How to tell the difference between Data Analysts & Data Scientists

One of the best ways to drive revenue internally and externally to your business is understanding your data. It is thus, only common sense why so many organisations are on the hunt for data professionals to help them harness their data and become profitable.

But data professionals appear under many job titles and vary according to their skills and experience. It is very important for hiring managers to understand their needs and what type of data professionals they should be on the look-out for.

In this post, I will give a brief overview of Data Scientists and Analysts, the differences between these roles and key things you’ll need to know about their job description.

Data scientist

Before jumping onto what a data scientist does, it is important to have a clear understanding of what data science is.

In a nutshell, data science is used to solve complex business issues. This is done by analysing data using creative ways and algorithm development technologies. This complex procedure involves pulling apart and putting together data sets to reveal patterns such as consumer habits, preferences or sales trends of a specific line of product. Doing so, requires a combination of experience in statistics, programming and business. In more detail:

Statistics is the heart of data science. Quantitative capabilities are a must when trying figure out trends within a data set that may consist of more than 1 million rows.

Programming is a skill that ultimately works together with statistics. In order to have as a end product a statistical analysis, someone very comfortable with programming languages is required ( Java / SQL / Python) to break-down the data set in more readable formats.

Business knowledge ensures individuals understand the bigger picture of the problems they are asked to solve. That will enable them to be consistent and beneficial towards the organisation's goals.

Data analyst

It is common that data analysts are confused with data scientists, as these roles are similar in many ways.

Analysts, as well as scientists, derive key business insights by analysing data. The key difference to these two roles, is that data scientists are brought in organisations when their large data volume requires the creation of data products to help analyse it.

So while data analysts also conduct data science work, they are not required to know as much about programming. But data analysts must still have knowledge in statistics and business operations for they produce digestible high-end outputs such as reports or presentations.

Comparison

The differences between data scientists and data analysts may differ across different organisations and industries. Here is a basic comparison:

Data scientist: Required to create prototypes and develop data products to make intelligent decisions that drive the direction of businesses.

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Data analyst: May or may not be required to develop “data products.”

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Data scientist: Required to know a large number of programming languages to produce data products.

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Data analyst: May not be required to know a large number of programming languages. Their analytics operations may be operated on an excel spreadsheet.

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Data scientist: Only exists in organisations that require deciphering big data (i.e. large data sets) with programming languages.

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Data analyst: Exist in most organisations

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