Difference Between a Data Analysts and Data Scientists

Data is driving business strategy and economic growth across almost all industries. The increased integration of data-driven approaches is constantly creating new career opportunities for data-savvy experts.

Regardless of industry, there are generally two career paths that data science professionals follow. After completing your education, you can pursue a career as a data analyst, or a career as a data scientist.

Though these roles overlap in several ways, there are a few key areas in which they differ. When setting your sights on your career goals, it’s important to know what kind of position is right for you. Read on to understand the key differences between these two roles.

Key Differences Between These Data Science Careers

Though both kinds of professionals analyze data in order to understand reality, the angles they take towards their work are slightly different. Data analysts can be said to examine data to better understand the past, whereas data scientists use data to make assumptions about the future.

Data analysts examine data sets with the intention of drawing insights about things that have happened, and presenting coherent stories through visualizations. Data scientists use things like raw data, statistics, and deep learning to create predictions and analyze opportunities.

Data analysts:

  • Utilize data science skills to become experts in the performance of specific businesses and departments.
  • Tend to be specific to a single team or department, like Sales, Marketing, or Customer Experience.
  • Implement basic scripts and pipeline code, but typically are not expected to develop software.

Data scientists:

  • Use data expertise to create guiding insights for businesses based on trends and patterns.
  • Work across multiple departments or in dedicated data science teams with individual focus areas, like Applied Machine Learning, Marketing Optimization, and Churn Prevention.
  • Typically report to a C-suite executive or senior data scientist.
  • Develop tools or software to serve predictions, analytics, or insights for internal or customer-facing use.

The Tools and Workflows Data Analysts and Data Scientists Use

Though both types of professionals work with data, the tools and workflows they use can be different. Because of the more advanced responsibilities of data scientists, they tend to utilize more complex kinds of tools, and work within more convoluted workflows.

Data Analysts:

  • Work with popular pre-packaged BI and Analyst tools such as Tableau, Periscope, Salesforce/Gainsight, Excel, and Metabase, but also have basic experience using statistical and scripting languages such as R and Python.
  • Junior analysts work within well-defined processes and workflows, often developed by seniors analysts.
  • Their workflows include data and report generation pipelines.
  • They are expected to keep up with developments in business intelligence tools and reporting methodologies.

Data Scientists:

  • Have intermediate to advanced SQL knowledge, and are familiar with popular database systems and cloud platforms.
  • Can be expected to implement custom ETL processes, and perform aspects of data engineering. Create their own processes and workflows, or improve existing ones continuously.
  • Workflows can include data and reporting pipelines, as well as machine learning, project management and software development workflows.
  • Are expected to work with a few business intelligence tools, but also must be able to code and develop parts of a tool, feature or software product when necessary.

Where These Pros Can Work Following Data Science Education

The typical day-to-day of these professionals can differ in many ways, from the types of industries they tend to work in, to how their careers progress over time.

Data Analysts

  • Are typically found in industries that collect and maintain large amounts of data, like SAAS, healthcare, retail, and government.
  • Can be found in medium to large enterprises with established or up and coming data departments.
  • Continue to develop their skills in statistics, machine learning, and software development to advance to a data scientist role.

Data Scientists

  • Tend to be found in engineering or software companies that are pivoting to data-centric products and services.
  • Have a higher level of data skills and expertise, resulting in more specialized roles and higher salary expectations.
  • High-tech startups are beginning to hire data scientists in dual roles of Data Analyst and AI/machine learning technology developer.

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Data Analysts vs. Data Scientists