phd in data science salary

Phd In Data Science Salaries: What You Can Expect To Earn

A PhD in data science qualifies you for some of the most prestigious and high-paying jobs in tech. But exact salaries can vary significantly based on your specialization, industry, location and experience. Understanding the earning potential can help you evaluate if a PhD is worthwhile.

If you’re short on time, here’s the key takeaway: Data scientists with a PhD earn median salaries between $130,000 to $165,000, with top earners making $200,000 or more annually .

Average Salary Ranges for Data Science PhDs

When it comes to pursuing a PhD in Data Science, one of the key factors that potential students consider is the earning potential. Data Science is a rapidly growing field that offers lucrative career opportunities, and the salaries for professionals with a PhD in Data Science can be quite impressive.

National median salaries

The national median salary for individuals with a PhD in Data Science is around $120,000 per year. This figure represents the midpoint of the salary range, with some professionals earning significantly higher salaries based on their level of experience, expertise, and the industry they work in.

According to recent studies, data scientists with a PhD can earn anywhere between $90,000 to $180,000 per year, depending on various factors such as location, industry, and company size.

Salaries by major cities and regions

Salaries for data science PhDs can vary significantly based on the city or region in which you work. For example, major tech hubs like San Francisco, New York City, and Seattle tend to offer higher salaries due to the high demand for data scientists and the cost of living in these areas.

On the other hand, cities with a lower cost of living may offer slightly lower salaries.

According to a survey conducted by Glassdoor , the average salary for data scientists with a PhD in San Francisco is around $145,000 per year, while in New York City, it’s approximately $135,000 per year.

In contrast, in cities like Atlanta or Austin, the average salary might be around $120,000 per year.

Variance across industries and companies

The industry and company you work for also play a significant role in determining your salary as a data science PhD. Industries such as finance, healthcare, and technology tend to offer higher salaries due to the high demand for data-driven insights and analytics.

Big tech companies like Google, Facebook, and Amazon are known for offering competitive salaries to data science PhDs. On the other hand, start-ups and smaller companies may offer slightly lower salaries but provide other perks like equity or flexible work arrangements.

According to a study published by PayScale , the average salary for data science PhDs in the finance industry is around $150,000 per year, while in the healthcare industry, it’s approximately $140,000 per year.

However, it’s important to note that these figures can vary based on factors such as location, experience, and the specific role within the industry.

Factors That Influence Salary

Academic focus or specialization.

One of the key factors that influence the salary of a PhD in Data Science is their academic focus or specialization. Data Science is a vast field with various sub-disciplines, such as machine learning, artificial intelligence, big data analytics, and more.

Specializing in a high-demand area can significantly increase earning potential. For example, professionals with expertise in machine learning algorithms and predictive modeling are in high demand and can command higher salaries.

Industry and type of employer

The industry and type of employer also play a crucial role in determining the salary of a PhD in Data Science. Different industries have different demands for data scientists, and some industries, such as finance, healthcare, and technology, tend to offer higher salaries due to the complexity and volume of data they deal with.

Additionally, working for large companies or research institutions may lead to higher salaries compared to smaller organizations.

Cost of living and location

The cost of living and location can have a significant impact on the salary of a PhD in Data Science. Salaries can vary greatly depending on the region and city where the professional is employed. For instance, data scientists working in cities with a high cost of living, such as San Francisco or New York, may earn higher salaries to offset the higher expenses.

On the other hand, data scientists working in smaller cities or regions with a lower cost of living may have a lower salary.

Years of experience

Experience is another crucial factor that influences the salary of a PhD in Data Science. As professionals gain more experience and expertise in the field, their earning potential tends to increase. Data scientists with several years of experience may have a higher salary compared to those who are just starting their careers.

Additionally, professionals who have a track record of successful projects and achievements may be able to negotiate higher salaries.

Roles and Titles for PhDs

Obtaining a PhD in Data Science opens up a wide range of career opportunities. Let’s explore some of the roles and titles that PhD holders in this field can expect to pursue.

Data Scientist

The role of a data scientist is highly sought after in today’s data-driven world. Data scientists are responsible for collecting, analyzing, and interpreting large sets of complex data to uncover valuable insights and drive decision-making.

