Data Analyst vs. Data Scientist: What’s The Difference?

By Chris Kolmar - Nov. 6, 2020

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Companies in every industry rely on data to make decisions. Whether it’s perfecting a marketing campaign or redesigning the company’s processes, executives and managers want to have facts to back up their decision making.

Now more than ever, data is available on almost any aspect of business, making it possible to make these data-driven decisions in nearly every area. Most executives don’t know how or have time to collect and accurately interpret the data, though, so they hire data analysts and data scientists to do it for them.

The need for people to fill these roles is only going to keep growing in the future, making them good choices for your career path if you’re interested in numbers, statistics, programming, and business.

This article will talk about what data analysts and data scientists do and discuss the differences between them so that you can make an informed decision about your future.

What Is a Data Analyst?

Data analysts examine data to find trends and other insights that can be used for the good of the company. They then present these findings to others in the company in easy-to-understand ways.

What Is a Data Scientist?

Data scientists do the same thing, but they also create programs that collect, interpret, and use the data. These include data modeling and algorithms, which require more coding skills than data analysts typically need.

Both data analysts and data scientists handle data of all kinds, but they do so in different ways.

What They Do

Data analysts are the people you go to for reports, charts, and visualizations. They know how to go through the organization’s data to find pieces of information that can shed light on complicated problems or simply create reports that highlight different aspects of the company and the results of their efforts.

Data scientists are, in a nutshell, data analysts with more computer programming knowledge. In addition to finding insights from the company’s data, they are the ones who create the processes to collect that data and then take it a step further and develop models and algorithms that use that data.

Because the two careers are so closely linked, many data scientists start as data analysts and then either build their experience or go back to school to become data scientists.

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Role Requirements

Both data analysts and scientists must have experience in statistics, programming, and communications. Some requirements differ between the two roles, though.

  • Data Analysts

    Data analysts are usually only required to have an undergraduate degree, typically in a field such as engineering, mathematics, statistics, or business analytics.

    Whatever their degree, data analysts need to have skills in these areas:

    • Analytical thinking

    • Programming

    • Statistics and math

    • Data mining

    • Data warehousing

    • Machine learning

    • Databases

    • Data frameworks

    • Modeling

    • SQL

    • SAS

    • Tableau and data visualization

    • Communication (written and verbal)

    • Microsoft Excel and Office

    Many companies will also be looking for candidates with a good amount of work experience and an understanding of business principles, so be sure to look for internships and other opportunities to get some experience under your belt in the industry you want to work in.

  • Data Scientists

    While most data analysts don’t need advanced degrees, data scientists are usually required to have them.

    Most have a master’s degree, but almost half have doctorate degrees as well. These are often in fields such as mathematics, statistics, computer science, and engineering, and many institutions now offer specific programs for those wanting to enter the field of data science.

    In addition to a degree, here are some skills that data scientists need to have:

    • Data mining

    • Data warehousing

    • Data architecture

    • Advanced statistics

    • Data modeling

    • Computer science

    • Data visualization programs such as Tableau, Periscope, Business Objects, D3, and ggplot

    • Computer programming languages such as Python, R, Java, Scala, SQL, Matlab, and Pig

    • Machine learning

    • Third-party data providers such as Google Analytics, Site Catalyst, Coremetrics, AdWords, Crimson Hexagon, and Facebook Insights

    • Communication (written and verbal)

    The ideal data scientist is skilled in statistics and computer programming, as well as in communication. After all, collecting and interpreting the data has no value if you can’t effectively communicate your findings to the people who can put them to good use.

    This is why communication and data visualization skills are so critical for data scientists to have, and this unique combination of abilities makes them valuable employees.

Data Scientist vs. Data Analyst: Role Responsibilities

Like any job, data analysts’ and scientists’ roles differ based on the companies and industries where they work. There are some general responsibilities that each one typically has, however.

