Statistics vs. Mathematics: Comparing Data-Driven CareersStatistics vs. Mathematics: Comparing Data-Driven CareersStatistics vs. Mathematics: Comparing Data-Driven Careers

Data has become a central, defining asset to businesses and governments today. Being able to transform data into readable, understandable insights is a much-needed skill. Mathematical-based professions, such as statisticians and mathematicians, are in high demand in our interconnected world. Being able to turn a jumble of numbers into readable information that a company can act upon is a useful skill. Even governments and their various departments use these professionals to help them understand the progress they have made toward achieving key goals, data-driven or otherwise.

While these two professional fields may look similar, several differences exist concerning statistics versus math. Students considering a Master of Science in Data Science degree need to gain a thorough understanding of the two concepts. Doing so can help them shape their career path in a way that best aligns with their interests and personal goals.

Statistics vs. Mathematics: What Are They?

The first step in scrutinizing the differences between statistics and mathematics is to define both terms.

Statistics, sometimes known as statistical analysis, is the scientific procedure of gathering, analyzing, and interpreting data sets to extrapolate patterns and identify trends. These insights can help give businesses a clearer, unbiased picture of the attitudes, behaviors, and interests of current and potential consumers. This information can, in turn, lead to hyperfocused engagement strategies or new products that align with market demands.

Mathematics is the scientific process of measuring order, structure, and quantity. It uses calculations and logic to develop systems that define concrete and abstract concepts in a wide range of areas. These can include technology; finance; engineering; athletics; and even fields that might seem to have little to do with math, such as art. Conceptually speaking, it provides explanations for how things work.

Advanced education in statistics and math can lead to data science careers that apply these elements to achieve specific goals.

Statistician vs. Mathematician: What Do They Do?

Statisticians and mathematicians work with data and formulas to help solve problems, and while their work can appear similar, their career paths are distinct.

What Does a Statistician Do?

Statisticians in the business world have been a critical part of many companies’ attempts to understand their consumer base. Before the advent of electronic processing systems, statisticians offered the only way for a business to gain deep insight into its business efforts. Today, statisticians usually work alongside data scientists to help pinpoint essential metrics that are crucial to a company’s success. Additionally, statisticians aid in creating theoretical frameworks that data scientists can then implement through computer code.

Statisticians are influential in helping regular people understand numbers. For the non-specialist, terms such as “average,” “mean,” and “median” all seem the same. However, to a statistician, these terms can generate completely different results depending on what’s being studied and analyzed.

A statistician’s utmost ability is to take numbers that can be confusing for the average person and present them in an understandable form. For business executives in particular, this is especially useful since it enables them to visually ascertain the points of both success and failure in their operations and how to respond to them.

What Does a Mathematician Do?

Mathematics is one of the oldest fields of human inquiry. It’s an abstract field that requires its practitioners to solve problems using calculations, logic, theorems, and a host of associated knowledge. Applied mathematics forms a significant part of many scientific fields. However, many mathematicians prefer to stick to the academic realm, solving problems that help expand the accumulated knowledge of the profession. Mathematicians continue to delve into the world of facts and figures, and academics in the field consistently develop new theorems or find innovative ways of testing old ones.

In industry, mathematicians are useful in several areas. A significant number of mathematicians work in government-funded organizations as well as in private enterprise. Mathematics is useful, for instance, to businesses that are leveraging such advanced tools as predictive analytics that enable real-world analytics models to be produced from existing raw data.

Applying mathematical theorems to real-world data can produce surprising results. Occasionally, when predicted outcomes dispute established mathematical models, it offers mathematicians opportunities to conduct further research to discover why.

Statistics vs. Math: Comparing Salaries

Regardless of the path an individual chooses, a career as a statistician versus a mathematician can be very rewarding. A number of factors can affect salaries, but the U.S. Bureau of Labor Statistics (BLS) reports competitive compensation for both.

Statistician Salary

According to the BLS, the median annual salary of a statistician was $95,570 as of May 2021, with the top 10% making a median annual salary of $157,300. Salary can fluctuate by industry; for instance, the BLS states that those working in computer systems design and related services bring in an annual mean wage of $119,210, while those in the scientific research and development services field earn a mean annual wage of $115,840.

Statistician is also one of the fastest-growing careers in the U.S. The BLS projects job growth of 33% in the field between 2021 and 2031. This is more than six times the 5% rate of job growth that the BLS projects for the average profession.

