More often than not, anything to do with data processing gets grouped under the umbrella term of data science. This can lead to bewilderment when terms such as data analytics come up, which, while sometimes used interchangeably with data science is in actuality reference an interconnected but distinct field. Both areas ripe with opportunity and worthy of study, it is important and useful to distinguish between them, and the differing skillsets they require and roles the play in the industry.

The actual process, however, of separating them is thorny, unclear, and generally unwelcoming. It gets even worse because data analytics, depending on the site you ask, is either a subset or a distinct field entirely from data science. Both require data, both study data, and both extract insights from it – so what differentiates them?

What is Data Science?

Data Science, defined specifically, generally examines problems of a more uncertain nature than data analytics. As Dana Liberty from Sisense puts it, “the field primarily fixates on unearthing answers to the things we don’t know we don’t know”. Its aim is more in the production of new areas of research, that is, it creates more questions rather than definite answers. It creates algorithms and new ways of mapping data rather than using them. If the words machine learning and AI mean anything to you, they fall definitively under the field of data science. Think of it as being exploratory in nature, a journey without a destination: mapping out the landscape of data, as it were, and searching for the unknown.

What is Data Analytics?

Meanwhile, Data Analytics is far more interested in answers, insights and checking a fixed hypothesis. It is a field with its focus on practical problem-solving, faced with immediate problems that require solutions rather than exploration. The insights drawn by a data analyst can often be translated to immediately actionable strategy. It answers queries rather than creating them, covering both the organisation of the existing dataset and predictive analytics drawing from that data. Its primary goal is the creation of a conclusion, rather than another hypothesis.

Commonalities and Differences

“If data science is the house that hold the tools and methods, data analytics is a specific room in that house.” This is not an incorrect statement. In general, data analytics has a narrower focus than data science, with problems of shorter term and more specific inquiries. Data analysis could also be described as ‘a necessary level of data science’, or the most applicable and simplest form of it.

The education level and skillset required for working in data science is generally of a higher level than for those who work in data analytics. Data Science jobs are typically filled by those with a doctorate or a master’s in maths or computer science. In fact, 88% of data scientists have a Master’s degree, and 46% have PhDs. Meanwhile data analytics is a field more willing to consider people without postgraduate education. Both require proficiency in programming languages, mathematics and data analysis, but the scope and expertise that data science requires tends to be demanding than that of data analytics. Accordingly, data science also pays better, with data scientists making between $63K-$130K a year while data analysts make between $51K - $99K.

Finally, it is important also to remember that these terms are not so much universal as they are defined on a case-to-case basis. Each company has its own definition, and some will make a distinction between these two terms while others will not. Both these fields are promising, ‘hot topics’, as it were, but their subtle differences do warrant consideration in planning your own desired career path.