  • Data Analyst

    Data analysts are aptly named because their primary responsibilities always require some level of analyzing and interpreting data.

    Here are some examples of their responsibilities and how they impact the organization as a whole:

    1. Analyzing customer data to help determine target demographics. Being able to collect and analyze data about the organization’s customers can help them understand who their customers are on a more specific level.

      This will inform marketing strategies and product development so that the company produces content and products that are the best fit for the customers, resulting in increased revenue.

    2. Running data reports for different departments can further their goals. As a data analyst, you’ll likely be regularly asked to pull reports from your databases for various departments within your organization.

      Creating clear reports with all the data that could impact the issue at hand is vital to these departments’ decision-making processes. It also allows them to see if their efforts both in and outside of the organization are paying off or need to be revisited.

    3. Creating and improving dashboards allows employees and stakeholders to access important data more quickly. Many data analysts are also in charge of providing financial reports and building dashboards that enable other members of the organization to see the data they’ve collected.

      This is vital to the company’s efficiency and transparency, as allowing as many people as possible to see the data coming from the organization will increase accountability. It will also allow departments to use this data in new and innovative ways more easily.

  • Data Scientist

    Data scientists do everything that data analysts do, except they also run the collection of the data they’re interpreting and then put those interpretations to work. Many of these will require interdepartmental efforts. Here are some examples of what their work can do:

    1. Predictive models may inform marketing and user experience strategies. Using past data from customer behavior and advertising campaigns, data scientists can program predictive models that the marketing department can use to decide which strategies will work best for the future.

      This results in better use of money and increased revenue from customers having a more effective experience with your organization.

    2. A/B testing allows designers to choose the best option. Data scientists can design and run experiments that allow them to determine which of several options is best. For example, on a web page, they can show half of their active customers one design and half another and then look at the data to determine how each one performed.

      This allows web designers to make informed choices that will increase engagement rates and hopefully increase sales.

    3. Creating and monitoring data gathering systems will provide new insights. Another one of data scientists’ responsibilities is to find and build new methods for collecting data and making sure they’re running smoothly.

      This may sound mundane, but it’s vital to any organization’s success, as innovations in this area can significantly boost its success levels.

How Much Do They Earn?

For any job in any industry, your salary will depend on your location, your experience, and your education.

Because of this, data scientists usually earn more to begin with than data analysts do. More experienced analysts, however, may earn more than entry-level data scientists.

Data analysts will still make a good living, though, with their average salary sitting a little over $60,000 a year. The average data scientist will earn over $110,000 a year, and salaries for both positions will only increase as they gain experience.

Since nearly every industry has data-based positions, your salary as a data analyst or scientist will also change depending on where you work.

The Major Differences Between Data Analysts and Data Scientists

To sum up and to help you get a better understanding of data analysts and data scientists, here are the basics of the most significant differences between them:

  1. Data analysts typically don’t need an advanced degree, while data scientists do. Having an advanced degree as a data analyst will always help you, though, and some companies may even prefer it.

  2. Data analysts generally focus on managing and analyzing data, while data scientists also focus on computer programming. Many people think of data scientists as being in charge of the entire process of collecting, interpreting, and using the data by programming algorithms and predictive models, while data analysts simply interpret and use the data already collected.

    Because of this, data scientists need a strong background in computer programming in addition to their skills in statistics and data management.

  3. Data scientists usually get paid more than data analysts. This is because data scientists have more responsibilities and typically have a higher education level than data analysts.

    This is also one reason why many data analysts eventually work to move up to becoming data scientists.

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Chris Kolmar


Chris Kolmar

Chris Kolmar is a co-founder of Zippia and the editor-in-chief of the Zippia career advice blog. He has hired over 50 people in his career, been hired five times, and wants to help you land your next job. His research has been featured on the New York Times, Thrillist, VOX, The Atlantic, and a host of local news. More recently, he's been quoted on USA Today, BusinessInsider, and CNBC.

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