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๐Ÿ“ŠData Analyst Vs Data Science: What They Do Actually ๐Ÿคทโ€โ™€๏ธ

Updated: Apr 14, 2020

How many times you've found yourself explaining or trying to distinguish between those 2 job titles to someone in your company/family/close friends?

Well...I bet it happened many times!๐Ÿ™„

After a lot of conversations with my friends from the industry, I realised it's time to write a post about it!

Honestly, when I landed the first position as a data analyst I didnโ€™t know what it actually means. A lot of companies define the roles differently, what makes it even harder to understand.

So, what does a Data Analyst do thatโ€™s different from what a Data Scientist does?

A Data Analyst deals with many of the same activities, but the leadership component is a bit different.

Letโ€™s start at the beginning, Who owns what? ๐Ÿ‘ฉโ€๐Ÿ’ป


The following might change a lot between companies and teams, I'm sharing only form my personal perspective:

1. Data Scientist will investigate new features or products by unlocking the value of data. Data Analyst develops convention SQL queries to find answers to complex business questions, and will develop dashboards/reporting tools.

2. Data Scientist clean and organize data for analysis (e.g prediction models etc). Data Analyst analyzes data to identify correlations and discover patterns from various data points. In some cases, some cleanup will be done only by the analyst / data engineer and the data science will use the tables post clean up.

3. Data Scientist develops new analytical methods and machine learning models. Data Analyst identifies any data quality issues and partialities in data acquisition.

4. Data Scientist conducts causality experiments by applying A/B experiments to identify the root issues of an observed result. Data Analyst Implements new metrics for finding out formerly not so understood parts of the business (but in some companies, product analysts would also run A/B test).

5. Data Scientist owner of data storytelling and visualization usually through Python. Data Analyst design and create data reports using various reporting tools such as Tableau, Power BI, etc. In order to help business executives make better decisions.

๐Ÿค“ Summary

Both roles are expected to develops queries (in SQL, or actually to write code in Python), to work with engineering teams to source the right data, perform data munging and derive information from data. However, the day-to-day job, looks a lot different in those roles, also, that what made shifting from one role to another quite common.

Hope, it was useful ๐Ÿ’โ€โ™€๏ธ!

What's your take on both roles? Is it the same in your company?

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1 Comment

Unknown member
Feb 19

I have to disagree a bit. You talk mostly about a small part of the DS work (called EDA - exploratory data analysis) which overlaps with DA's work. DS are between DA and SW since we develop an ML code which runs in production. 99% of the work is coding (in Python mainly) and a lot of DS actually don't know SQL because they have no use of it in their role. It varies by company and role, but DS is not that close to DA in my opinion and experience.

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