Data analyst vs. data scientist: the data behind the differences


The digital universe contains about 33 zettabytes of data, which is almost as many digital bits as there are stars in the observable universe. There’s a lot of power hidden in that mind-boggling amount of data, but it’s all useless without data analysts and data scientists to make sense of it.

Thanks to this incredibly fast pace of data creation, the number of open roles for both data analysts and data scientists has increased in recent years. In fact, data science jobs on have increased by nearly 55% from June 2017 to June 2019—but data analyst jobs have only increased by 7%. What’s more, the number of people searching for data science jobs on Indeed increased by over 8%, while the number searching for data analyst jobs actually decreased by over 8%.

What might be causing the disparity among both employers and job seekers? Read on to discover more about the (sometimes surprising) differences between data analysts and data scientists, including interesting salary stats, the most in-demand skills and the education level required for each role.

Table of contents

Data analyst vs. data scientist: Salary

Let’s start by taking a peek inside the wallets of both data scientists and data analysts—and where their paychecks stretch the furthest. Data analysts can expect to earn an average annual income of $65,364. Data scientists, on the other hand, can expect to bring in $121,189 per year. That means data scientists make an impressive 86.15% more per year than data analysts.

But as with every tech role, certain metro areas offer higher-than-average salaries. And as you’ll see, it’s striking just how much a high-paying metro can start to close the salary gap between the two roles. 

According to Indeed, data analysts typically earn the most in non-traditional tech areas, like San Antonio, TX where the average salary adjusted for cost of living is $87,666.30 (about $22,000 more than the average national data analyst salary).

top 5 data analyst salaries

In contrast, data scientists can usually land the highest salaries in bigger cities famous for high tech activity, like San Francisco, where the COL-adjusted average salary is $128,240 (about $7,000 above the average national data scientist salary). This higher compensation potential might partially explain why more people are searching for data science jobs, as opposed to data analyst jobs.

But why does such a huge pay gap exist? After all, data scientists and data analysts both have the same goals: make sense of data, find patterns and trends and use that to inform critical business decisions. As you’ll see below, despite similar goals, data scientists and data analysts bring different skills, education and levels of experience to their roles, which, in turn, affects their compensation. 

Data analyst vs. data scientist: Key role responsibilities

To start unpacking why there’s such a huge salary disparity between data analysts and data scientists, let’s take a look at the fundamental role differences.

What does a data analyst do?

Data analysts sift through structured data that easily fits into the rows and columns of a spreadsheet or database (e.g., what people have listened to on Spotify, retail store purchase histories, medical records) to uncover hidden insights and trends that aren’t immediately obvious. 

They’re in charge of creating reports, charts and other data visualizations to communicate their findings to senior management and non-technical audiences. By discovering actionable insights, data analysts fuel critical decision-making and help solve business problems that lead to immediate improvements. 

A data analyst in the transportation industry, for example, might gather, process and organize data from existing datasets like dispatch reports and transportation databases, including break down stats, incidents and late departures, to identify patterns and make recommendations to improve the efficiency of bus or train services (and save money in the process).

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What does a data scientist do?

Data scientists are qualified to do the work of a data analyst…and much more. They often interpret bigger, more complex datasets, including both structured and unstructured data (e.g., Netflix video thumbnails, social media activity, audio files). Plus, they design experiments—after all, they are data “scientists”—to solve advanced problems with code and build predictive models and machine learning algorithms.

Data scientists don’t just answer questions, they identify the right questions to ask based on business problems, as well as figure out what research questions they can answer given huge volumes of disconnected data—all with the end goal of helping businesses make accurate, informed decisions.

Take music streaming service Spotify, for example. Unlike data analysts, who might focus on pinpointing music listening patterns, data scientists transform terabytes of data into audience segmentation models that help engineers build personalized music recommendation engines (as seen in its “Discover Weekly” and “Daily Mix” custom playlists). Another data science team at Spotify focuses on user behavior, monetization research and exploratory analysis to support the creation of targeted ads.

Stand out from the crowd: Check out 7 of the best kept secrets behind winning data science resumes.

Breaking it down: The main differences

  • Data analysts answer a set of well-defined questions asked by the business, while data scientists both formulate and answer their own open-ended questions to derive business insights.
  • Data analysts primarily work with structured data from a single source, while data scientists focus on making sense of messier, unstructured data from multiple disconnected sources.
  • Data analysts organize and sort through data to solve present problems, while data scientists leverage their background in computer science, math and statistics to predict the future.

