Data Analyst vs Data Scientist: What’s the Difference?

If you’re looking into careers that can put you at the center of the booming data economy, you’ll quickly find that two receive far more attention than the others: data analyst and data scientist. Start looking into educational opportunities that can put you on a path to becoming a data professional, however, and the line between these positions can quickly blur: many master’s degrees, bootcamps, and certificate programs in one discipline emphasize their crossover appeal for the other, or even market themselves as combined data analytics and data science programs.

They can do this because the overlap between data analytics and data science is substantial. For someone looking to upskill and enter the data economy, however, it’s important to understand the differences, as these can have implications for your eventual skill set, on-the-job responsibilities, job prospects, and salary. In this article, we’ll dive into what data analysts and data scientists have in common and what sets them apart. At the end, we’ll share some ways you can learn more about getting started with each.

Data analyst vs data scientist: top-line difference

Ultimately, data analysts and data scientists are working towards the same goal: to harness the raw data produced by almost every aspect of human activity, employ statistical analysis to extract valuable and actionable insight, and communicate this insight to relevant stakeholders to enact meaningful changes that drive growth.

Where data analysts and data scientists differ is in the part they play in this larger process. Generally speaking, data analysts are more narrowly focused on the analysis of data to support discrete initiatives using established techniques, while data scientists are concerned with advanced analytics as well as designing and implementing the pipelines, techniques, and tools — including machine learning models — that enable this analysis. To use a culinary metaphor, data scientists are like the executive chefs at fancy restaurants: they design the flow of the kitchen, develop the recipes, and make sure everything goes smoothly. Data analysts are like the cooks: they prepare food according to the recipe so that the dish tastes great, every time.

In the next sections, we’ll dive deeper by looking at how the differences between data analysts and data scientists manifest in their skill sets, day-to-days, salaries and job outlook, and education.

How do the skill sets for data analysts and data scientists differ?

As you can see below, data analysts and data scientists share a core set of hard skills in computer science, mathematics, data management, as well as soft skills like critical thinking and communication. Because they need to design research projects, develop advanced machine learning models, and communicate with internal and external stakeholders, however, data scientists require a number of additional skills.

Data Analyst	Skills	Data Scientist
Excel, SQL, sometimes a programming language like Python or R	Computer Science	SQL, Python and/or R, cloud computing
Statistics	Mathematics	Statistics, probability, and linear algebra
Data mining for big data, data cleaning, cloud computing	Data Management	Data mining for big data, data cleaning, cloud computing, data engineering
Tableau, matplotlib	Data Visualization	Tableau, matplotlib
Limited, if any, experience necessary	Machine Learning	Unsupervised learning, supervised learning, reinforcement learning, deep learning, predictive analytics
Critical thinking, communication, teamwork	Soft Skills	Research design, critical thinking, communication, teamwork, leadership, data storytelling

Abstracted from a real-world job, it can be difficult to understand how these skills can come together for data analytics or data science. In the next section, we’ll leverage actual job postings to better understand what responsibilities data analysts and data scientists generally have and how their typical days might differ.

How does the day-to-day differ for a data analyst and a data scientist?

Data scientists and data analysts work towards the same ultimate goal — developing actionable new intelligence from data — but because they support this goal in different ways, data scientists focused on developing new methods, data analysts focused on deploying existing ones, their jobs can look very different.

What’s a day in the life of a data analyst look like?

A data analyst’s responsibilities can vary depending on seniority and the company they work at. At the New York Times, an analyst focused on marketing analytics would be responsible for the following:

Building an understanding of channel effectiveness of marketing tactics
Providing analysis and recommendations that can be actioned on to improve marketing performance
Writing SQL to pipeline and analyze big data
Developing dashboards to expand access to data and analytics
Delivering test analysis and insights, advice on future tests and next steps
Collaborating with internal stakeholders to understand the business and develop data-driven insights that are both strategic and operational
Participating in and sometimes leading all phases of analytic work: from problem definition to representation of results
Producing insights, champion them and drive them into action

To perform this job well, the New York Times would prefer candidates to possess the following technical qualifications:

1+ years of experience working with data teams to deliver reporting and analysis or a quantitative degree
Proficiency in SQL and experience working with relational databases
Experience in Python or R
Experience with data visualization tools such as Mode, Tableau, or Looker
Experience with A/B testing
Experience with analyzing marketing efforts and familiarity with typical marketing KPIs.

