Top 5 Ways To Distinguish Data Science From Data Analytics

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Tom Merritt explains the differences between the two job titles and what you can expect from each.

Data science is big business and not only important but useful for business. But data science and data analytics have different meanings depending on the business or the situation. Do you have trouble distinguishing them? While definitions may vary, here are five things to help distinguish data science from data analytics.

  1. A data scientist usually makes models. Data scientists are developing algorithms to help make predictions about things. Sometimes it’s about specific things; sometimes it’s a more general type of thing. But in any case, these are more unknowns.
  2. Data analysts use models. Data analysts often answer a specific question about a business need. A data analyst knows which algorithm might be the best to elicit an answer.
  3. Data scientists code. Many. They use SQL, Python, Spark, Hadoop and others to manage big data on big platforms like AWS and Databricks.
  4. Data analysts primarily manage databases. I mean they will also be using SQL and Python, but also Excel and SAS. Data analysts extract, store and manage data.
  5. They can both answer your business questions. But a data analyst will act more like a consultant, performing A / B testing and identifying information needs. A data scientist, meanwhile, will take a huge mountain of unstructured data and make it meaningful to you. They can even answer questions you didn’t know you had.

There’s a fine line between the two, and you probably need a bit of both for your business. But it can help you understand the difference in the way people use the terms.

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