Data analyst vs data scientist

The differences between data analysts and data scientists can be seen in a few details. Each job has different responsibilities and duties. It typically takes 2-4 years to become both a data analyst and a data scientist. Additionally, a data scientist has an average salary of $106,104, which is higher than the $74,342 average annual salary of a data analyst.

The top three skills for a data analyst include data analysis, python and power bi. The most important skills for a data scientist are python, data science, and visualization.

Data analyst vs data scientist overview

	Data Analyst	Data Scientist
Yearly salary	$74,342	$106,104
Hourly rate	$35.74	$51.01
Growth rate	11%	16%
Number of jobs	167,520	106,973
Job satisfaction	-	-
Most common degree	Bachelor's Degree, 65%	Bachelor's Degree, 51%
Average age	44	41
Years of experience	4	4

What does a data analyst do?

Data analysts are responsible for interpreting the company's statistics and providing sound recommendations to the organization. They manage the organization's data sets, usually related to market performance, finance, or human resources. They are in charge of studying the available data, spotting trends, interpreting what the data and the trends mean, and recommending suggestions that will help the organization perform better. Their recommendations should also be relevant and backed up with strong analyses. Data analysts are expected to have a good grasp of the current market trends in the industry.

What does a data scientist do?

A Data Scientist analyzes information from multiple sources in order to gain maximum insight that can give the company a competitive advantage. They work in different domains, including manufacturing, healthcare, education, and finance.

Data analyst vs data scientist salary

Data analysts and data scientists have different pay scales, as shown below.

	Data Analyst	Data Scientist
Average salary	$74,342	$106,104
Salary range	Between $53,000 And $103,000	Between $75,000 And $148,000
Highest paying City	Richmond, CA	Richmond, CA
Highest paying state	New Jersey	California
Best paying company	The Citadel	The Citadel
Best paying industry	Finance	Start-up

Differences between data analyst and data scientist education

There are a few differences between a data analyst and a data scientist in terms of educational background:

	Data Analyst	Data Scientist
Most common degree	Bachelor's Degree, 65%	Bachelor's Degree, 51%
Most common major	Business	Computer Science
Most common college	Northwestern University	Columbia University in the City of New York

Data analyst vs data scientist demographics

Here are the differences between data analysts' and data scientists' demographics:

	Data Analyst	Data Scientist
Average age	44	41
Gender ratio	Male, 50.2% Female, 49.8%	Male, 79.6% Female, 20.4%
Race ratio	Black or African American, 7.5% Unknown, 4.4% Hispanic or Latino, 8.5% Asian, 14.5% White, 64.9% American Indian and Alaska Native, 0.2%	Black or African American, 4.2% Unknown, 5.4% Hispanic or Latino, 6.9% Asian, 18.8% White, 64.2% American Indian and Alaska Native, 0.6%
LGBT Percentage	12%	9%

Differences between data analyst and data scientist duties and responsibilities

Data analyst example responsibilities.

Manage patient s data in UNIX system.
Manage loan performance in Hyperion Financials using SQL and PL/SQL tools.
Manage and contribute to EDI processes ensuring HIPAA compliance and access to clearing house review on individual case status.
Develop Java application to automate reformatting of text files for an internal application.
Execute deterministic modeling and Monte Carlo simulations in MATLAB to achieve greater mathematical confidence in results obtain from analysis.
Manage weapon system project websites via SharePoint, including troubleshooting technical issues and developing guidelines for public/private information and user permissions.

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Data scientist example responsibilities.

Update, maintain, and manage regional CRM database and records for customers, vendors, and suppliers.
Configure and manage JobScope ERP system for a make-to-order/make-to-stock design and manufacturing environment.
Lead the analysis in SAS for data integration of mortality data using meta-analysis integration methods.
Implement a proximal stochastic gradient descent with a line search to fit a regularize logistic regression in Scala
Perform cross-validation-test on linear regression model of data using scikit-learn.
Develop python base statistical visualization to provide insights of fuzzy social media data.

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Data analyst vs data scientist skills

Common data analyst skills

Data Analysis, 10%
Python, 7%
Power Bi, 6%
Data Management, 6%
Visualization, 5%
Data Quality, 4%

Common data scientist skills

Python, 13%
Data Science, 10%
Visualization, 5%
Java, 4%
Hadoop, 4%
Tableau, 3%

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Updated January 8, 2025

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