What’s the take-away? A data analyst’s day-to-day is coding-heavy, with significant amounts of time writing in languages like SQL to provide analysis and develop dashboards, but there is also substantial time spent in meetings to align on business goals and deliver insights. Let’s now dive into the day-to-day of a data scientist so that we can start to see the differences.

What’s a day in the life of a data scientist look like?

As was the case with the data analyst, a data scientist’s responsibilities can vary depending on seniority and company. Generally, they will have more responsibility for defining and overseeing projects than a data analyst, will need to deploy a greater degree of technical expertise, including machine learning, and will interact with a greater variety of stakeholders. At Netflix, for example, a data scientist working in customer insights would be responsible for the following:

Championing research innovation and establishing a program culture of excellence in the conceptualization, design, analysis, and interpretation of consumer research in order to ensure the quality and actionability of our insights.
Effectively leveraging statistics causal inference frameworks to implement a scalable research recruitment and sampling strategy that enables global research across our Consumer Insights team.
Working collaboratively and iteratively throughout the research lifecycle to support survey design, deployment, analysis, and interpretation.
Consulting with Legal and Privacy Engineering teams to ensure best practices in the collection, storage, retention and use of data collected across consumer surveys, focus groups, and other research methodologies.
Partnering with our product and message engineering teams to develop and adopt innovative methods of outreach for consumer research.
Bringing vision to a developing space, establishing best practices and developing dashboards, tools, or other data products to support consumer research at scale and increase the impact and efficiency of our CI team.
Collaborating with Researchers, Engineers, and Data Scientists to conduct analyses and apply statistics and causal inference methods to answer specific day-to-day questions or support larger initiatives.

To perform this role successfully, Netflix would expect candidates to have the following technical qualifications:

Advanced degree in Statistics, Mathematics, Physics, Economics, or a related quantitative field or relevant industry experience.
Strong statistical knowledge and intuition - ideally utilized in experimentation, market, consumer, or social research settings.
Expert quantitative analysis, programming, and data manipulation skills using SQL, R and/or Python, and version control (Github, Stash).
Experience building multi-step ETL jobs/data pipelines and working with job scheduling systems.
Experience in building causal inference solutions
Experience working with 3rd party APIs for data ingestion

Netflix’s job posting paints a picture of an individual with a far more sophisticated skill set, including an ability to employ machine learning for causal inference, an ability to lead big-picture data management practices, and an ability to design and implement data projects. Unsurprisingly, Netflix aims to hire candidates for this role who have more advanced qualifications — and will pay them more. While the range for the New York Times data analyst is $80,000-$95,000, the range for the Netflix data scientist is $150,00-$900,000, the (astounding) top of the range being reserved for PhDs with extreme experience and technical expertise.

How do these salaries stack up against the median salaries in the US? And what will these job markets look like over the next ten years? In the next section, we’ll break down the differences in the data for data analysts and data scientists.

How do salaries and job outlook differ for data analysts and data scientists?

Judging from just the differing skill sets and the New York Times and Netflix positions we explored in the prior section, you get the sense that data scientists stand to make more than data analysts, even if you adjust for the industries involved (print media and tech, respectively). Data from the US Bureau of Labor Statistics backs this up: data scientists make more on average than data analysts in the US. But regardless of which career you pursue, you can be confident that you’ll earn more than the average American and have more job opportunities.

In May 2021, the median annual pay for a worker in the US was $45,760, and the BLS expected 5% headcount growth across all occupations. Compare this with the data for data analysts and data scientists:

The BLS estimates the median data analyst salary in the US to be $82,360 annually. Over the next decade, the BLS expects 23% growth, or about 10,300 openings per year.

The BLS estimates the median data scientist salary in the US to be $100,910 annually. Over the next decade, the BLS expects 36% growth, or about 13,500 openings per year.