Mathematician Salary

The BLS reports that the median annual salary for mathematicians was $108,100 in 2021, with the top 10% of earners taking home a median of $169,500. As with statisticians, salary can differ by industry. For instance, the BLS notes that mathematicians in scientific research and development services roles earned a mean annual salary of $143,960, while those in architectural, engineering, and related services earned a mean annual wage of $111,840.

Positions for mathematical occupations are projected to grow by 29% between 2021 and 2031, according to the BLS.

Similarities Between Statisticians and Mathematicians

In their most basic forms, both mathematicians and statisticians deal primarily with numbers. Both professions work in their field’s range of theorems to produce results. In some cases, nonspecialists may even consider statisticians to be a subset of mathematicians. The work of these two professions displays several similarities, including a heavy dependence on theoretical research. Additionally, both mathematicians and statisticians tend to rely on previous groundwork to perform their calculations. They both use formulas or models to provide a real-world analog of their research.

The primary goals of both fields are also similar. Simplification is at the core. Both professions accomplish this by applying certain field-specific methodologies. Mathematicians may utilize formulas to transform complex readings into something simple to communicate. Similarly, statisticians can use data from thousands of individual sources and categories and transform them into visual representations.

Differences Between Statisticians and Mathematicians

The differences between a statistician versus a mathematician are easier to pinpoint. While mathematicians are usually concerned with the abstract measurement of values and creating results through functional processing, statisticians prefer a more involved methodology.

The numbers that represent data points in a statistical model can have different representations depending on what a statistician is looking at. Determining the real-world impact of those numbers is a core competency of a statistician. For a mathematician, whose work is mostly in the realm of the abstract, the concern is more about representing quantitative data in a form that the average non-specialist can understand.

Another significant difference between the professions is where their work is applied. Governmental agencies traditionally employ mathematicians in large numbers, with some individuals working in private companies that receive some level of government funding. The main goal of mathematicians in these organizations is to quantify data in such a way as to enable key decision-makers to understand the results of math-driven initiatives and experiments.

Statisticians, on the other hand, are employed primarily by private businesses. Their goals are similar to mathematicians in that they create models that their employers can use to understand the numbers that surround and drive their businesses. Still, the methodology for creating those models differs significantly from their mathematician counterparts.

Curriculum for Statistics and Mathematics

Entering the fields of mathematics and statistics requires a particular set of skills and abilities that need to be practiced and exercised consistently. At Maryville University, the online master’s in data science degree curriculum helps cultivate these skills by focusing on core concepts that are fundamental to the data science field. Gaining expertise in these skills allows professionals to stand out from other prospective job candidates.

  • Modeling: These courses focus on designing and applying modeling techniques to approach various problems and generate reasonable solutions. They include the following:
    • Math Modeling
    • Predictive Modeling
    • Statistical Modeling
  • Programming: Understanding the first principles of a concept enables a mathematician or statistician to conceptualize ways to automate data processing. To help graduates use computers to accomplish this task, several programming courses are offered, including the following:
    • Python
    • R
    • SQL
    • SAS
  • Probability: Both statisticians and mathematicians need to understand the impact that random chance plays in their work. Statistical probability courses define the concepts that a student should know to be successful.
  • Data Visualization: Presenting information in a way that’s understandable for those outside data science is critical to successfully using data. This course teaches students best practices on how to share their findings in ways that emphasize the most crucial information.

Find Your Ideal Career in Statistics or Math

If you enjoy exploring the fields of statistics and math as a professional and are looking to become a leader in the field, Maryville’s online Master of Science in Data Science dynamic program has all the tools you need to succeed. You’ll get a chance to learn the most in-demand skills from partners and employers that use data analytics across industries. To learn more about the program, visit our website and take the first step toward a better tomorrow.

Recommended Readings

What Is Data Wrangling?

Predictive Analytics in Insurance: Types, Tools, and the Future

Master’s in Statistics vs. Data Science


Business News Daily, “What Is Statistical Analysis?”

Houston Chronicle, “Mathematical Statistician Job Description”

Indeed, “How to Become a Statistician: Duties, Skills and Salary”

Live Science, “What Is Mathematics?”

U.S. Bureau of Labor Statistics, Mathematicians

U.S. Bureau of Labor Statistics, Mathematicians and Statisticians

U.S. Bureau of Labor Statistics, Math Occupations

U.S. Bureau of Labor Statistics, Statisticians

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