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What Do Statisticians Do? Roles, Responsibilities, and Career Paths

Industry Advice Analytics Science & Mathematics

Statisticians have long played a role in research and academia. Recently, however, there has been a spike in demand for statisticians in business, due to the proliferation of data generation and collection across industries and because businesses are now realizing the value of data-driven decision making.

With this increased demand in mind, it’s understandable that more and more professionals are considering careers as statisticians.

Unfortunately, the term “statistician” is rather vague, and many people are unsure what, exactly, these professionals actually do. Here, we explore the responsibilities of a statistician, the education and skills typically required to excel in the role, and offer some alternative career paths for those who want to work with data.

What is a statistician?

At a high level, statisticians are professionals who apply statistical methods and models to real-world problems. They gather, analyze, and interpret data to aid in many business decision-making processes. Statisticians are valuable employees in a range of industries, and often seek roles in areas such as business, health and medicine, government, physical sciences, and environmental sciences.

According to the Bureau of Labor Statistics, which groups statisticians with mathematicians, the job outlook for the industry is positive. Overall employment for mathematicians and statisticians is expected to grow 30 percent from 2018 to 2028—nearly five times as fast as growth for all occupations.

Much of this projected growth will result from businesses collecting an increasing amount of data from an ever-widening number of sources. In order to analyze and interpret this data, businesses and organizations will need to hire more people specifically trained in such analysis.

The BLS also reports that the median annual wage for statisticians was $91,160 per year as of 2019.

Roles and Responsibilities of a Statistician

The specific tasks that statisticians are expected to complete on a daily basis will naturally vary and depend on the specific industry and organization in which they work.

Generally speaking, in the private sector, statisticians often work to interpret data in a way that can inform organizational and business strategies; for example, by understanding changes in consumer behavior and buying trends. In the public sector, on the other hand, analyses will often be focused on furthering the public good; for example, by collecting and analyzing environmental, demographic, or health data.

Regardless of whether a statistician works in the public or private sector, their daily tasks are likely to include:

  • Collecting, analyzing, and interpreting data
  • Identifying trends and relationships in data
  • Designing processes for data collection
  • Communicating findings to stakeholders
  • Advising organizational and business strategy
  • Assisting in decision making

Required Skills for Statisticians

In order to be successful, statisticians typically have a unique combination of technical, analytical, and leadership skills. These include:

  • Analytical skills: First and foremost, statisticians must be experts in statistical analysis. They must have a keen eye for detecting patterns and anomalies in data.
  • Technical skills: To effectively collect and manipulate the data that informs their actions, statisticians must leverage computer systems, algorithms, and other technologies, meaning technical proficiency is critical.
  • Communication skills: Although statisticians are experts in mathematics and statistics, they must also exhibit strong communication skills to effectively communicate the findings of their analysis with others in their organization. This includes both verbal and written communication, as well as the ability to present data in easy-to-understand, visual ways.
  • Leadership skills: Truly effective statisticians must be able to think critically about the data that they are analyzing through the lens of key stakeholders and executives. Learning to think like a leader can help statisticians identify trends and data points that can make a big difference in their organizations.

Education for Statisticians

Many entry-level statistician roles require candidates to hold a master’s degree, usually in statistics or mathematics.

However, those who demonstrate proficiency in both statistical analysis as well as another subject area—for example, economics and econometrics, computer and material science, or biology—can have a distinct competitive advantage when seeking employment in a specialized industry.

Students are encouraged to take classes in computer sciences as well, which has important applications on the job. Those specifically considering a career in research or academia are typically required to earn a PhD.

Alternative Career Path: Professions Similar to Statisticians

For individuals who enjoy working with data or who exhibit all of these skills but don’t want to become a statistician, there are similar career paths that will still allow you to put your skills and passions to use. Two possible alternative career paths include becoming a data analyst or becoming a data scientist.

Statistics vs. Data Analytics

Similar to statisticians, data analysts identify and communicate data-driven insights that allow business stakeholders to make better-informed decisions. Nearly all industries have a need for skilled data analysts, at least to some degree. Industries with the greatest demand tend to be sales, marketing, healthcare, and various sciences.

Data analysts typically work with teams to complete projects or address problems as needed. Since most work is done on a computer, there are possibilities to work remotely in this field.

Although at first glance it may seem statistics and data analytics are one and the same, there are some major differences that set these careers apart.

According to Thomas Goulding, professor for Northeastern University’s Master’s in Data Analytics program, statisticians are more immersed in the mathematics and computational aspects of data. Data analysts should also have a strong feel for statistics, but their real skills focus on being able to use tools to extract information from the data. He points out that analysts are charged with cleaning, formatting, and integrating data so that it can be input into software to be analyzed.

“Someone with a strong background in computer science that is very comfortable using complex software to extract meaningful information from data is more suited to analytics,” he says.

In contrast, statisticians must be devoted to the computational nature of their work and be highly confident in their ability to solve complex mathematical equations.

The demand for data analysts is similarly high, as it is for statisticians. Recent projections by IBM estimate that there will be more than 2.7 million job openings in 2020, with nearly 40 percent in advanced data analytics requiring a master’s degree or higher. By comparison, the aforementioned 33 percent growth rate for statistician jobs in the U.S. translates to 13,500 additional openings by 2026.

The earning potential for these roles is also promising. Data analysts earn an annual salary between $83,750 and $142,500 per year, according to the Robert Half Technology (RHT) 2020 Salary Guide.

Statistics vs. Data Science

Another possible alternative to a career in statistics could be to pursue a role in data science. While there is a fair amount of overlap between the fields of statistics and data science, there are several important distinctions. For example, whereas statisticians use mathematical analysis to solve real-world problems, data scientists take a multidisciplinary approach which is more focused on computing techniques in order to extract insights from data.

It’s also important to understand the difference between data science and data analytics. Unlike data analysts who interpret and draw conclusions from data sets, data scientists design processes for modeling data. A large difference between the two lies in the data scientist’s need for advanced coding skills.

Data science is a rapidly growing field that has caught the attention of those looking to break into a career that combines mathematical and statistical analysis, coding skills, and substantive expertise. Since this role is considered more senior than data analysts, it is common for employers to seek candidates with a graduate degree in data science or a related field. 

Due to their expertise, data scientists can also expect competitive compensation. According to the RHT 2020 Salary Guide, data scientists in the United States earn an annual salary between $105,750 and $180,250 per year.

Choosing Your Right Fit

If you’re considering earning an advanced degree to further your career, it’s important that you choose the degree that will best prepare you for the career that you want to pursue. Consider your personal and professional goals to determine which path you want to follow.

Northeastern’s MPS in Data Analytics prepares students for a successful career in this field by offering courses in statistics, mathematics, analytics systems technology, business intelligence, advanced analytics, business process and management, business analytics agility, communicating with data, and more. Upon graduating, students possess a portfolio of professional samples that demonstrate their range and depth of skills through their participation in Northeastern’s renowned experiential network.

The data analytics program is suitable for people who are analytical thinkers and problem solvers and can be valuable to those with virtually any background. Goulding says, “the College of Professional Studies is very focused on working with professionals who want to develop new skills and capabilities in ways that will help them add value to their organizations.” As such, the program provides students with the tools and experience needed to jump-start their careers.

On the other hand, if you aspire to become a data scientist, consider the benefits that earning an MS in Data Science can have on your career. Not only do employers value advanced degrees when recruiting for these positions, but choosing a program that is rooted in experiential learning can provide you with the hands-on learning and skills you will need to excel in the workplace.   

No matter which path you choose, be sure to evaluate both your personal and professional goals to help you decide which career path is right for you

 

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