The data would suggest that if you are interested in working in the data economy, you should go into data science to maximize your earning potential — but it’s not always this easy. As you saw above, data scientists have a significantly more advanced skill set compared to data analysts. Accordingly, data scientists often require more education and experience. But what the BLS data doesn’t show is that there is a job pipeline from data analytics to data science.

As data analysts gain more experience, and potentially pursue more education through a data science course, master’s degree, bootcamp, or certificate program, they become viable candidates not only for data science positions, but for data engineer and data architect positions. If you’re not yet sure of your aptitude for a data profession, it can be beneficial to start by pursuing data analytics. Over time, you might be able to transition into data science. You’ll also have many of the skills necessary to work as a business analyst or a business intelligence analyst.

With enough training, in turn, data scientists can also apply for machine learning engineer positions or other jobs in artificial intelligence. For more on the career opportunities out there — and their average salaries — see our career path explainers:

What’s a typical data analyst career path?
What’s a typical data scientist career path?

How do the educational opportunities differ if you’re interested in data analytics or data science?

In this article, we’ve covered the top-line differences between data analysts and data scientists and dived into how their skill sets, day-to-days, and salaries and job outlooks differ. Which leaves one big question: if you are interested in data analytics or data science, how do you get started?

Luckily, for either discipline there are an abundance of options open to you: bachelor’s degrees, bootcamps, certificate programs, and master’s degrees. With so many options, however, it can be difficult to pick the right one. Here’s how they differ:

Data analytics and data science bachelor’s degrees

A bachelor’s degree in the US is typically an in-person undergraduate degree that allows students to focus on one area of study (their major) while gaining exposure to different areas of study through distributional requirements. While bachelor’s degrees in data analytics and data science exist and are viable options, students interested in ultimately going into these fields often major in related fields like applied mathematics, computer science, or even economics, finance, or business.

Most bachelor’s degrees are four years long, with tuition varying widely depending on the quality of the institution, its reputation, and whether it is private or state-funded. According to Educationdata.org, average in-state tuition at a public university is $102,828 over four years, while tuition at a private university averages $218,004.

If you’re interested in learning more about data analytics bachelor’s degrees, head to our guide to the best data analytics undergraduate colleges and universities.

If you’re interested in learning more about data science bachelor’s degrees, head to our undergraduate data science guide.

Data analytics and data science bootcamps

Data analytics and data science bootcamps are months-long, comprehensive educational programs focused on helping students land an entry-level job. Most bootcamps emphasize practical training as opposed to theory, culminating in a capstone project of some kind. They also offer extensive career services including mock job interviews and resume help.

Bootcamps are great options for someone who’s already earned a bachelor’s degree in another field or who simply doesn’t want to invest the time and money it takes to earn a four-year degree. In 2020, the average bootcamp cost $11,727. Since many bootcamps are online, they’re also good options for those who don’t want to relocate.

Head to our guides if you’re interested in pursuing a data analytics or data science bootcamp.

Data analytics and data science certificate programs

Data analytics and data science certificate programs are more flexible alternatives to bootcamps, often for those who might already possess some necessary skills for data analytics or data science. Offered online, certificate programs range in price from free to several thousand dollars. Many give students the opportunity to self-pace their own study through asynchronous modules. Upon completion, students receive a certificate that they can advertise on their resume or LinkedIn account.

To learn more, see our guides to data analytics and data science certificate programs.

Data analytics and data science master’s programs

Data analytics and data science master’s programs are generally two-year graduate degrees that can help a career transitioner land a junior data analyst or data scientist role or a career advancer gain promotion to a mid-level or senior role. Master’s degrees are also a go-to for those who already hold bachelor’s degrees in a different STEM field — or even the social sciences or humanities — who are willing to invest in leveling up their skills and getting a handle on the latest field-leading techniques and practices. According to Educationdata.org, the average master’s of science degree costs $61,200 in the US. Public universities, however, are more affordable, especially if you qualify for in-state tuition: around $29,150.

Additional cost savings are possible if you study online. Online programs offer the possibility to study at home, and often part-time, which can allow students to continue working while they improve their career prospects.

If you’re interested in pursuing a master’s degree, see our guides for data analytics and data science.