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Is a PhD In Statistics Worth It?

At face value, a statistics PhD seems like a sound career investment, the ticket to higher paying jobs and career growth.

It’s no surprise, then, that one of the most common questions we hear is: Are statistics PhD programs worth it for data science jobs?

If we’re just looking at PhD in statistics salaries, sure, from a purely financial perspective, a PhD might be a good investment in your data science career. There’s a strong financial case you can make for earning one.

But beyond the great statistics PhD salary data, there are many other variables that make the answer a little less clear. When you think about the time commitment - almost 8 years - and the average salaries between master’s and PhD students in statistics, you’ll see that there are a number of trade-offs and that the bump in earnings isn’t so significant as to be a no-brainer.

That’s not to say there aren’t tons of great benefits of a PhD, because there are. For one, a PhD provides much more specialized knowledge, which can help you land competitive, more senior-level jobs. (It’s a preferred qualification for many Google jobs, in fact.) And of course, the average starting salaries for statistics PhDs are very enticing.

To help answer the question, “Is a PhD worth it?” we took a closer look at salaries for data scientists and statistics PhDs, as well as some of the pros and cons of pursuing a PhD for your data science career.

PhD In Stats: Salary Comparison

It’s probably not all that surprising that a PhD can increase your earnings, often by 2X or 3X. That’s really across the board, in all industries. For example, according to the Bureau of Labor Statistics, median weekly pay for a PhD ($1,885) was 45% higher than bachelor’s ($1,305) in 2020.

When you take a closer look at PhDs by field, though, PhDs in math and statistics have some of the best starting salaries in any industry. According to 2019 Survey of Doctorate Recipients data , recipients of a PhD in statistics have an average median starting salary of $140,000 (when pursuing a job in industry). That’s better than business administration, economics, and engineering:

A PhD also results in a pretty big bump in salary compared to just earning a bachelor’s or master’s degree. For instance, median salaries for statistics PhD are two times that of bachelor’s recipients and 1.5 times that of master’s of statistics recipients:

In other words, if you’re looking at the question through a purely financial lens, yes, a PhD in statistics is worth it.

But there’s one caveat. The lifetime earnings of a PhD vs a master’s recipient in statistics isn’t all too significant (on average about $3.6 million vs $3.45 million).

PhD in Stats: The Skills Bump

A big reason why starting salaries are so good for statistics PhDs is that your knowledge will be much more specialized.

Master’s in statistics programs tend to provide broad knowledge in the field. You’ll get a strong foundation of the fundamentals, and become well-versed in many different statistical concepts and methodologies. But you likely won’t get the depth of knowledge that you would from a PhD program.

A PhD differs quite a bit, and these programs are built around research. Here’s how it usually works: After completing initial coursework (usually 2 years), you’ll choose an area to focus your research. And then, you’ll spend 3-5 years researching that topic and preparing a dissertation on it.

The difference in focus, therefore, provides you with very specialized knowledge, and that’s a big reason why starting PhD salaries tend to be so high.

Is It Worth It? Delayed Earnings and Career Goals

Of course, the biggest trade-off in getting all this knowledge is the time commitment. PhD candidates in statistics spend nearly a decade – 7.75 years on average – earning the credential.

And that commitment is something you have to consider to really know if it’s worth it to you. Do you want to make this time commitment and spend the next 8 years researching a topic?

As a master’s recipient, you’ll gain a lot of useful professional skills and can jump right into a career. Sure, you might fully understand advanced statistical methodologies, but you will have a strong grasp of the fundamentals. And you can learn a lot to advance your career with professional development and on-the-job training.

Although they spend a lot of time researching a topic, PhDs do have one advantage: They’re often qualified for more senior-level data science jobs. At Google, for example, a PhD is a preferred qualification for many of their data science jobs, and that’s increasingly true for many FAANG companies.

A PhD Is a Good Investment, But With One Caveat

There’s a lot of reasons why you might consider a PhD in statistics. Salaries, for one, are some of the highest in data science , and job growth for statisticians is about 30% year-over-year. You’ll also have a lot of specialized knowledge that will increase your worth and prepare you for senior-level positions.

