Data Science vs Data Analytics: Which one is better for you?

Data never sleeps and in today’s world, without utilizing the wealth of digital information available at our fingertips, a brand or business risks missing vital insights that can help it grow, scale, evolve, and remain competitive.

That said, to spare you any confusion and offer you a clearcut insight into these two innovative fields, here we explore data science vs data analytics in a business context, starting with an explanation.

What Is Data Science?

Data science focuses on uncovering answers to the questions that we may not have realized needed answering. Experts in the field utilize techniques to drill down into complex data, combining computer science, predictive analytics, statistics, and machine learning.

What Is Data Analytics?

Primarily, data analytics is focused on processing and conducting critical statistical analysis on current or existing data sets. The main role of a data analyst is to create methods to capture, collect, curate process, and arrange data from different sources.

Data Science vs Data Analytics

Data science is an umbrella term for a more comprehensive set of fields that are focused on mining big data sets and discovering innovative new insights, trends, methods, and processes.

Data analytics is a discipline based on gaining actionable insights to assist in a business’s professional growth in an immediate sense. It is part of a wider mission and could be considered a branch of data science.

n summary, science sources broader insights centered on the questions that need asking and subsequently answering, while data analytics is a process dedicated to providing solutions to problems, issues, or roadblocks that are already present.

• Skillset

Looking at data science vs data analytics in more depth, one element that sets the two disciplines apart is the skills or knowledge required to deliver successful results.

Concerning data analytics, a solid understanding of mathematics and statistical skills is essential, as well as programming skills and a working knowledge of online tools, and intermediate statistics. Data analysts and are well versed in SQL, they know some Regular Expressions, and can slice and dice the data.

• Scope

When we use the word “scope” concerning data analytics vs data science, we’re talking big and small, or more specifically, macro and micro.

Essentially, as mentioned, science is, at its core, a macro field that is multidisciplinary, covering a wider field of data exploration, working with enormous sets of structured and unstructured data.

Similarities of the data science and data analytics:

Differences aside, when exploring data science vs analytics, it’s important to note the similarities between the two — the biggest one being the use of big data.

Businesses that choose to leverage the full potential of big data analytics can optimize their operational margins by up to 60% — and as both fields focus on big data, the rewards of exploring science and analysis have the potential to be great.