Data is everywhere.

No matter the industry, data is driving business strategy and economic growth. This is opening up new career paths for data-savvy experts in almost every field.

In a world of data-driven opportunity, which career is right for you?

Roles And Expectations

Data Analysts

Data Analysts know hindsight is 2020, and they use data to draw insights about what happened and present a story through visualizations.

Data analysts are:

  • Experts in their business and department performance, underpinned by basic data skills.
  • Specific to a single team or department - like Sales, Marketing, Customer Experience, etc.
  • Implements basic scripts and pipeline code, but typically not expected to develop software at the junior level.

Data Scientists

With Data Scientists, it's all about foresight and using raw data, statistics, and deep learning to make predictions and identify new opportunities.

Data scientists are:

  • Experts in data, supported by business domain experts to guide insights gained from patterns.
  • Work across multiple departments, or in dedicated data science teams with individual focus areas - like Applied Machine Learning, Marketing Optimization, Churn Prevention, etc.
  • Reports to a C-suite executive or senior data scientist.
  • Expected to develop tools or software to serve predictions, analytics, or insights for internal or customer-facing use.

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Tools and Workflows

Data Analysts

  • Works with popular pre-packaged BI and Analyst tools such as Tableau, Periscope, Salesforce/Gainsight, Excel, Metabase, but typically also have basic experience using statistical and scripting languages such as R and Python.
  • Juniors work within well-defined processes and workflows, often developed by Seniors.
  • Workflows include data and report generation pipelines.
  • Expected to keep up with developments in business intelligence tools and reporting methodologies.

Data Scientists

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

Qualifications and Job Markets

Data Analysts

  • Bachelor's Degree in a quantitative or business-related field. Senior roles require a Master's degree or several years of experience.
  • Typically found in industries which collect and maintain large amounts of data. SAAS, healthcare, retail, government, etc.
  • Found in medium- to large-scale companies with established DA departments or actively pivoting to data.
  • Continue to advance the, skills in statistics, machine learning, and software development to advance to a DS role.

Data Scientists

  • Post-graduate degree in a STEM discipline. Senior roles generally require a PhD or several years of data science experience.
  • Found in traditional engineering or software companies who are pivoting to data-centric products and services.
  • Require a higher level of data maturity, skills, and expertise, resulting in fewer positions and higher salary expectations.
  • High-tech startups are hiring data scientists in the dual role of Data Analyst and developer of Al and ML technologies.

Are you an analyst or a scientist?

Data Analysts vs. Data Scientists