But here’s the caveat:

Even if you earn a PhD, you’ll still have a skills gaps that you need to fill, especially if you’re interested in a career in data science. There will skills - like coding or machine learning - that you might need to brush up on.

So if you’re expecting that a PhD is a ticket to a FAANG job, it’s not. But the specialized knowledge that it brings is, increasingly, a preferred qualification.

• Graduate Studies

Ph.D. Program

The PhD program prepares students for research careers in theory and application of probability and statistics in academic and non-academic (e.g., industry, government) settings.  Students might elect to pursue either the general Statistics track of the program (the default), or one of the four specialized tracks that take advantage of UW’s interdisciplinary environment: Statistical Genetics (StatGen), Statistics in the Social Sciences (CSSS), Machine Learning and Big Data (MLBD), and Advanced Data Science (ADS). 

Admission Requirements

For application requirements and procedures, please see the graduate programs applications page .

Recommended Preparation

The Department of Statistics at the University of Washington is committed to providing a world-class education in statistics. As such, having some mathematical background is necessary to complete our core courses. This background includes linear algebra at the level of UW’s MATH 318 or 340, advanced calculus at the level of MATH 327 and 328, and introductory probability at the level of MATH 394 and 395. Real analysis at the level of UW’s MATH 424, 425, and 426 is also helpful, though not required. Descriptions of these courses can be found in the UW Course Catalog . We also recognize that some exceptional candidates will lack the needed mathematical background but succeed in our program. Admission for such applicants will involve a collaborative curriculum design process with the Graduate Program Coordinator to allow them to make up the necessary courses. 

While not a requirement, prior background in computing and data analysis is advantageous for admission to our program. In particular, programming experience at the level of UW’s CSE 142 is expected.  Additionally, our coursework assumes familiarity with a high-level programming language such as R or Python. 

Graduation Requirements 

This is a summary of the department-specific graduation requirements. For additional details on the department-specific requirements, please consult the  Ph.D. Student Handbook .  For previous versions of the Handbook, please contact the Graduate Student Advisor .  In addition, please see also the University-wide requirements at  Instructions, Policies & Procedures for Graduate Students  and  UW Doctoral Degrees .  

General Statistics Track

• Core courses: Advanced statistical theory (STAT 581, STAT 582 and STAT 583), statistical methodology (STAT 570 and STAT 571), statistical computing (STAT 534), and measure theory (either STAT 559 or MATH 574-575-576).  
• Elective courses: A minimum of four approved 500-level classes that form a coherent set, as approved in writing by the Graduate Program Coordinator.  A list of elective courses that have already been pre-approved or pre-denied can be found here .
• M.S. Theory Exam: The syllabus of the exam is available here .
• Research Prelim Exam. Requires enrollment in STAT 572. 
• Consulting.  Requires enrollment in STAT 599. 
• Applied Data Analysis Project.  Requires enrollment in 3 credits of STAT 597. 
• Statistics seminar participation: Students must attend the Statistics Department seminar and enroll in STAT 590 for at least 8 quarters. 
• Teaching requirement: All Ph.D. students must satisfactorily serve as a Teaching Assistant for at least one quarter. 
• General Exam. 
• Dissertation Credits.  A minimum of 27 credits of STAT 800, spread over at least three quarters. 
• Passage of the Dissertation Defense. 

Statistical Genetics (StatGen) Track

Students pursuing the Statistical Genetics (StatGen) Ph.D. track are required to take BIOST/STAT 550 and BIOST/STAT 551, GENOME 562 and GENOME 540 or GENOME 541. These courses may be counted as the four required Ph.D.-level electives. Additionally, students are expected to participate in the Statistical Genetics Seminar (BIOST581) in addition to participating in the statistics seminar (STAT 590). Finally, students in the Statistics Statistical Genetics Ph.D. pathway may take STAT 516-517 instead of STAT 570-571 for their Statistical Methodology core requirement. This is a transcriptable program option, i.e., the fact that the student completed the requirements will be noted in their transcript.

