Understanding the Difference Between Data Analyst and Data Scientist

by June 18, 2020

Data Scientist

Does an organization need to have both a Data Analyst and Data Scientist, and why?

Data is the new gold! With the world rapidly moving towards digital transformation, data is the asset that forms the backbone for this change. From Artificial intelligence to Big data, IoT devices, data is required to push for further innovation. Though data flows in almost every industry, it is not of any value until enriched and brought into focus.

This is where data analytics comes into the picture, where we employ analysis tools to collect data, analyze it, and gain a more in-depth, rounded insight and understanding of its potential value. These insights help to improve business performance, make better-informed decisions, and eventually transforms the way businesses are carried out. However, not all work is carried out by analysts. We also have data scientists to help us in data modeling via designing algorithms, prototypes, and predictive models. Despite the two being interconnected, they provide different results and pursue different approaches; often, people confuse data analysts with data scientists and vice versa. Let us try to understand the difference between them by understanding what data science and data analysis actually are.


What are they?

Data Science is more like an umbrella term. This branch includes several scientific methods, math, statistics, and other tools to analyze and manipulate data, on a variety of models and raw data to get information. The main objective is to ask questions and locate potential avenues of study, with less concern for specific answers and more emphasis placed on finding the right question to ask. Data scientists accomplish this by predicting potential trends, exploring disparate and disconnected data sources, and finding better ways to analyze information. By using a combination of programming, statistical skills, Machine Learning algorithms, data scientists can excavate through large amounts of structured and unstructured data to identify patterns. This can help an organization curb costs, boost efficiencies, identify new market opportunities, and propel the organization’s competitive advantage.

Whereas, data analytics is about focusing on processing and performing mainly statistical analysis on existing datasets. The work of a data analyst is less in Artificial Intelligence, machine learning, and predictive modeling, and more with viewing historical data in context. Unlike data scientists, their work involves determining data requirements, figuring out the reasons why sales dropped, or create dashboards that support a business’s KPIs, and develop charts. Basically, data analysis is about solving issues and coming up with solutions for immediate outcomes. At times, organizations use the automated analysis tool to provide insights in certain areas. Data analysts work only on structure and processed data.


A difference of roles:

•  Data scientists’ job includes data mining by using APIs or building ETL pipelines, data cleaning using programming languages like R or Python. And data analysts perform data querying using SQL and analysis and forecasting using Excel or frameworks like Apache Hadoop and Spark.

•  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.

•  Data analysts need not have business acumen like data scientists.

•  Data analysts act on data that is localized or smaller in scale. But data scientists focus on data as per business needs, market requirement, and exploring more business from black data.

Other than these key differences, one needs to have substantive expertise in data science; data analysts don’t. The scope of data analysis is at the micro-level and data science on a macro level. Data analytics is beneficial in industries like healthcare, gaming, and travel, while data science is conventional in internet searches and digital advertising. Data analysts also need to have sound knowledge of BI tools and a medium level understanding of statistics. Overall, they are the same sides of a coin and are highly interconnected with each other. Therefore, organizations need to invest in both these fields to stay relevant in the market.