What’s the Difference Between a Data Analyst and Data Scientist?

Data analysts and data scientists are two increasingly in-demand career paths that many individuals are trying to understand in the current economy. While their names are obviously quite similar, work in data analysis and data science differ drastically from one another. Certainly, they both deal with data, but their exact methodologies and the demands of their work are quite distinct.

If you’re considering a career that employs the use of big data, then you should understand the differences between a data scientist and a data analyst. Below, we’ll detail the difference between these two paths so that you can make an informed decision about your future.

A data analyst is a professional who collects a variety of data from many unique sources, then proceeds to organize and analyze it. Businesses create a large array of data, including log files, client information, and data about their transactions and other digital interactions. A knowledgeable data analyst takes this data and transforms it into usable information. Data analysts are able to transform this often chaotic information into actionable material by using data manipulation techniques that help interpret the data at its core. Ultimately, this information helps businesses make decisions that will benefit them in the long term.

Data Analyst Skillset

When considering a career as a data analyst, it is extremely important that you identify and understand the skills that you need to master in order to find success in the industry. First and foremost, you need to acquire a thorough understanding of statistics. Second, you need to develop your programming skills with common languages such as Python. You then must be able to analyze data and create reports, typically done by successfully wrangling and translating data.

Data Analyst Responsibilities

As a data analyst, you’ll be doing the following on a daily basis:

• Collaborating with the organization to identify their needs

• Organizing data for analysis

• Identifying actionable insights

• Presenting findings in an easy-to-understand fashion

Data Analyst Career Opportunities

A data analyst is very often an entry-level role that will give you ample opportunity to master your skillset. In this kind of entry-level role, you’ll become familiar with business data that can be used to gain a substantial understanding of your organization. This role also allows you to build on both hard and soft skills while you learn how to interact with databases, create reports, and analyze all important data that moves through an organization. After mastering these hard skills, you may have the opportunity to become a senior analyst that employs more advanced techniques to explore data.

Working with a lot of chaotic information, data scientists employ a variety of statistical methodologies, data visualization techniques, and algorithms to build predictive models for a variety of industries. Notably, as this tends to involve particularly advanced programs, a data scientist is often considered a more advanced role than that of a data analyst.

Data Scientist Skillset

A data scientist typically has a comprehensive understanding of advanced mathematics, including calculus, algebra, and statistics. Similarly, a data scientist tends to have a full understanding of multiple programming languages, including Python, Spark, SQL, and more. In addition, more advanced technology is typically employed by data scientists, including data modeling that is driven by machine learning and comprehensive cloud computing.

Data Scientist Responsibilities

Here’s what a data scientist does on a daily basis:

• Harvesting, reviewing, and processing data

• Designing comprehensive, predictive models aimed to improve business practices

• Build out tools designed to organize and maintain accurate data

• Create visual success-oriented reports for their employer

• Craft automated programs to process relevant data

Data Scientist Career Opportunities 

A data scientist is very rarely an entry-level position—it’s expected that you already bring some experience and education to the table. But once you’ve gotten to that point, your opportunities as a data scientist are virtually unlimited, as essentially every industry in the world can make use of a data scientist. E-commerce, communications, and healthcare all bring in an impressive amount of data by the day, and they all need data scientists to gather relevant content from that information.

It used to be that you needed a bachelor’s degree in a relevant field to land a job as a data analyst or data scientist, but this isn’t the case anymore. Many forms of accreditation, such as those provided by professional bootcamps, have made getting into the industry that much easier. Data scientists tend to need more education to join a data-driven organization, but again, this doesn’t need to be from a traditional, degree-granting university.

According to Glassdoor, a data scientist tends to see a salary of up to $120,000, while Payscale notes an average of $65,000 for data analysts. The difference in expected salary comes as no real surprise, given how much more know-how is typically required to succeed as a data scientist.

Many are justifiably asking what the future of the data-driven business world looks like, and the simple answer is that there is no answer—big data is changing practically by the day. But the path toward a career in big data doesn’t need to be complicated, and you don’t need an advanced degree in order to become a part of the industry. There are a number of exceptional online bootcamp programs—such as the University of South Florida’s Data Analytics Bootcamp or the University of South Florida’s Data Science Bootcamp—that can teach you the skills you need to become a data scientist or data analyst in just a matter of months.