOHM – Advertising through AI