They use various statistical and machine learning techniques to develop models and algorithms that can predict future trends and patterns. According to Glassdoor , the average salary for a data scientist with a PhD is around $120,000 per year.

Research Scientist

A research scientist with a PhD in Data Science focuses on conducting advanced research and development in the field. They work on cutting-edge projects, exploring new algorithms, and developing innovative solutions to complex data problems.

Research scientists often publish their findings in academic journals and contribute to the advancement of the field. The average salary for a research scientist can range from $90,000 to $150,000 per year, depending on experience and industry.

Machine Learning Engineer

Machine learning engineers are responsible for designing, implementing, and maintaining the infrastructure and systems that power machine learning models. They work closely with data scientists and software engineers to deploy and scale machine learning algorithms in real-world applications.

Machine learning engineers also optimize and fine-tune models to improve their performance. According to Payscale , the average salary for a machine learning engineer with a PhD can range from $100,000 to $150,000 per year.

Analytics Lead/Director

As an analytics lead or director, PhD holders in Data Science take on managerial roles where they oversee data analytics teams and projects. They are responsible for defining the strategic direction of analytics initiatives, setting goals, and ensuring that projects are executed successfully.

Analytics leads/directors collaborate with cross-functional teams to identify business opportunities and develop data-driven strategies. The average salary for an analytics lead or director can range from $120,000 to $200,000 per year, depending on the size and industry of the organization.

These are just a few examples of the roles and titles that PhD holders in Data Science can pursue. The field is rapidly growing, and there is a high demand for skilled professionals who can leverage data to drive innovation and make informed decisions.

With the right skills and qualifications, a PhD in Data Science can open up exciting and lucrative career opportunities.

Career Advancement and Growth

Obtaining a PhD in Data Science can open up a world of career advancement and growth opportunities. With the increasing demand for data professionals in various industries, individuals with advanced degrees in data science are highly sought after.

Here are some ways in which a PhD in Data Science can propel your career:

Move into leadership/executive positions

One of the major benefits of having a PhD in Data Science is the opportunity to move into leadership or executive positions within an organization. With your advanced knowledge and expertise in data analysis and interpretation, you can take on roles such as Chief Data Officer or Data Science Director.

These positions often come with higher salaries and greater responsibilities, allowing you to have a significant impact on the strategic direction of the company.

Manage teams of data professionals

As a PhD holder in Data Science, you will have the ability to lead and manage teams of data professionals. This can involve overseeing the work of data analysts, data engineers, and data scientists, ensuring that projects are executed efficiently and effectively.

By managing a team, you can not only contribute to the success of the organization but also develop your leadership and managerial skills, which are highly valued in the industry.

Consulting and freelance opportunities

With a PhD in Data Science, you can also explore consulting and freelance opportunities. Many companies and organizations are in need of data experts to help them make sense of their data and provide insights for decision-making.

As a consultant or freelancer, you have the flexibility to work on a variety of projects with different clients, allowing you to continuously sharpen your skills and expand your network. This can lead to exciting and lucrative opportunities in the field.

According to a study conducted by Bureau of Labor Statistics , the demand for data scientists is projected to grow by 16% from 2020 to 2030, much faster than the average for all occupations. This indicates a promising outlook for those pursuing a career in the field of data science, especially for those with advanced degrees such as a PhD.

Other Benefits Beyond Salary

Expert status in a top field.

One of the major benefits of earning a PhD in Data Science is the expert status it confers upon you in a rapidly growing and highly sought-after field. With a PhD, you become a recognized authority in the world of data science, which can open doors to exciting career opportunities and prestigious positions.

Employers value the depth of knowledge and expertise that comes with a PhD, and this can lead to rewarding projects, collaborations, and leadership roles within organizations.

Opportunities for publications

Another advantage of pursuing a PhD in Data Science is the opportunity to contribute to the advancement of knowledge through research publications. As a PhD student, you will have the chance to conduct original research and publish your findings in reputable journals and conferences.

Publishing your work not only adds to the body of knowledge in the field, but it also enhances your professional reputation and can increase your chances of securing high-profile positions or academic research roles.

Additionally, having publications to your name can be a valuable asset when applying for grants and funding for future projects.

