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Have you ever wondered how big tech companies such as Alphabet, Twitter and Meta can let you use their platforms for free yet still manage to generate enormous revenues every year? Data!!!

Since long before the invention of the internet, data has been driving business strategy and economic growth across industries. The advent of the internet and the increased ease of collecting and processing significant amounts of data has made it even easier. This increased integration of data-driven approaches constantly creates new career opportunities for data-savvy experts.

Regardless of industry, data science professionals generally follow two career paths. After completing your education, you can pursue a career as a data analyst or a data scientist. While these might often be referred to with other job titles, such as business analyst and data engineer, the data field is generally broadly divided along these career paths.

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


Key Differences Between These Data Science Careers

Though both these professionals analyze data to acquire insights and ultimately understand reality, their angles toward their work are slightly different. Data analysts can be described as using data to better understand the past. In contrast, data scientists use data to make assumptions about the future. Data analysts examine data sets to draw insights about things that have happened and present coherent stories through visualizations. Data scientists use 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.
  • Usually assigned 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.

Interested in becoming a Data Analyst? Read our feature: HOW TO BECOME A DATA ANALYST.


The Tools and Workflows Data Analysts and Data Scientists Use

Though both types of professionals work with data, their tools and workflows can be different. Because of the more advanced responsibilities of data scientists, they tend to utilize more complex 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. 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 senior analysts.
  • Their workflows include data and report generation pipelines.
  • They are expected to keep up with new 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 include data and reporting pipelines, 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

  • They are typically found in industries that collect and maintain large amounts of data, like SaaS, healthcare, retail, and government.
  • They usually work within medium to large enterprises with established or up-and-coming data departments.
  • Continue to develop their statistics, machine learning, and software development skills to advance to a data scientist role.

Data Scientists

  • Tend to be found in engineering or software companies pivoting to data-centric products and services.
  • They usually have higher 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.

Ready to jump in? We’ll be launching our newest Data Analytics Bootcamp in 2023. Sign up to be the first to know all the details.