What is the difference between data science and data analytics?

Last updated on October 20th, 2021 at 11:31 am

One of the biggest jobs in the technological center is working with big data. There are plenty of roles within this sector and two of the most popular ones include data science and analytics. While a lot of companies tend to hire similar candidates for these roles, there is still a difference between the two.

It is important that you understand the two roles before you choose a career path in either. If you’re looking to kickstart a career in the field of big data, then knowing the difference between data science and analytics is a good pointer to keep in mind.

What is data science?

Data science is a broader term for different methods and models used to get information. Under data science are the statistics, scientific methods, and math along with other tools which can be used to manipulate and analyze data. If there is a process or a tool that can be used on data to analyze it and extract information from the same, then it falls under the umbrella of data science.

As a practitioner of data science, you’ll have to connect data points and information to figure out connections which can be more useful for a business. It requires you to explore the unknown and find newer patterns or insights which can then be turned into actionable decisions from a business perspective. Data science attempts to delve into connections and figure out methodologies which work for the betterment of a business.

What is data analytics?

Data analytics is more concentrated and specific than data science. It is focused on achieving a specific goal by sorting through large data sets and looking for ways to support the same. Analytics are more automated as they can help in providing better insights in certain areas. Data analysis also involves analyzing large data sets to find smaller, more useful pieces of information to fulfill an organization’s goals.

Analytics basically sorts data into things that an organization knows or doesn’t know and can be used to measure any event in the present, past or even future. It moves from insights to impact and connects patterns and trends along with the true goals of a company, keeping the business aspect in mind.

Knowing the difference:

Data analysts and scientists perform different roles and companies must know exactly what they’re looking for. Data analytics usage in industries such as travel, gaming or healthcare, where analysts can extract specific data to improve business, data science is used in more broader categories such as digital advertising or internet searches.

Data science also plays a role in developing machine learning and Artificial Intelligence. Companies are looking at systems which allow computers to go through large amounts of data. They then formulate algorithms, developed by analysts which can sift through the same and find connections which can help them reach their objectives and thus, bring in more revenue.

Imarticus provides the best data analytics course to make it easier for anybody looking to enter the world of big data science and kickstart their career.