Data Science vs. Data Analytics: What's the Difference?

While a career in technology may naturally lead you to consider studying engineering or computer science, in today’s world of predictive marketing, cloud computing and globalized thinking, jobs working with data are among the most in demand.*

Due to an increase in artificial intelligence and other technologies, the U.S. Bureau of Labor Statistics (BLS) projects a 33.5% increase in data scientist roles over the next decade.* That's about 11 times higher than the expected 3.1% growth for all occupations, BLS noted.*

So, if you have a natural sense of curiosity and enjoy discovering new information, a career in data might be a good choice.

The question is, what's the difference between data science and data analytics?

What is Data Science in Simple Terms?

Data science uses the scientific method and machine learning to identify patterns in data, according to Dr. Brian Letort, an adjunct instructor of data analytics at Southern New Hampshire University (SNHU) with 25 years of experience in the field. Data scientists use that data to make predictions.

Data scientists are always looking for ways to best leverage data to address business opportunities. They do this by creating complex mathematical or statistical models and writing algorithms to determine the answers to those questions.

If you're interested in figuring out how to get and apply the information needed to address a business need, a career in data science just might be for you.

Questions data scientists ask: What will happen next? What factors influence outcomes? How can we build systems that learn from data? Can we automate predictions or recommendations?

What is Data Analytics, Then?

While a data scientist focuses on how to best obtain and use data, a data analyst mines existing data to interpret it and present findings based on the specific business needs of their organization.

"A data analyst creates dashboards and visualizations to communicate insights to stakeholders," said Dr. Susan McKenzie, a senior associate dean overseeing STEM programs at SNHU with more than three decades of experience in mathematics, science and data analytics education. "They focus on analyzing business performance through SQL queries and database analysis."

This often involves visual and graphic design skills as well as excellent interpersonal skills and business intelligence.

“A data analyst is a storyteller,” said Letort. “When you look at the data, you look for ways to present it visually to other stakeholders as a narrative, considering the visuals you might use to make the data accessible to them.”

Questions data analysts ask: What happened? Why did it happen? What trends are emerging? What should we do next?

Read more: What is Data Analytics?

Why is Data Important, Anyway?

Using data wisely is critical to the success of most, if not all, organizations. Companies use data to identify their target customers. They can use data to learn why people are behaving a certain way or to help develop new programs or products to sell or provide to customers.

Data can be used to gather all sorts of useful information to support business processes.

A university might use student enrollment data to determine which majors or courses of study to add to or remove from its catalog. Data can also be used by manufacturers to design new prototypes, or by marketers to create new campaigns targeted to a specific audience.

Data Science and Data Analytics in Action

Consider your favorite streaming service. They have millions of bits of data that they gather from customers based on each person’s viewing history and choices.

• A data scientist can create an algorithm to determine what recommendations to make for which other programs customers might enjoy.
• A data analyst might review the data and present it to various stakeholders as justification for adding or removing certain programs.

This is just one example of how data can be used to improve the customer experience and add value to a company’s services or products.

In What Ways Do Data Science and Data Analytics Overlap?

There is significant overlap between the roles of data scientists and data analysts, according to McKenzie — particularly in recent years.

"As AI becomes more integrated into business operations, the line between analyst and scientist is becoming increasingly blurred," she said. "Industry experts note that analysts now frequently use Python and automated modeling tools, while data scientists are expected to communicate business insights and future predictions."

According to McKenzie, both fields use:

• Data collection and cleaning
• Data visualization
• Python or R programming
• SQL and databases
• Statistical analysis

In smaller organizations, especially, the overlap can be so great that data professionals will perform both data science and data analytics tasks, McKenzie noted.

Above all, genuine curiosity is critical in both roles. It's important to become very good at asking questions.

“You really need to have a desire to look at data and try to derive patterns,” said Letort. “Critical thinking and problem solving are key. You need to question everything like you’re a detective to mine the appropriate data and derive meaningful conclusions."

Which is Better: Data Science or Data Analytics?

The answer is subjective and depends on your interests. But McKenzie recommends becoming a data analyst before advancing into data science. The point of entry is a bit more accessible, she said, and allows you to start developing a business-thinking mindset.

"Once data analytics has been mastered, the next step is to learn data science, where predictive modeling, programming and mathematics, as well as machine learning, are introduced," she said.

According to McKenzie and Letort, choose:

Read more: How to Become a Data Scientist

Is Data Science a Lot of Math?

Math is used in both data science and data analytics roles, but McKenzie said it plays an especially significant role within data science.

The types of math that are used include:

• Calculus
• Linear algebra
• Mathematical optimization
• Probability theory
• Statistics

"The good news is that you do not need to be a mathematician to be a data scientist," McKenzie said. "Most successful professionals develop mathematical skills gradually while applying them to real-world problems."

How to Start Your Career in Data

For data analytics roles like operations research analyst, BLS reports that a bachelor's degree is the typical entry-level education.

A bachelor's degree in data analytics includes classes that explore structured databases, scripting, emerging technologies, data validation and more. It will also include math courses like calculus and applied statistics.

While a master's in data analytics is not necessarily required for a successful career, it can help you be competitive in the job market. The more education you have, the more you communicate to your employers your interest in growing in your field. Some employers, BLS noted, actually prefer candidates with a master's degree.

At some schools, like SNHU, eligible data analytics students may be able to take graduate courses while they're still completing their bachelor's degree. If their goal is to earn both a bachelor's and a master's in data analytics, this accelerated degree pathway can help them save time and tuition dollars.

What Types of Data Jobs Can You Pursue?

According to McKenzie, there are job roles for data professionals at all levels:

No matter where you are in your data career, it’s important to always work toward developing new skills. In Letort's experience, the field changes daily.

"To be successful, you need to know enough to stay current but also realize you’ll never know everything,” he said.

Some data professionals choose to upskill by earning professional certificates or certifications in certain tools or aspects of data analytics. At SNHU, for example, you could work on your data storytelling skills over the course of a 6-week professional development course — and earn a Data Literacy Practitioner badge at the end of it.

Being able to do all this takes practice and a flexible business mindset in addition to technical and mathematical expertise.

Will AI Replace Data Analytics?

No. BLS growth predictions indicate the opposite — that AI and other technologies are growing the fields of data analytics and data science.*

But McKenzie said roles are changing dramatically: AI can now create visualizations and dashboards, write queries and automate reports. But humans continue to be necessary to the field.

"Organizations overall need professionals who can validate results, understand data quality, manage ethical considerations, explain outputs and connect analytics to business decisions," McKenzie said.

And yes, AI literacy will be important, too. McKenzie noted that organizations need professionals who can work within an AI-enabled enterprise ecosystem.

Data analytics degree programs can help prepare you for this shift. At SNHU, for example, students can gain technical foundations, problem-solving and communication skills while also getting familiar with emerging technology.

"AI is making technical tasks faster, but it is increasing demand for professionals who can translate data and AI outputs into meaningful business decisions," McKenzie said.* "That is why data analytics remains one of the strongest pathways into data science, AI, business intelligence and digital transformation careers."

*Cited job growth projections may not reflect local and/or short-term economic or job conditions and do not guarantee actual job growth. Actual salaries and/or earning potential may be the result of a combination of factors including, but not limited to: years of experience, industry of employment, geographic location, and worker skill.

A former higher education administrator, Dr. Marie Morganelli is a career educator and writer. She has taught and tutored composition, literature, and writing at all levels from middle school through graduate school. With two graduate degrees in English language and literature, her focus — whether teaching or writing — is in helping to raise the voices of others through the power of storytelling. Connect with her on LinkedIn.