Top 5 ways to distinguish data science from data analysis


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

Data science is a big deal and not only important but helpful for businesses. But data science and data analysis have different meanings depending on the company or situation. Having trouble distinguishing them? While definitions may vary, here are five things to help distinguish data science from data analysis.

  1. A data scientist generally makes models. Data scientists develop algorithms to help make predictions about things. Sometimes it’s about specific things; sometimes it’s about a more general type of thing. But in any case it involves more unknowns. 
  2. Data analysts use models. Data analysts are often answering a specific question about a business need. A data analyst knows which algorithm might be the best to get at an answer.
  3. Data scientists code. A lot. They use SQL, Python, Spark, Hadoop and the like to manage big data on big platforms like AWS and Databricks.
  4. Data analysts manage mostly databases. I mean they’ll use SQL and Python too but also Excel and SAS. Data analysts mine, warehouse and manage data. 
  5. They can both answer your business questions. But a data analyst will act more like a consultant, doing A/B testing and identifying informational needs. A data scientist, on the other hand, will take a huge mountain of unstructured data and make sense of it for you. They may even answer questions you didn’t know you had.

There’s a fine line between the two, and you likely need a little of both for your business. But this can help you make sense of the difference in the way people use the terms. 

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