Data analyst vs. data scientist: Top skills

With different responsibilities come different skill sets. Our data shows that the most common skills mentioned in data scientist and data analyst job postings have a lot of overlap, but with some key differences. Here’s a deeper dive into what employers are looking for when hiring for both roles.

data analyst vs data scientist top skills

As you can see, machine learning tops the list for both roles. However, it’s important to note that over 34% of all data science job postings ask for machine learning, but only 3% of data analyst job postings include it, despite it being the #1 skill. This means that even though ML might give data analysts a competitive advantage, it’s not necessarily a requirement. Plus, since there’s not one “standout” skill for data analysts, it could also mean that the definition of “data analyst” is a little bit fuzzy, meaning different things to different companies

What’s also interesting is that the most desired skills for data scientists include advanced coding languages. For instance, employers are looking for data scientists with knowledge of at least one programming language, such as Python (#3), R (#4) or Java (#7). Data analysts, on the other hand, should have a basic understanding of statistical languages like Python or R, but these skills are farther down on the list, at #7 and #8 respectively. Plus, Java doesn’t even make the top 10 for data analysts. 

What are the key differences?

Looking at the top skills for each role reveals major differences. For instance, data scientists need expert-level coding expertise, but data analysts don’t. Instead, data analysts should know data management programs and data visualization software, such as Microsoft Excel (#5) and Tableau (#6)—skills that don’t make the top 10 for data scientists. Data scientists also need to know big data tools like Spark and Hadoop, which don’t top employer priorities for analysts. 

What are the overlapping skills?

Beyond machine learning and some of the coding languages we’ve already discussed, you’ll also see that SQL, scripting and Stata are in high demand for both data analysts and data scientists. That’s because these tools are fundamental for extracting data from different sources and slicing and dicing it.

With all of these overlapping skills, it’s quite possible for a data analyst to transition into a data scientist role. But they first need to fill in some of their skills gaps by adding data mining, big data tools like Hadoop and Spark, and languages like Java to their resume. 

For those looking to level up from data analysis to data science, you’ll also need to consider educational requirements, which we’ll talk about in the next section.

Education matters

No tech job truly requires a certain level of education—at least not anymore. After all, Apple, Facebook and Airbnb are just a few of the tech giants that recently announced they’re no longer requiring their employees to have a college degree, instead looking for equivalent practical experience.

But while self-taught data scientists and data analysts do exist—and bootcamp grads have gone on to have successful careers in the space—landing these types of roles is much easier when you have a formal college degree. In fact, the vast majority of data scientists hold a master’s or PhD in computer science, engineering, math or statistics. One report even suggests that 94% of data scientists have a master’s or PhD. That’s because complex data mining, algorithm building and big data analysis requires a solid CS or math foundation.

Data analysis, however, has a lower barrier to entry, typically only requiring an undergraduate degree in a technology or business-related field. In fact, much of a data analyst’s skill set can be learned on the job. 

Making sense of it all

When it comes down to it, a data scientist can’t be successful without a data analyst, and vice versa. Breaking into data science requires more of an upfront investment (more advanced education, skills, etc.) but comes with a higher payoff when it comes to salary. Plus, the data science job market appears to be growing at a faster pace than the data analyst job market, which means there could be even more opportunities for this hot job in the future.

When thinking about whether to pursue a data analysis or data science career path, consider your interests, how much you’re willing to put into your education and what kinds of projects you’d like to work on. Then, shoot for the stars (after all, the amount of data might surpass the number of stars in the sky sooner than you might think).

*Methodology: Indeed analyzed the percentage change in the share of job postings with “data” and either “analyst” or “analysis” in the job title and the share of job searches per million for “data analyst” or “data analysis” over a two-year period from June 2017 to June 2019.

We also analyzed the percentage change in the share of job postings with “data” and either “science” or “scientist” in the job title and the share of job searches per million for “data scientist” or “data science” over a two-year period from June 2017 to June 2019.

For the most in-demand tech skills, verified skills listed in job postings on with “data” and either “analyst” or “analysis” in the job title were calculated as a share of all postings over a six-month period through July, 2019.

Additionally, verified skills listed in job postings on with “data” and either “science” or “scientist” in the job title were calculated as a share of all postings over a six-month period through July, 2019.

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