Statistics in the Social Sciences (CSSS) Track

Students in the Statistics in the Social Sciences (CSSS) Ph.D. track  are required to take four numerically graded 500-level courses, including at least two CSSS courses or STAT courses cross-listed with CSSS, and at most two discipline-specific social science courses that together form a coherent program of study. Additionally, students must complete at least three quarters of participation (one credit per quarter) in the CS&SS seminar (CSSS 590). This is not a transcriptable option, i.e., the fact that the student completed the requirements will not be noted in their transcript.

Machine Learning and Big Data Track

Students in the Machine Learning and Big Data (MLBD) Ph.D. track are required to take the following courses: one foundational machine learning course (STAT 535), one advanced machine learning course (either STAT 538 or STAT 548 / CSE 547), one breadth course (either on databases, CSE 544, or data visualization, CSE 512), and one additional elective course (STAT 538, STAT 548, CSE 515, CSE 512, CSE 544 or EE 578). At most two of these four courses may be counted as part of the four required PhD-level electives. Students pursuing this track are not required to take STAT 583 and can use STAT 571 to satisfy the Applied Data Analysis Project requirement. This is not a transcriptable option, i.e., the fact that the student completed the requirements will not be noted in their transcript. 

Advanced Data Science (ADS) Track

Students in the Advanced Data Science (ADS) Ph.D. track are required to take the same coursework as students in the Machine Learning and Big Data track. They are also not required to take STAT 583 and can use STAT 571 to satisfy the Applied Data Analysis Project requirement. The only difference in terms of requirements between the MLBD and the ADS tracks is that students in the ADS track must also register for at least 4 quarters of the weekly eScience Community Seminar (CHEM E 599). Also, unlike the MLBD track, the ADS is a transcriptable program option, i.e., the fact that the student completed the requirements will be noted in their transcript. 

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Phd in statistics: requirements, salary, jobs, & career growth, what is phd in statistics.

A PhD in Statistics is a doctoral-level program focusing on advanced statistical theory, methods, and applications.

Individuals with a good background in mathematics, statistics, or a similar discipline who want to build skill in statistical research, analysis, and data modeling generally pursue it.

Advanced statistical theory, probability theory, mathematical statistics, statistical computing, and data analysis are common courses in a PhD in Statistics program.

Depending on the individual curriculum and research interests of the student, it may also need study in adjacent subjects such as mathematics, computer science, or economics.

A PhD in Statistics program often requires students to undertake original statistics research and write a dissertation, which is a major and original piece of scholarly work that contributes to the subject of statistics.

This research frequently entails developing and executing statistical methodologies, evaluating data, and interpreting findings.

How much money do people make with a PhD in Statistics?

Individuals with a PhD in Statistics typically earn better salaries than others with less schooling in statistics or similar subjects.

According to data from the US Bureau of Labor Statistics (BLS) and other sources, the median annual pay for statisticians was $92,270 in May 2020. It is crucial to remember, however, that this figure indicates the median wage, which means that half of statisticians make more and half earn less.

Salaries for statisticians with a PhD in academia can vary based on the level of the academic employment.

Salaries for statisticians in academia can range from $60,000 to more than $150,000 per year, or even higher for highly talented and experienced statisticians working in famous institutions.

In the workplace, salaries for statisticians with a PhD might vary greatly based on the industry, employer, and job duties.

For example, statisticians working in banking, pharmaceuticals, technology, and consulting may earn more than those working in other areas.

In the business, salaries for statisticians can range from roughly $80,000 to well over $150,000 or more per year, depending on criteria such as employment level, region, and employer.

What is expected job growth with PhD in Statistics?

Individuals with a PhD in Statistics have a generally good career growth outlook, as demand for statistical skills continues to rise in a variety of industries due to a rising reliance on data-driven decision making and the increasing availability of massive datasets.

While particular job growth estimates differ depending on factors such as geography, sector, and economic conditions, there are a number of indicators that point to a positive work future for statisticians with a PhD.

What can you do with a PhD in Statistics?

A PhD in Statistics opens the door to a variety of careers in academia, research institutes, government organizations, and the commercial sector. With their extensive statistical knowledge, research abilities, and data analysis experience, statisticians with a PhD can pursue a variety of careers, including:

1. Careers in academia and research: Many PhD statisticians choose to work in academia as professors, researchers, or postdoctoral associates in universities or research institutions. They can conduct cutting-edge statistical methods research, create new statistical models, publish research articles, and contribute to statistical theory and practice advancement. They may also teach statistics courses and guide students interested in careers in statistics or similar subjects.

