How students can choose between Data Science and Data Engineering

With the demand for these professionals on the steady rise, there is no time like the present to delve into the nuances of each career and how you can pursue either profession.

Industry 4.0 has revolutionised the interactions between data and industrial operations, creating numerous job opportunities in various sectors. A report released in 2017 famously said that data would be the most useful resource in the world, and this certainly holds true today.

Big data has indeed changed the world, and companies today have access to several billion gigabytes of data generated every day at various operational stages. There is, therefore, significant emphasis on the role of data and analytics in the modern industrial structure.

Data Science is one of the fastest expanding sectors in the world, and in India- widely considered home to several large data-related operations, an array of data-related professions such as data science, data engineering, data analytics, data architects and big data engineers, provide significant opportunities for growth. Industries in which large volumes of data are generated constantly, such as Health Care, E-Commerce and Mobility, are in imminent need of data scientists and engineers. There has been a sharp incline in the global demand for data scientists and data engineers, and almost four-fifths of the world's enterprises have begun incorporating data scientists and data engineers into their business structures or future projects. With the demand for these professionals on the steady rise, there is no time like the present to delve into the nuances of each career and how you can pursue either profession.

Data Science versus Data Engineering

Data Science refers to understanding and analysing certain core human abilities such as vision, speech and cognitive abilities, and designing machines and software applications meant to emulate these human actions. Data Science is an associated discipline of Big Data, designed to examine tremendous quantities of complicated or raw data, and offer relevant information to the firm based on that data. It makes it easier for businesses to improve their performance, efficiency, customer happiness, and financial goals.

Data Engineering on the other hand, involves the development and maintenance of data pipelines that are commonly used by data scientists for their analysis. It would not be incorrect to say that the data engineer sets the foundation that is built upon further by the data scientist. The biggest difference between the two is that while the former builds and maintains the pipeline systems and structures that store and organise data, the latter analyses the data to identify patterns and predict market behaviour, identify answers to questions relevant to operational activities, etc.

Skills and attributes

Data Scientists typically run projects that involve large volumes of data and are expected to explore and analyse the data, and identify and formulate questions and relevant findings. This means that a Data Scientist must possess broad knowledge of machine learning algorithms, statistics, and data mining and be aware of the different techniques used in big data infrastructures. Students interested in pursuing a career in Data Science must be proficient in programming languages such as SQL, Python, and Java, as well as possess competency in subjects such as multivariate calculus, probability, deep learning, and database management.

On the other hand, programming, system architecture, database design and configuration, interface and sensor configuration and distributed systems are among the skills considered necessary to pursue a career in data engineering.

Data engineers must possess an in-depth understanding of database management and SQL, along with proficiency in handling data transformation, mining and buffering tools. It is evident that although data science and engineering have distinct skill requirements, there are areas of overlap between the two. This overlap means that both data scientists and engineers possess the requisite foundational knowledge to transition to the other side, with only minimal additional training required.

There is no recipe for success in any field. However, just as with any profession, it is important to ensure that your skills do not stagnate- brush up on technologies, programming, and increase proficiency in various programming languages and analytics tools. Build your practical experience and engage with the diverse data science community. And most importantly, never stop learning.

The way data interacts with the world is constantly shifting, and every day brings with it new opportunities and possibilities. As an aspiring data scientist or data engineer, you must ensure that you constantly update yourself on new developments around you and keep learning!

(The author is the Country Head and Director - IET India)