Difference between Data Analyst and Data Scientist

Data Scientist vs. Data Analyst — Definition

“A data scientist is someone who can predict the future based on past patterns whereas a data analyst is someone who merely curates meaningful insights from data.”

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“A data scientist job roles involves estimating the unknown whilst a data analyst job roles involves looking at the known from new perspectives.”

“A data scientist is expected to generate their own questions while a data analyst finds answers to a given set of questions from data.”

“A data analyst addresses business problems but a data scientist not just addresses business problems but picks up those problems that will have the most business value once solved.”

“Data analysts are the one who do the day-to-day analysis stuff but data scientists have the what ifs.”

This is what Abraham Cabangbang, Senior Data Scientist at LinkedIn commented on the difference between data analyst and data scientist -

“It’s definitely a gray area. At my previous company I did both analyst and
scientist jobs and as an analyst we were more customer facing; the tasks we did were directly related to the tangible business needs — what the customers wanted/requested. It was very directed. The scientist role is a little more free form. The first thing I did as a data scientist is work on building out internal dashboards, basically surfacing information that we were tracking on the back end, but weren’t being used by the data analysts for any reasons; for example, we might have lacked the infrastructure to display it, or the data was just not very well processed. It really wasn’t anything tailored out from a customer need, but came from what I noticed the analyst team needed in order to do their job.”

There are several definitions doing rounds on the internet to differentiate the job role of a data analyst and a data scientist but they are inadequate as different organizations have different ways to define big data job roles. Most of the people think that data scientist is just a fancy word for a data analyst role, however, it is not so. Data analyst and data scientist are two hottest career tracks in the big data world. Let’s understand what the difference between data analyst and data scientist is and what differentiates the two hottest IT professions of 2017.

Data Analyst vs. Data Scientist — Differences

• The job role of a data scientist strong business acumen and data visualization skills to converts the insight into a business story whereas a data analyst is not expected to possess business acumen and advanced data visualization skills.
• Data scientist explores and examines data from multiple disconnected sources whereas a data analyst usually looks at data from a single source like the CRM system.
• A data analyst will solve the questions given by the business while a data scientist will formulate questions whose solutions are likely to benefit the business.
• In many scenarios, data analysts are not expected have hands-on machine learning experience or build statistical models but the core responsibility of a data scientist is to build statistical models and be well-versed with machine learning.
• Most Data Scientists / Analysts get productive on their projects by having access to a ready-to-use library of sample solved code snippets. Click here to get free access to 100+ Data Science code snippets.

Data Analyst vs. Data Scientist — Comparison

Data Analyst vs. Data Scientist- Skills

Data analyst and data scientist skills do overlap but there is a significant difference between the two. Both the job roles requires some basic math know-how, understanding of algorithms, good communication skills and knowledge of software engineering.

Data analysts are masters in SQL and use regular expression to slice and dice the data. With some level of scientific curiosity data analysts can tell a story from data. A data scientist on the other hand possess all the skills of a data analysts with strong foundation in modelling, analytics, math, statistics and computer science. What differentiates a data scientist from a data analyst is the strong acumen along with the ability to communicate the findings in the form of a story to both IT leaders and business stakeholders in such a way that it can influence the manner in which a company approaches a business challenge.