Data Science vs Data Analytics: What’s the Difference?

According to LinkedIn, data science careers have grown 650% since 2012. The U.S. Bureau of Labor Statistics expects that by 2026, there will be approximately 11.5 million new jobs in data science.

It is a promising career path, but you may wonder, why get into a data science role and not data analytics? What makes them different, data science vs data analytics?

Both are great career choices and are in high demand. They both help organizations make informed decisions, increase profitability, and increase efficiency. But to get into the specifics of what makes them unique, keep reading this article and we will share more right now.

What Is Data Science?

It is the study of vast volumes of data. Data science uses modern techniques and tools to find patterns otherwise not seen to derive information that is meaningful. The purpose of this field is to help companies make better-informed decisions.

In this field, professionals leverage complex machine learning algorithms to build predictive models. The data that a data scientist will analyze comes from various sources. It can present in multiple formats.

Importance of Data Science

Enabling organizations to comprehend gigantic amounts of data efficiently from different sources, data scientists are key to driving valuable insight. They equip companies to make smart decisions based on data-driven evidence. Industries like banking, finance, healthcare, marketing, and more thrive from the importance of data science.

If you want to learn more about finding exceptional careers in data science, check out these great data science recruiters.

What Is Data Analytics?

Otherwise referred to as “DA,” data analytics examines data sets. It takes information and finds trends, drawing conclusions. More and more, the field of data analytics is performed with software and specialized systems aiding it.

A data analyst exists at the intersection of business, information technology, and statistics. Combining these fields helps organizations to be successful. The number one goal of the data analyst is to improve performance and increase efficiency by finding data patterns.

Types of Data Analysis

There are primarily four types of analytics, which include descriptive, diagnostic, predictive, and prescriptive. Each type has a distinct part of the process in data analysis, and they each have a different goal.

• Descriptive–answers “What happened?”
• Diagnostic–answers “Why do things happen?”
• Predictive–answers “What will happen in the future?”
• Prescriptive–answers “What should be done?”

With these types of data analytics, an organization has the insight they need to make decisions that are effective.

Data Science vs Data Analytics

While you may assume these terms are interchangeable, they are not. They are indeed unique fields. The distinction between data science and analytics is their scope.

Data science is a term that is an “umbrella” covering a group of fields. This field will mine large datasets. Data analytics is part of a larger process of realizing actionable insights.

Another major difference is about “exploration.” Data science parses large datasets; it is not concerned with a particular query. Data analysis works when focused.

Seeing the Difference

Now that you know the details between data science vs data analytics, this can be the foundation for finding the right career field for you. What do you find more interesting? Parsing massive datasets and delving into machine learning, or focusing on the larger picture to create practical items for real-world results?

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