2. Statistical Consulting: PhD-level statisticians can work as statistical consultants, providing knowledge to firms in a variety of industries including healthcare, finance, technology, marketing, and others. They can work with multidisciplinary teams to develop experiments, analyze data, interpret results, and provide statistical insights to help decision-makers.

3. Data Science and Analytics: Statisticians with a PhD can work as data scientists or analysts, analyzing complicated data sets, developing and evaluating models, and deriving meaningful insights from data. They could work in fields such as finance, healthcare, technology, or marketing, where data-driven decision making is essential.

4. Government and Public Policy: PhD statisticians can work in government agencies such as the National Institutes of Health (NIH), the National Institute of Standards and Technology (NIST), or the Bureau of Labor Statistics (BLS), where they can contribute to policy development, design and conduct statistical surveys, analyze data, and provide statistical expertise in fields such as public health, economics, and social sciences.

5. Research and Development: Statistics PhD holders can work in research and development roles in industries such as pharmaceuticals, biotechnology, and technology, where they can contribute to the design and analysis of experiments, develop statistical models, and provide statistical support for research projects.

6. Entrepreneurship and consulting: PhD statisticians can start their own statistical consulting firms or data analytics firms, delivering specialized statistical services to clients in a variety of industries. They can give experience in experimental design, data analysis, statistical modeling, and tailored solutions to meet the demands of their clients.

7. Teaching and training: Statisticians with a PhD can work in educational institutions, training programs, or professional development settings, where they can teach statistics courses, develop curricula, and train students, professionals, or organizations on statistical methods, software, and data analysis techniques.

What are the requirements for a PhD in Statistics?

The specific requirements for obtaining a PhD in Statistics can vary depending on the institution and program, but generally, the following are common requirements:

1. Bachelor’s or Master’s Degree: Applicants to most PhD programs in Statistics must have a Bachelor’s degree from a recognized university. Although it is not usually required, certain schools may accept applicants with a Master’s degree in a related discipline.

2. Academic Transcripts: Applicants are usually expected to present certified transcripts of their undergraduate and graduate education, which demonstrate their academic performance and achievement.

3. Statement of Purpose: Applicants are typically expected to provide a personal statement or statement of purpose detailing their research interests, academic ambitions, and reason for pursuing a PhD in Statistics.

4. Standardized Test Scores: Applicants to many PhD programs may be required to submit scores from standardized tests such as the Graduate Record Examination (GRE) or other related assessments.

5. Letters of Recommendation: Applicants to PhD programs in Statistics are frequently required to provide letters of recommendation from academic or professional sources who may speak to the applicant’s academic talents, research potential, and eligibility for a PhD program.

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How long does it take to get a phd in statistics.

The time it takes to complete a PhD in Statistics depends on a number of factors, including the specific program, the individual’s rate of advancement, and any additional prerequisites or obligations. A PhD in Statistics normally requires 4 to 6 years of full-time study beyond a bachelor’s degree.

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Do you need a masters in statistics to get a phd in statistics.

In many circumstances, a master’s degree in statistics is not required to pursue a PhD in statistics. While some statistics PhD programs accept students with only a bachelor’s degree, others may need a master’s degree or equivalent coursework in statistics or a related discipline as a prerequisite for entrance.

A master’s degree in Statistics or a similar discipline, on the other hand, can provide considerable benefits when pursuing a PhD in Statistics.

It can provide a good foundation in statistical theory, technique, and data analysis, as well as indicate a person’s potential to perform at the graduate level.

It can also potentially allow students to transfer credits from their master’s program to their PhD program, which can reduce the overall time and coursework required to complete the PhD.

What are the Best PhD in Statistics Degree programs?

1. stanford university – phd in statistics 2. university of california, berkeley – phd in statistics 3. harvard university – phd in statistics 4. carnegie mellon university – phd in statistics 5. university of chicago – phd in statistics 6. columbia university – phd in statistics 7. duke university – phd in statistical science 8. university of washington – phd in statistics 9. massachusetts institute of technology (mit) – phd in statistics 10. university of michigan – phd in statistics, leave a comment cancel reply.

