There’s a lot of confusion about the differences between data analysts and data scientists with many people thinking these are the same job. This is due in large part to job descriptions that use the terms interchangeably or post jobs for one of the two positions with responsibilities that are generally done by the other.

This post will compare data analytics and data science, highlighting the pay, required skills, and ways to break into these fields.

Pay:

Data Analyst: Starting out, it is reasonable for an entry level data analyst to expect an annual salary of $50,000-$75,000. Some will make above and below this range, but this is a standard salary estimate for new analysts.

2-4 years in, hardworking data analysts can expect to become senior data analysts and receive a pay jump to roughly $90,000-$120,000. Again, this is an estimate and won’t be the same for everyone. Further, some people will rise through the ranks faster than others.

5-10 years in, dedicated analysts can become principal data analysts and achieve managerial positions. This range gets larger, but most in these roles will make $125,000-$200,000. The number of analysts at the top of this range is relatively small, but these roles do exist. And these analytics professionals had to start somewhere, just like you.

Data Scientist: Starting out with a more developed and complex skillset than data analysts, data scientists can reasonably expect to start out higher at $80,000-$110,000. Data scientists are in demand and the required skills demand more mathematical and technical know-how, driving the salary range up.

Working their way up in their career, top-level data scientists can expect to make $250,000 or more at the apex of their careers. Data Science is a fast-growing field, and it takes a lot of work to achieve the skills desired, making data science a highly lucrative career.

Skills Required:

Data analyst: To land your first job in data analytics, you should have a solid understanding of the basic and intermediate functions of SQL, experience creating reports with a data visualization tool like Tableau or Power BI, and a familiarity with the interface and common uses of Microsoft Excel.

Some sources claim that Python or R skills are required to become a data analyst. This couldn’t be further from the truth. Most lower-level data analyst jobs and some higher-level positions don’t require these skills. However, to become a better analyst and advance in your career, adding Python and/or R to your toolbelt can be helpful.

Additionally, you do not need crazy math skills to be a data analyst. A basic understanding of probabilities and statistics will go a long way, and the software you use does the actual calculating.

Data Scientist: To become a data scientist, it can be helpful to know some of the basics of the skills listed above. However, data scientists must learn more complex software and coding languages. They should learn some of the following skills: R, Python, Stata, SAS, Matlab, C++, Hadoop, and Hive.

Data scientists don’t need to know all of these, but virtually every data scientist knows a few of the above skills.

The level of mathematical prowess required for data science is also much greater than data analytics. A data scientist should have a strong grasp of advanced statistics and calculus. These skills are needed to build the complex statistical models required of them.

How to break into the field:

Data analyst: There are various ways to break into data analytics. The traditional route has been to get a four-year degree in a field like data analytics mathematics, computer science, information systems, business, economics, or accounting and learn the skills needed to break in.

However, it is becoming more and more common to break into data analytics without a related degree or degree at all by teaching yourself the skills and building a skills portfolio to show employers you possess these skills. If you’re interested in this route, check out THIS previous blog post for more.

Data scientist: To become a data scientist, the main route is by getting a four-year degree then getting a master’s or doctoral degree in a field like data science, mathematics, statistics, or economics.

Because of the advanced mathematical and technical skills needed, advanced degrees are the typical route to become a data scientist.

Another route would be to become a skilled data analyst first, then either learn the math and coding skills by teaching yourself or enrolling in a college or bootcamp. You’d need at least a few years of experience and a strong work ethic, but data analysts can transition to data science this way.

To summarize, data scientists typically have higher education, math prowess, and coding skills than data analysts, but they are paid handsomely for their effort. It also takes some grit for aspiring data analysts to learn SQL, data visualization, and basic Excel skills, but learning these skills still leads to an above average payday for analysts.

Thanks for reading!

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