data analyst - data analyst vs data science

Data Analyst vs. Data Scientist: What You Need To Know in 2021

Did you know that the global revenue for big data and analytics is expected to reach nearly 275 billion dollars by 2022? For enterprises looking to leverage their data to its highest potential, one of the biggest challenges is finding practical and scalable ways to use the data. To fully unlock the power of that data, you’ll likely need a data analyst or a data scientist on your team.

The real question then is: do you need a data analyst or a data scientist? Let’s take a look at the role of data analyst vs. data scientist to figure out what’s right for your enterprise.

Data Analyst vs. Data Scientist 

The world of big data is vast, with countless areas of focus. Professionals with experience in big data are in high demand. So before you hire, you’ll need to determine the specific needs of your business.

Data science is the umbrella term for the various models and methods used to get information and has multiple areas of specialization. 

Data science includes both math and statistics, and specific methodologies and tools used to collect, analyze, manipulate and interpret data. It’s focused on identifying patterns and gathering insight to explain the unknown.

Data scientists use both coding and modeling techniques to leverage the data. By connecting information and data points, data scientists can answer questions and help shape business plans for the future.  

Data analysis is often considered the secondary component to data science. Data science is the foundation of big data that focuses on tools and methods, whereas data analytics is a focused approach to understanding the data and making it usable.

Data analysts work with a specific purpose in mind. Data science is what provides the information, but the analysis is where they hone in on solving a specific issue or creating a business case to support an initiative. It’s used to measure events of the past and present, while forecasting potential outcomes for the future. 

A simple way of thinking about these two roles is that data analysts are the translator, whereas data scientists are the integrator. 

Data Scientists in the Workplace

When comparing a data analyst vs. data scientist, first thing to note is the level of education required.

Data scientists often hold advanced degrees, and they have extensive knowledge of both coding and mathematical concepts. They’ll also be skilled in business intelligence, analytics or data-driven decision making.

Data scientists can be highly instrumental in the application of artificial intelligence (AI) for your enterprise, since the implementation of AI is in part focused on leveraging data to impart autonomy.  

Data scientists can have a big impact on an enterprise by helping mitigate risks, guiding leadership in making informed decisions, uncovering new business opportunities and identifying opportunities to improve the customer experience. 

The Role of the Data Analyst

Having a ton of data can be highly beneficial to your enterprise — but only if you’re learning from it. A data analyst can be the eyes and ears of your business, finding challenges and opportunities you may not have even realized existed. 

The data analyst is responsible for taking all the data and figuring out what it all means and then translating it into a format that is easy to understand and paints a picture of the story the data tells. 

When considering whether hiring a data analyst is the right move for your enterprise, understand what exactly they can bring to your team.

A data analyst can help with: 

  • Better understanding of customer demographics 
  • Making data easy to interpret and digest 
  • More accurate measurement of business initiatives
  • Proactively identifying possible trouble areas or opportunities

Choosing a Path Towards Data Analyst vs. Data Scientist

While on the surface these roles may seem pretty similar, the differences between the two can actually have a pretty big impact on your enterprise. Both are dealing with big data, but the skillsets are not interchangeable. 

If making the most out of your big data is part of your upskilling strategy, consider the specific needs of your organization when choosing a data analyst vs. data scientist. Both can offer significant benefits, so depending on the skillset of your current teams, having employees upskill in both areas might just be the right move. 

Are you ready to create an upskill strategy for your organization?

Contact the Udacity Enterprise Team to discuss upskilling solutions. 

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