Data Scientist vs Data Analyst: Understanding the Differences

Are you looking to become a data science expert but don't know how you can distinguish yourself from the rest of the pack? Today, many data-driven companies are looking to hire either data scientists or data analysts. There are many data scientist jobs and data analyst jobs.

While the job responsibilities of both positions are relatively similar, they often have slight nuances. In this post, we'll explain the differences between a data scientist and a data analyst.

Data scientist vs Data Analyst

We'll also discuss any overlaps or benefits of the two positions. We'll help you determine whether you should pursue a data science career as a data scientist or data analyst, and help you learn what these roles entail.

What is a Data Scientist?

If you ever watched the TV show Star Trek, you might recall that the Enterprise's starship captain, Jean-Luc Picard, was renowned for his encyclopedic knowledge of many things, including history, languages, and technology.

On the show, Picard's knowledge served as a valuable resource in areas the crew was lacking, such as navigation, or in helping him understand the motivations of other cultures.

In the real world, it might be the same thing as data science, a discipline that seeks to utilize Big Data to inform decisions.

Data scientists are professionals who use data to answer questions and solve problems. They can use data science to create algorithms and insights to solve business challenges.

Data scientists are involved in many different roles. Some Data Scientists work on model development, while others focus on deployment and scaling. Another popular area of focus is data engineering.

Regardless of the specific role, they all have one thing in common: They use data to find solutions and make better decisions.

Data scientist

What is a Data Analyst?

A data analyst is a person who conducts data-related tasks including data collection, performing data analysis, data modeling, data cleaning, the transformation of data, and other related tasks using different data frameworks. Data analysts typically have bachelor's degrees in computer science, mathematics, or statistics.

A data analyst role may require working on projects for companies or non-profit organizations that require them to perform analysis of large sets of data and they spend most of their time analyzing data using different data visualization tools. 

Data analysts typically work in teams with other types of data scientists such as software engineers to develop new software tools or models that can be used to analyze the data.

In some cases, data analysts may also be responsible for collecting and cleaning the raw data that they will use in their analyses.

Data analysts usually work regular hours outside of office hours and may work from home or office space if they choose to work independently rather than as part of a team.

Data Analyst

Roles and Responsibilities

Data Scientists and Data Analysts have different roles and responsibilities depending on their skills and the kind of company they are working at. The field of Data Science and machine learning is so vast that different tasks are to be performed by professionals. 

Common ‘Data Scientist’ roles include but are not limited to

  • Data Science Managers
  • Data Scientists
  • Decision Scientists
  • Data Engineers
  • Big Data Architects

Through a combination of data mining techniques as well as the use of statistical and machine learning techniques for segmentation and prediction, Data Science Managers are responsible for driving the analytics roadmap and providing descriptive and predictive solutions to the marketing and product management team. 

They work together with internal stakeholders and cross-functional teams to find solutions, improve operational effectiveness, and meet strict organizational standards. They oversee a group of data scientists and are crucial to making strategic choices.

The Data Scientist role involves drawing actionable insights based on the model predictions. They do data modeling and build statistical models to predict or forecast the future. Many Data Scientists tend to make decisions based on the model outcomes.

Decision scientists combine the existing data with other sources and use it to analyze data. They make decisions based on these findings.

Data Engineers query databases and do data mining. By doing data mining they collect unstructured data, and raw data and prepare data for prescriptive and predictive modeling.

The Big Data Architects are responsible for the full life cycle of a Hadoop system. They do platform selection, requirement research, and technical architectural design.

Common ‘Data Analyst’ roles include but are not limited to

  • Statistician
  • Database Administrator
  • Data Analyst

Statisticians generally spend most of their time analyzing data and interpreting the data. They do data visualization using various data visualization tools. They communicate to stakeholders and help in shaping the business strategy.

Database Administrators do database management. They develop and maintain the data collection systems. They also help in database design and development.

Data Analysts, unlike Data Scientists, spend most of their time identifying, analyzing, and interpreting the trends in the data sets. They address the business problem using the data insights they derive from the given data points.

These are some of the industry's roles and responsibilities performed by various Data Scientists and data analysts.

Skills and Tools Requirements

The skills and tools required to do these jobs vary. For example, Data Scientists must be well versed in machine learning algorithms and specific data science domains like Natural Language Processing(NLP), etc., to solve domain-specific problems.


Data science expert


Data Scientists need to have machine learning skills to model the given data set correctly. They also need to have database query skills like SQL etc., to pull the data from the sources.

Data Scientists mainly use tools like open-source libraries, Cloud platforms like GCP, and AWS, to train their models. Most Data Scientists are well versed with mathematical concepts like calculus, linear algebra, probability, and statistics.

On the other hand, Data Analyst skills include knowledge of dashboarding and reporting tools like MS-Excel, Tableau, PowerBI, etc., to convey the insights from the dataset. To do data analysis with different data sets that comprise both structured and unstructured data, a data analyst needs to have good knowledge of Exploratory Data Analysis. 

