Data analyst and a data scientist…What is the difference?

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Jennifer Agor, Assistant Director, Duke Career Center
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Both roles use data, coding, business acumen, and statistics to answer business related questions from the huge amount of data available. The major differences between the roles is:

  1.  The way they apply the skills they have

  2.  The extent of technical knowledge and education they possess

Data Analyst

  • Uses data available to find a solution to business problems presented to them by business leadership—identifying trends in data, developing charts, and creating visual presentations of data.
  • Data analysts are not expected to build statistical models or be well versed in programming and machine learning; therefore, they usually aren’t expected to have an advanced degree.
  • Data analysts work primarily in databases and tend to love numbers and statistics. Because of how they extract and review data, they need to have a good understanding of the industry in which they work. They work with a variety of team members with different levels of technical understanding, so they need to be able to communicate findings across teams.
  • Due to the smaller amount of technical knowledge and lower education requirements, data analysts tend to earn less than data scientists. Coding skills are usually basic, geared towards cleaning and normalizing data. According to Glassdoor, the average salary in the U.S. is around $60k.
  • For more information, see the Bureau of Labor Statistics entry on Data Architect on O*Net Online.

 

Data Scientist

  • Combines skills that include business acumen, customer/user insights, analytics, statistics, programming, machine learning, data visualization, and more. A data scientist is one who combines sound business understanding, data handling, programming, and data visualization skills to maximize business impact by working with data mining–making sense of it, building statistical models, and proving causality to answer questions which drive business forward.
  • Data scientists often construct new processes for data modeling and production. Because of this, they use more advanced skills to develop algorithms, predictive models and custom analysis; therefore, data science usually requires an advanced degree due to the reliance on machine learning and data mining skills.
  • Data science is much more mathematical and technical than data analysis. While data scientists need to be strong in stats, math and computer science, they also need to have a strong business acumen to determine the questions which need answers and understand how the data can inform them. Coding expertise is a necessity and they also need strong communication skills and the ability to put technical information into layman’s terms.
  • Because more education is required, and more advanced skills in coding are necessary, Data Scientists tend to earn more than analysts. Glassdoor estimates average salary at $110k.
01.31 data scientists https-::www.quora_.com:What-is-the-difference-between-a-data-analyst-and-a-data-scientist-1.png

 

Data analyst "Data Detective"

Source and content for image: https://www.quora.com/What-is-the-difference-between-a-data-analyst-and-a-data-scientist-1

 

Text from https://www.edureka.co/blog/difference-between-data-scientist-and-data-analyst/

 

Source and content for image: https://www.edureka.co/blog/difference-between-data-scientist-and-data-analyst/

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