Data scientist vs data analyst: The major difference between the two

Decades ago, the world of analytics was nascent, and the designations were simple. You had to analyse data and find insights to help decision-making. The tools for this work evolved from manual analysis to spreadsheets, to statistical software to more advanced technologies. New trends such as artificial intelligence (AI) and machine learning (ML) came in, and so did new designations.

Today, people looking at a career in the world of data have a common question: how do you differentiate between a data scientist and a data analyst?

The skills for both are similar, says Simplilearn, an online learning platform. While the role of a data analyst has been around for longer, data science started when the data boom led to the use of new technologies to manage, interpret and analyse such large data sets to make better decisions, according to Simplilearn. The work involves understanding business problems, looking at various sources of data to assess patterns, finding the best way to look at insights and then distilling them for outcomes.


The learning platform says data scientists are primarily problem solvers. The kind of tools they need to work on are vast — Tableau, Python, Hive, Impala, PySpark, Excel, Hadoop, etc — to develop and test new algorithms. Data scientists look at both structured and unstructured data, and build AI and machine learning models.

Meanwhile, the responsibilities for data analysts range from gathering data across databases, building complex queries using SQL and other tools, spotting trends and creating reports.

In terms of qualifications, engineering, information technology, and degrees in maths, statistics and economics are important for both, along with the right domain knowledge.

Where the skills differ

Data Analyst Skills	Data Scientist Skills
Good understanding of statistics and probability	A strong foundation of calculus, linear algebra, statistics and probability
Knowledge of Python and SQL	Well-versed in Python, SQL, R, SAS, MATLAB, Spark.
Analysing data with MS Excel and creating reports using Tableau	Data visualisation using Power BI and storytelling using Tableau
Data wrangling	Data wrangling and data modelling
Exploratory data analysis	Machine learning and cloud computing

Source: Simplilearn

According to Manav Das, who leads the AI and analytics leadership hiring practice at Fidius Advisory, a data scientist develops statistical and machine learning models that help a business solve a complex problem. A data analyst, on the other hand, is focused on collecting, cleaning and organising data.

Data scientists need to have a deep understanding of programming and ML. As the majority of the tools they use are programming languages such as Python and R. Data analysts make use of Excel, SQL or Tableau or PowerBI for visualisation and analysis of the data.

“We mostly see data scientists working with a large amount of unstructured data and developing custom algorithms using it. Data scientists are closer to engineering teams. Data analysts are expected to work on structured tasks related to data such as creating regular business reports and conducting data queries. They help in business problem identification and presentation; they work closely with the business stakeholders,” he says.

In the current market condition, opportunities for data scientists are higher than those for data analysts, says Das. He adds that a data scientist gets paid more than a data analyst with the same years of experience.