Exploratory Data Analysis (EDA) is a method for data analysis that employs visual methods. Using statistical summaries and graphical representations, it is used to identify trends, and patterns, or to test hypotheses.

A Data Analyst, unlike a Data Scientist, is well versed in basic probability and statistics. Data Analysts also have basic fluency in Python and R programming languages. Thus, a Data Analyst role requires individuals to be less robust in the domain of programming but well-versed in statistical and visualization tools.

Data Analyst Expert

Salary Comparison

The salaries for both the job roles vary a lot. As per Glassdoor, the average base pay for the data scientist role is $117,212 per year. Whereas for the Data Analyst role, the average base pay is $69,517 per year.

There is a huge gap in the salaries of the two job roles. This is mainly due to the skillsets that vary as per the job. A Data Scientist's job requires good objected oriented programming skills, model development, and advanced mathematical and statistical knowledge like calculus, linear algebra, probability, and statistics. Whereas, for a Data Analyst job one doesn’t need to have the kind of skills that a Data Scientist possesses.

Due to this skill gap, the intensity of the work, and the value a Data Scientist adds to the business by creating predictive models, a Data Scientist earns more than a Data Analyst. 

With the increase in experience salary also increases for either Data Scientist or Data Analyst. Also, salary varies as per the size of the company and the industry that the company serves. As per the survey by O’Reilly, Data Science professionals in the computer industry are earning more salaries than in other industries. 

Though both Data Scientist and Data Analyst jobs are in demand, the salary for a Data Scientist job seems to be higher than the Data Analyst job.

Career Prospects and Career Growth

Currently, there is a huge demand for Data Science professionals. But there is a shortage of qualified Data Science professionals in the market. This indicates a high demand for Data Scientists and Data Analysts.

The Covid-19 pandemic, which has expedited businesses' digital transformation across industries and nations, is a significant contributor to the increase in demand for Data Science professionals. 

As a result, all businesses today have an online presence and require the assistance of a qualified data specialist who can assist them in storing and processing their data in order to make informed decisions. 

With growth on pace, both data scientists and data analysts with the necessary skills and abilities can are expected to be netting six figures in the upcoming years. So we can’t really distinguish between a data scientist and a data analyst in terms of career prospects and career growth as both these jobs will be in demand.

Which is better: Data Scientist or Data Analyst?

Both Data Scientist and Data Analyst jobs are in demand. They have good career prospects. But one can’t do both jobs at the same time and one must choose a role. As we saw earlier in the above sections, the Data Scientist's job requires foundations in advanced mathematics like calculus, probability, and statistics. 

So if you are good at math, and programming and have a deep interest in building machine learning models, then Data Scientist is better for you.

If you are more interested in numbers, visualizations, making dashboards with various tools like MS-Excel, Tableau, etc., and making a career in the analytics field,  then the Data Analyst role should be better.

Who gets paid more: Data Scientist or Data Analyst?

The average salary of a Data Scientist is more than the average salary of a Data Analyst. This is due to the different nature of the two jobs which require a unique skill set for each role. The Data Scientist role is one of the highest paying roles in the industry. 

Due to the increased demand for qualified professionals, the salary seems to be going high. Nevertheless, Data Analyst salaries are also decent enough to be considered good.

Can a Data Analyst become a Data Scientist?

The Data Scientists role requires more skills than the skills of a Data Analyst role. So a Data Analyst can become a Data Scientist by upskilling through an advanced degree or through a good certification course. 

An advanced degree will always be a better option to improve technical skills like predictive analytics, and artificial intelligence.

Which is easier: Data Analyst or Data Scientist?

Data Scientists need to have good programming skills and strong mathematical foundations. Also, Data scientists possess the skills of a Data Analyst already. Hence, Data Analyst is easier than Data Scientist as it doesn’t require much background in mathematics and programming.


Data Scientist and Data Analyst are lucrative career options. Both are interesting and they require their own skill sets for the job. But they are subtle differences due to which the salary and career growth vary. The below table summarizes the difference between the two:

Data Scientist

Data Analyst

Builds predictive models and draws insights using the predictions.

Uses data visualization tools to make visualizations and insights from the data.

Takes part in a variety of data operations.

Mainly involved in data cleaning, transforming, and generating inferences from the data.

Takes care of both structured and unstructured data.

Usually works with structured data.

Proficient in advanced mathematical concepts like calculus, linear algebra, probability, and statistics.

Possess problem-solving skills and basic statistics.

Well-versed in tools like Python, Tensorflow, and Spark. 

Well-versed in tools like MS-Excel, Tableau, Looker, Power BI, etc.

Big Data tools such as Hadoop, Sparks, Databricks

Analytical tools such as DBT, SAS


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