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What Can I Do with a Master’s in Statistics?

There are many prosperous career opportunities one can find in the thriving world of big data, but if you’re just beginning to survey the field, it can be difficult to determine which path is the best for you.

One area of specialization that should be high on your list of options is statistics. Dating back to the 17th century, statistics has continuously evolved as a field, remaining highly relevant even in our age of technological advancement.

If anything, statisticians have only grown in stature in recent years, as the rise of data-driven analytics has transformed businesses across industries.

Indeed, there’s a tremendous amount of need for employees with statistics skills, making it a highly valued and much sought-after role in companies of all sorts. If you have strong abilities in math, a career in statistics is worth giving serious consideration.

The majority of high-paying statistics jobs are only available to those who hold master’s degrees in statistics. This is because the advanced work expected of statisticians requires deep expertise and complex skills that are difficult to develop outside of a master’s program. Even if you hold a bachelor’s degree in statistics, it’s still recommended that you pursue further education to ensure that you can capitalize on the wonderful job opportunities available to you.

This article will provide an overview of the top job titles for those who specialize in statistics, alongside statistician salary information, job outlook, and more.

Job Titles for Statistics Specialists

As you’ll see, other master’s in statistics jobs follow from this basis of collecting data and interpreting it to improve an organization’s or individual’s decision-making. This baseline skill has a wide range of practical applications in other jobs that are worth mentioning first.

Every industry needs someone who can analyze data to simplify it, creating easier decision-making processes for others unacquainted with the technical side of probability and statistics.

Statistician

Statisticians make good money, but what exactly is their job? What does a statistician do at work?

The short answer is that statisticians are primarily responsible for processing numerical data so businesses and organizations can use it to make the right decisions. This can entail a variety of tasks, from assessing raw data to find its most salient information to projecting the probability of event outcomes.

In this day and age, businesses of all types are relying on this type of data-driven analysis to guide decisions ranging from the efficacy of operational models to the success of viral marketing campaigns. This is why statistics is a good baseline for many careers.

In contexts where high-level decisions are being made, such as in business or government, people with an understanding of statistics are necessary assets for risk-management purposes.

Statistician Salary Range and Job Outlook

Since statisticians provide such critical services to businesses, they are compensated highly for their work. According to the Bureau of Labor Statistics , as of 2021, statisticians earn a median annual salary of $96,280. Statisticians in top-paying industries, such as scientific and technical services, earn even more, with the BLS reporting a whopping median salary of $129,800.

Job growth estimates for statisticians are equally promising, if not even more so. The Bureau of Labor Statistics , job openings for statisticians will grow by an incredible 31% 2031, greatly exceeding most other industries in the United States. Indeed, few industries outside of those encompassed by big data can boast such high salaries at the same time that they promise booming job opportunities.

Data Scientist

At first glance, data scientists’ job descriptions may sound highly similar to statisticians. They are responsible for interpreting large quantities of complex data into digestible reports for professionals in all industries, especially in business.

However, the primary difference between data science and statistics is that statistics is a method involved in both disciplines. In contrast, data science uses different data interpretation and collecting methods not used by statisticians.

Data scientists use Python and SQL, like coders, and usually do not expressly write business reports. That’s typically the job of data analysts, who rank slightly lower on data teams than data scientists.

Thus, while data scientists interpret data, they do so at a high technical level and create models for analysts to interpret in non-technical language.

A benefit of a master’s in statistics is that learning new data analysis methods will not be as challenging. Having an analytics background–especially at a graduate level—makes it so new methods of analysis become easier to pick up.

Further, many data research teams feature data scientists and statisticians working together, using the different tools at their disposal to paint the most complete picture of their data findings to company leadership.

Data Scientist Salary Range and Job Outlook

According to the Bureau of Labor Statistics , as of 2021, the average data scientist’s salary was $100,910 annually, ranking it slightly higher than statisticians in terms of salary numbers for those who hold Master’s in Statistics degrees.

Like statisticians, the job outlook for data scientists is exceptional, with the Bureau of Labor Statistics reporting an anticipated growth of an absolutely stellar 36%. If you’re looking for a career path with longevity, take note.

