If you’re interested in a career working with big data and crunching numbers, there are two paths you may want to consider—becoming a data analyst or a data scientist. What is the difference between data analysts and data scientists? We’ll take a look at the differences and the career paths for both disciplines.

Employers are looking for professionals with data-driven skills such as analytics, machine learning and artificial intelligence. As the world relies increasingly on data in many aspects of business, research and the economy, both data scientists and analysts are in demand with salaries typically above the national average.

A data analyst typically gathers data to identify trends that help business leaders make strategic decisions. The discipline is focused on performing statistical analyses to help answer questions and solve problems. A data analyst uses tools such as SQL to make queries to relational databases. A data analyst may also clean data, or put it in a usable format, discarding irrelevant or unusable information or figuring out how to deal with missing data.

A data analyst typically works as part of an interdisciplinary team to determine the organization’s goals and then manage the process of mining, cleaning and analyzing the data. The data analyst uses programming languages like R and SAS, visualization tools like Power BI and Tableau, and communication skills to develop and convey their findings.

A data scientist will typically be more involved with designing data modeling processes, creating algorithms and predictive models. Therefore, data scientists may spend more time designing tools, automation systems and data frameworks.

Compared to a data analyst, a data scientist may be more focused on developing new tools and methods to extract the information the organization requires to solve complex problems. It’s also beneficial to possess business intuition and critical-thinking skills to understand the implications of the data. Some in the field might describe a data scientist as someone who not only has mathematical and statistical knowledge but also the skills of a hacker to approach problems in innovative ways.

Both career paths require at least a bachelor’s degree in a quantitative field such as mathematics, computer science or statistics.

A data analyst may spend more time on routine analysis, providing reports regularly. A data scientist may design the way data is stored, manipulated and analyzed. Simply put, a data analyst makes sense out of existing data, whereas a data scientist works on new ways of capturing and analyzing data to be used by the analysts.

If you love numbers and statistics as well as computer programming, either path could be a good fit for your career goals. An analyst typically works on answering specific questions about the organization’s business. A data scientist may work at a more macro level to develop new ways of asking and answering important questions.

Although each role is focused on analyzing data to gain actionable insights for their organization, they’re sometimes defined by the tools they use. It helps data analysts to be proficient with relational database software, business intelligence programs and statistical software. Data scientists tend to use Python, Java and machine learning to manipulate and analyze data.

To become a data analyst or data scientist, it may benefit you to obtain at least a bachelor’s degree in a quantitative field such as mathematics, statistics or computer science. But some analysts may have a bachelor’s in business with a focus or concentration in analytics.

Education: According to an O*NET OnLine report, 76% of business intelligence analysts have a bachelor’s degree and 14% hold a master’s degree. Data scientists and predictive analytics professionals (PAPs) are more likely to have an advanced degree. According to a Burtch Works study of salaries of data scientists and predictive analytics professionals [PDF, 1 MB] released in August 2020, at least 94% of data scientists who participated in the study held a master’s or doctorate and 86% of PAPs had a master’s or doctorate. The study also found that salaries of  professionals with advanced degrees tended to be higher than for those with only a bachelor’s degree.

Work Experience: Data science bootcamps and master’s programs in data science can allow professionals to move their careers in a different direction. There may be higher demand for professionals with work experience. Burtch Works found that 42% of the data scientists and 37% of PAPs they sampled had zero to five years of experience.

A data analyst or data scientist’s role and responsibilities may vary depending on the industry and location where they work. A data analyst’s day may involve figuring out how or why something happened—such as why sales dropped—or creating dashboards that support KPIs. Data scientists, on the other hand, are more concerned with what will or could happen, using data modeling techniques and big data frameworks such as Spark.

It may be helpful to read job descriptions carefully so you have a better understanding of a company’s expectations. In some cases, job postings for data scientists may actually involve the responsibilities of a data analyst and vice versa. To get a better idea of the differences between data analysts and data scientists, here are some of the common job responsibilities of data analysts and data scientists.

Each role analyzes data and gains actionable insights to make business decisions. Data analysts use SQL, business intelligence software and SAS, a statistical software, while data scientists use Python, JAVA and machine learning to make sense of data.

There is some overlap in analytics between data scientist skills and data analyst skills, but the main differences are that data scientists typically use programming languages such as Python and R, while data analysts may use SQL or Excel to query, clean or make sense of data. Another difference is the techniques or tools they use to model data: Data analysts typically use Excel and data scientists use machine learning. It’s important to note that some advanced analysts may use programming languages or have familiarity with big data.

To better understand the differences between data analysts and data scientists, here are some of the common job skills of data analysts and data scientists.Data Analysts vs Data Scientists

A data analyst or data scientist’s salary may vary depending on their industry and employer. The job outlook for data scientists is bright and the projected growth between . According to O*NET, data analysts may earn a median annual salary of $98,230.

In addition to computer science, some data scientists may choose to apply their skills to specific areas of interest to them, such as engineering and natural sciences. To advance their careers, they can dig deeper with an online master’s in data science program.

The data scientist route focuses on learning frameworks for processing, analyzing, modeling and drawing conclusions from data. A data scientist might use a data lake to manage unstructured data for analysis.

A data analyst might pursue knowledge to use statistics, analytics technology and business intelligence to answer specific questions for the organization.

In addition to technical skills, data analysts and data scientists may benefit from soft skills to work in teams and communicate their findings. They should understand their organization’s priorities and nuances and apply critical thinking and business intuition to communicate their process and findings.

A data analyst may start out in an entry-level role where their main responsibilities are reporting and creating dashboards. The next step may be to take on a role that involves strategy or advanced analytics techniques. Taking it a step further, an advanced analyst may be interested in a managerial role and become an analytics manager after working for over nine years. In some cases, a data analyst may continue their education and sharpen their skills to become a data scientist.

There is currently a skills gap in data science, with many more open positions than there are skilled professionals to fill them. Companies seeking to fill these roles are looking to career-changers who have completed bootcamps, as well as training their current employees. Someone currently working as a data scientist may choose to continue their education and earn a doctorate to position themselves for more advanced data science roles.

For more information on careers in data science, read these helpful guides:

Last updated: April 2022