While both work with data, the way they use data differs. Learn more about the difference between a data scientist and a data analyst in their skillset, job function, and demand.

What’s the difference between a data analyst and a data scientist?

While their titles may sound similar, there is a difference in data analyst and data scientist jobs. They’re technical, data-driven, and both improve business processes behind the curtain. However, how they work with data is what differentiates them. Let’s take a deeper dive into the infrastructure of data analysts and data scientists to understand what they really do.

 

Learning more about data analysts 

Data analysts spend a great length of time reviewing the analytics of a business. This explains why they are required to have strong statistical skills. Data analysts spot trends in data, measure results, and formulate a story for other departments in their business. Working in a wide variety of industries, they’re able to perform extensive analyses on data to figure out the microscopic details of how an organization runs, improves, and profits.

 

As a result, data analysts have a brief background in programming skills (SaS, R, Python, etc.) to help them discover data behind the scenes. In addition, they update stakeholders on KPIs (key performance indicators) to help gauge a company’s overall long-term performance and relationship with customers. It is the job of a data analyst to easily explain what happened, or is happening, based on data alone. As you can see, they’re extremely useful and in high-demand. According to Glassdoor, the national average salary for a Data Analyst is $62,453 in the United States.

 

Prerequisites of data analysts 

Data analysts are usually equipped with at least an undergraduate degree in a science, technology, engineering, or math (STEM) major. In addition, data analysts are comfortable in creating data visualizations by using programs like Tableau, Power BI, and Salesforce Einstein Analytics, just to name a few. Data analysts can easily arrange their analyses and  showcase insightful findings to tell the story of the data to managers or executives. 

 

Other skills of data analysts

What does it take for data analysts to spend a lot of time in the process of collecting, cleaning, and shuffling data together? Here’s a list of the most common skills held by data analysts:

  • Knowledge in statistical programming languages
  • Expertise in SQL
  • Ability to perform mathematical models (linear algebra and calculus)
  • Data visualization
  • Problem solving and critical thinking
  • Collaboration and willingness to work with other departments

most common data analyst skills

 

Similarities between data analysts and data scientists

Both data analysts and data scientists perform highly technical jobs that involve diving into the depths of data, but what they do with the data is different. Data analysts and data scientists play a critical role in collecting and consolidating data for the benefit of a business; this is the middle ground — both occupations revolve around data. But data scientists rely on machine learning and AI to help design and construct new processes for data modeling and production. In that way, data analysts tell the current and past story of the data, while data scientists are looking for the future story from the data.

data analyst vs data scientist venn diagram

Learning more on data scientists

Data scientists use prototypes, algorithms, predictive models, and custom analysis to build their own automation systems and frameworks. In return, they can easily extract information for their company to solve complex problems. As you can tell, this requires heavy coding, which is another difference between data analysts and data scientists. 

 

A data scientist is capable of running data science projects, with the intent to ask and formulate questions that could benefit future business based on data. Like data analysts, they’re extremely useful and in high-demand. Thanks to the 2020 pandemic, enterprise leaders in machine learning believe data scientists are more valuable now than ever before. According to Glassdoor, the national average salary for a Data Scientist is $113,309 in the United States.

 

Prerequisites of data scientists

Data scientists should have a more extensive background than data analysts, hence why they earn more money. Exceptional programming skills are of necessity (SaS, R, Python, etc.) and mathematical and statistical knowledge, like hacking skills, are other prerequisites of data scientists.

 

In terms of education, a data scientist should at least possess a bachelor’s degree with significant experience. But a master’s degree or PHD will always look more appealing for this role and is often required. This education coupled with a background in computer science is a formula for data scientist success. 

 

Other skills of data scientists

What does it take for data scientists to do the work of a data analyst, but also engage in machine learning, advanced programming, and discover new processes for data modeling? Here’s a list of the most common skills held by data scientists:

  • Master knowledge in statistical programming languages
  • Ultimate master in SQL
  • Knowledge in Hadoop and Apache Spark
  • Ability to perform mathematical models (linear algebra and calculus)
  • Advanced data visualization
  • Adept at working with unstructured data
  • Machine learning and AI
  • Problem solving and critical thinking
  • Collaboration and willingness to work with other departments
  • Intellectual curiosity

most common data scientist skills

 

As you can see, data scientists are worth more and require additional skills. For example, they tackle tasks like building and using algorithms to create their own automation systems and frameworks. Machine learning development platforms like Algorithmia can support all types of data workers, but are especially useful in streamlining individual algorithm processes and improving full pipeline management.

 

Algorithmia provides the fastest time to value for enterprise machine learning. In addition to our machine learning development platform, you can make sense of unstructured text, teach your app to teach itself, identify objects in images, and learn from your data to become a better data scientist.

 

How Algorithmia can benefit your data game

Algorithmia is here to simplify and benefit your data practices. Whether you’re a data scientist or a data analyst, Algorithmia created a flexible machine learning platform that connects to all necessary data sources in one secure, central location for repeatable, reusable, and collaborative model management. 

 

To learn more about how our platform can benefit your data workers, be sure to check out why you need a machine learning development platform. Beyond saving time in repetition and manual processes, a prebuilt system can save time in creation for both data analysts and data scientists.