Data scientist vs. data analyst: 3 main differences

Few tech jobs have been as hyped up in recent years as data scientists: With more companies collecting data to glean actionable insights and a competitive edge, data scientists were named the best job in America for the last four years. However, confusion remains around the difference between data scientists and another common big data role, according to a recent report from Indeed: Data analysts. 

Data scientists and data analysts have the same goals: Interpreting information by finding patterns and trends  that inform critical business decisions. But these professionals bring different skills, education, and levels of experience to their roles, impacting their demand and compensation, Indeed found. 

Here is a breakdown of the two roles, the salaries they command, and the skills they require, according to Indeed. 

What does a data analyst do?

Data analysts work with structured data that easily takes the form of a spreadsheet or database (for example, retail store purchase histories or medical records) to find insights that are not immediately obvious to the business side. These professionals then create reports, charts, and other visualizations to communicate their findings to management or other parts of the business and aid in decision-making. 

For example, Indeed noted, a data analyst working in the transportation industry might collect, process, and organize information from datasets like dispatch records or transportation databases to find patterns and make recommendations that improve the efficiency of bus services and save the company money. 

What does a data scientist do? 

Data scientists do similar work to data analysts, but on a higher scale. These professionals typically interpret larger, more complex datasets, that include both structured and unstructured data. Data scientists also design experiments to solve sophisticated problems with code, and build predictive models and machine learning algorithms. 

Data scientists also work to identify what questions need to be asked and answered with data based on business problems, with the goal of helping businesses make better decisions. 

Take the example of Spotify, Indeed noted. A data analyst at the company might focus on examining music listening patterns. But a data scientist might take terabytes of data and turn it into audience segmentation models to help engineers build personalized music recommendation engines, or examine user behavior and monetization research to create targeted ads.  

What skills do you need to become a data scientist or data analyst? 

The 10 most in-demand skills for data analysts are as follows, according to Indeed:

1. Machine learning
2. Scripting 
3. SQL
4. Stata
5. Microsoft Excel
6. Tableau
7. Python
8. R
9. Microsoft SQL Server
10. SAS

The average annual salary for a data analyst is $65,364, though varies depending on metro area. 

The 10 most in-demand skills for data scientists are as follows, according to Indeed:

1. Machine learning
2. Scripting
3. Python
4. R
5. SQL
6. Spark
7. Java
8. Data mining
9. Stata
10. Hadoop

The average salary of a data scientist is $121,189 per year, though again depends on the metro area. In other words, data scientists make 86% more per year than data analysts. 

While machine learning skills are most in-demand for both roles, there is a major difference in job posting demands, the report found: More than 34% of all data science job postings ask for machine learning skills, but only 3% of data analyst jobs do. Therefore, even though machine learning might give data analysts a competitive advantage, it may not actually be required. 

What are the main differences between data scientists and data analysts? 

Indeed named these three key differences between the two positions: 

1. Data analysts answer a set of well-defined questions asked by the business, while data scientists both formulate and answer their own open-ended questions to derive business insights.
2. Data analysts primarily work with structured data from a single source, while data scientists focus on making sense of messier, unstructured data from multiple disconnected sources.
3. Data analysts organize and sort through data to solve present problems, while data scientists leverage their background in computer science, math and statistics to predict the future.

“When it comes down to it, a data scientist can’t be successful without a data analyst, and vice versa,” the report stated. “Breaking into data science requires more of an upfront investment (more advanced education, skills, etc.) but comes with a higher payoff when it comes to salary. Plus, the data science job market appears to be growing at a faster pace than the data analyst job market, which means there could be even more opportunities for this hot job in the future.”