Data Analyst

Data analysts are more responsible for interpreting data for those unacquainted with the technical side of things than data scientists are. They are accountable for producing company reports and projections based on data scientists’ models.

In other words, data analysts aren’t collecting and analyzing data so much as taking data analyzed by the data scientists and consolidating it for business executives and entrepreneurs.

Data Analyst Salary Range and Job Outlook

Another highly valued role in companies across industries, data analysts earn high salaries. According to the Bureau of Labor Statistics , the median annual salary for operations research analysts is $82,360 per year, considerably higher than many other professions. Those in mathematical science occupations earn even more, with a median annual salary of $98,680, directly in line with data scientists.

The job outlook for data analysts is also quite strong: the Bureau of Labor Statistics projects an estimated growth rate of 23% by 2031 overall, with a 29% growth expectancy for mathematical science occupations.

Of the master’s in statistics jobs, the most challenging to attain at a high level is a job as an actuary.

Actuaries use data to make long-term projections for businesses on the opportunity costs associated with decisions of all kinds. Managing risk and reward are crucial for business operations, especially in finances. Actuaries primarily work as the managers of data in financial departments across institutions to project the probabilities of current financial decisions across multiple periods.

Actuarial science is the discipline involved in actuarial work, but statistics are essential to actuarial science.

Though entry-level jobs as an actuary only require a bachelor’s degree with no particular major, anyone interested in being an actuary with a master’s degree in statistics will be able to enter the field in a high-ranking role.

While the bar for employment entry isn’t high, the bar for acceleration as an actuary is extraordinarily high. Actuarial scientists must pass a series of exams from professional societies to progress in their career, requiring much study in mathematics and statistics.

Actuary Salary Range and Job Outlook

The entry-level salary for an actuary is roughly $45,000. Still, according to the Bureau of Labor Statistics , the overall average is $105,900 annually as of 2021. It is also not uncommon for actuaries to vastly surpass this average, easily clearing $250,000 a year.

Job outlook for actuaries is remarkably strong, with the Bureau of Labor Statistics projecting an estimated growth rate of 21% overall and 29% for those in mathematical science occupations.

Software Engineer

Everything on our computers has a physical basis, known as hardware. Hardware includes exterior features of the computer, such as the screen and keyboard, and interior features, like the hard drive and the RAM.

What is on our computer and supported by such hardware is known as “software.” Computers have hard drives that store software and software is a series of codes that generate all of the programs we know, love, and use daily.

Coding is not only used for data science and data analysis, but it also serves a functional purpose. For example, someone has coded everything that exists on the internet, either directly or indirectly –by “indirectly,” what is meant is that software engineers can code something. Hence, it automatically reproduces further code, known as “machine learning.”

In any case, software engineers are those most responsible in business and technology organizations for the functional use of data and statistics by creating all of the software programs needed to use a computer.

Even a software program as banal as software engineers coded Microsoft Word because code is what responds to inputs coming from the keyboard, producing outputs in the form of words on the screen.

Software Engineer Salary Range and Job Growth

Software engineers have highly valued skills and are compensated accordingly. The Bureau of Labor Statistics reports a median annual salary for software engineers of $109,020.

Job opportunities for software engineers are growing at an impressive rate, with the BLS projecting an estimated growth of 25% by 2031.

A Master’s Degree in Statistics Jobs Boost Salaries

Master’s in Statistics jobs like statisticians, data scientists, data analysts, and actuarial science are all highly lucrative career paths. If you already have a background in statistics, mathematics, or computer science, it’s well worth considering pursuing an advanced degree. A master’s degree in statistics makes you a more viable candidate than an online certificate or bootcamp program because the rigor of the curriculum makes graduates adaptable to learning endless alternative methods of data interpretation and engineering.

To become eligible for a master’s degree in statistics, one needs a bachelor’s degree–ideally in mathematics or statistics, but not necessarily.

If you’ve decided that the right path for you is a master’s degree in statistics, one of the first things you should consider is what school you plan on attending. Click here to learn about graduate programs in statistics, among other related programs, that can help you pursue these lucrative job options.

For all other career information related to master’s degrees in statistics, click here to learn more about how to pursue a thriving big data career.

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