Data scientist vs data analyst: Differences of the roles and job requirements

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This article was co-authored by Kenneth Koh.

Data is at the forefront of businesses and industries today, and organizations around the world are looking at harnessing the potential of the data at their disposal to drive revenue and profitability, improve operational productivity, and enhance customer satisfaction.

So, who does all these things? Data scientists and analysts, of course. In this article, I’ll give you a brief overview of the differences of these roles and some things you’ll need to know for the job.

Data scientist

Before answering the question of what a data scientist is, let’s first answer what data science is.

In short, data science is a process of analyzing data using creative ways (such as data inference) and using algorithm development technologies to find solutions to complex issues.

These involve pulling apart and putting together data sets to reveal hidden patterns such as consumer habits and preferences. It may also be simply figuring out the sales trends of a specific line of product.

Amazon, for example, mines user data patterns to determine the suggested products for each user. Doing this requires a combination of statistical expertise, programming, and business knowledge.

Statistics lies at the heart of data science. The field requires someone with certain quantitative capabilities to figure out complex trends within a data set that may consist of more than 1 million rows.

Programming skills, on the other hand, work together with statistics. For statistical analysis to happen, you need someone well-versed in programming languages (such as Java, SQL, and Python) to break down the data set in more readable formats.

Finally, business knowledge ensures that you’re solving problems that are consistent with the organization’s goals.

At the end of it, a “data product” such as Amazon’s recommendation system may be developed.

Data analyst

The role of a data analyst is similar to a data scientist in surprisingly many ways. They also analyze data and derive key insights from it. The main difference is that data scientists comes into play when an organization’s data volume exceeds a certain scale, which requires the creation of data products to help analyze it.

So while this means that data analysts also do data science work, they are not required to know much about programming. But data analysts must still have knowledge in statistics and business operations.

Comparison

Job requirements