Big Data is no longer a field that is about to blow up in the coming years. That future is here now, Big Data has been seamlessly accepted into many organizations. New job positions are also created for students who are picking up interest in the field. The most coveted or popular among these are Data Scientists and Data Engineers.
Universities and colleges are also picking up the pace and providing courses in Engineering such as TERNA Nerul. There are also more engineering colleges in Mumbai where education can lead to working as either Data Scientist or Data Engineer.
Both of these job profiles are similar in some aspects but also largely different. In order to understand which profile could be a good fit, one must learn the differences between both, which have been outlined below.
What is Data Science?
Data Science is a field of education that can be hard to put together in simple words. Data Science, in the whole process of data, comes after data collection and clean-up. They will use the data cleaned up by the Data Engineering department. Their primary work is to analyze the data, find trends, and make predictions. These findings are then put in a report for the upper management to use for important decision-making processes.
Data Science professionals are individuals with solid statistics knowledge, are data-driven, and possess expert-level technical skills. Most Data Science individuals know a couple of programming languages, depending on the organization they work at. Some such languages are R, Python, D3, Apache Spark, NoSQL Database, GitHub, Apache Pig, etc.
Data Scientists are a job role that went through a major boom in recent times. More and more students are learning about it and understanding its importance in the coming years. They are picking up the necessary skills for this role and make average salaries of up to INR 8.63 LPA.
What is Data Engineering?
To understand what Data Engineering is, let’s concentrate on the word ‘Engineering’. Engineering refers to building things after careful designing. Data Engineering, in the same vein, is the designing and creating of pipelines to transform raw data to be then transported to another executive for analysis.
Data Engineering professionals can design and build entire data systems for organizations. They will work on the raw data another IT professional collected, clean it up, and then forward it to the Data Science professionals. This is where the data is then analyzed and predictions are made.
This is a wide field with several job roles the primary of which is a Data Engineer. The Data Engineer generally has a bachelor’s degree in Engineering in Computer Science. Post this they pick up programming languages as per their wish and a master’s degree too. In India, Data Engineers earn an average salary of INR 9.64 LPA.
Difference between Data Scientist and Data Engineer
As evident by the definitions stated above, Data Scientists and Data Engineers have overlapping work responsibilities. One’s work is not possible without the others. There are some major differences, in particular, in the work done and the end goal of their work.
To start understanding the differences between a Data Scientist and a Data Engineer, one must first look at the hierarchy of work. From the bottom up, the Data Engineer is the second point in the whole data science process. The Data Infrastructure Engineer will collect data from sensors and external sources and give it to the Data Engineer. Here, the data is stored and transformed into ‘pipelines’ to be used by the Data Scientist. The pipelines are analyzed, tested, optimized, and presented in a suitable format to the company by the Data Scientist.
Data Engineers need to rely on the Data Infrastructure Engineers, Managers, and other non-technical executives to perform their job. Instructions on what to do, how to collect data etc are points these individuals help the Data Engineers with. On the other hand, Data Scientists are dependent only on the work of Data Engineers.
Data Engineers, being low in the chain of command, have no say in the decision-making. They are given goals on how to analyze the raw data. Data Scientists, do have some say in decision-making. Their work, particularly, the prepared reports is used by the upper management in important business decisions. Their reports state trends and predictions can make or break these decisions and influence the course of the organizations.
Data Engineers do not need to possess storytelling skills or presentation skills to showcase their work. Their work is more logic-based and figuring out how to best clean the data. This is done by the Data Scientist. A certain degree of storytelling ability is needed to make the work done by the Data Science department more effective.
Data Engineers must know programming languages and tools such as MySQL, Sqoop, Redis, Riak, Oracle, Hike, etc. Future Data Scientists learn Python, R, SAS, SPSS, Julia at different BE colleges in Mumbai.
Interrelated roles of Data Scientist and Data Engineer
As written above, both Data Scientists and Data Engineers work together and influence each other’s work. Only on the successful cleaning and moving of data done by the Data Engineer can the Data Scientist create analysis and reports. There is a great extent of overlapping between both roles and on an average day, there is a lot of communication between both roles. While Data Scientists are the face of the department, Data Engineers are the magicians behind the screen.
Data Scientists typically create reports and perform analyses for the upper management. However, they can also dictate down the chain to the Data Engineers how to better collect data. What sort of data is required and how to build a particular pipeline, as per their needs are some information that a Data Scientist can communicate to the Data Engineer.
Similarly, in cases where Data Engineers need access to certain information or if there is a mistake in the prepared reports, they can contact the Data Scientist for the same.
Both Data Engineering and Data Scientists are undeniable and necessary roles in any organization. As the demand for Big Data is increasing in India, the demand for trained professionals for these roles will also increase. To make the most of it, brush up on some programming languages and analytical skills.