Job security and flexibility

Having a PhD in Data Science can provide a sense of job security and flexibility in the ever-evolving job market. Data scientists with advanced degrees are in high demand across industries, as organizations increasingly rely on data-driven insights to make strategic decisions.

The specialized skills and knowledge gained during a PhD program make you a valuable asset in industries such as technology, healthcare, finance, and consulting. Furthermore, the versatility of a PhD in Data Science allows you to explore various career paths, including academia, industry research, consulting, and entrepreneurship.

According to a survey conducted by Burtch Works , data scientists with PhDs tend to earn higher salaries compared to those with master’s degrees. However, it is important to note that the benefits of a PhD in Data Science extend beyond monetary compensation.

The expert status, opportunities for publications, and job security and flexibility that come with a PhD make it a worthwhile investment for those passionate about the field of data science.

A PhD in data science can unlock high-paying, advanced career opportunities in data and analytics. While salaries vary based on specialization, location, and employer, data scientists with doctoral degrees can expect to earn well into six figures.

Beyond competitive pay, a data science PhD also brings prestige, flexibility, and the chance to be a leader in an increasingly critical domain. Weighing salaries alongside other benefits can help determine if pursuing this advanced degree is the right move to maximize your career potential.

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Phd in Data Science Salary for 2024

Introduction.

A Ph.D. in data science doesn’t just offer a bump in salary, it’s increasingly becoming a required credential for the industry’s most prized jobs.

In fact, 79% of data scientists hold an advanced degree, and 38% hold a PhD. Similarly, for many experienced FAANG data science jobs, a Ph.D. is a preferred or required qualification.

Breaking down the Ph.D. in data science salary for 2023, we see results in a significant salary bump:

• The average PhD in data science salary is $172,045 .

If you’re contemplating if a data science PhD is worth it, you’ll likely recoup costs in yearly salary. Yet, there are additional advantages to pursuing a PhD, including advanced domain knowledge and research expertise.

Data Science PhDs Salary vs Master's

A Ph.D. in data science often results in some of the best-starting wages in tech. Here’s a comparison of data science salaries for PhDs vs. master’s degree holders:

Individual Contributor II

• Master’s : $123,313
• PhD : $139,857

In the top 75% of earners, the wage gap decreases to $140,000 (master’s) vs $150,000 (PhD).

Individual Contributor III

• Master’s : $150,304
• PhD : $172,262

In the top 75% of earners, the wage gap increases for more senior-level jobs, from $160,000 (master’s) to $189,000 (PhD).

Is a PhD in Data Science Worth It?

Here are some factors why a Ph.D. in Data Science may be worth it:

• New Technology - Many Ph.D. students are drawn to the challenge of solving new problems in machine learning, AI, computer vision, and robotics.
• Increasing Salaries - The earning potential of private industry jobs is a key driver.
• Need for Researchers - Companies need professionals with extensive research capabilities.

Does a Phd in Data Science Command a High Salary?

Data science PhD programs have become more and more common over the last five years. These programs offer highly specialized knowledge, and as a result, PhDs who specialize in data science can expect a significant earnings increase and will always be competitive for jobs, including senior-level management positions.

Yet, although career earnings might be more and the fact these programs can provide a competitive edge, there are some considerations that can make the decision less clear. They include:

• Cost : A Ph.D. in Data Science costs $1,300 per credit hour to $2,000 amount per credit hour. Ph.D. in Data Science programs require at least 60 - 75 credit hours, which will cost you up to $150,000 a year.
• Time Commitment : A PhD program typically requires 3-5 years of study, which can delay earnings. Master’s require as little as one year of study.

Ultimately, the worth of a data science PhD comes down to the type of work that you want to do. If you have an interest in research, you might consider a PhD.

Watch this video below on how much data scientists can make

Data Science PhD Salary vs Other Disciplines

Traditionally, those interested in a PhD for a data science career, choose a quantitative discipline, and in 2023, the most common were:

• Computer science (24%)
• Mathematics/statistics (21%)
• Natural science (21%)
• Engineering (19%)

Specific data science PhD programs are relatively new. For example, NYU launched its PhD program in 2017.

Therefore, there isn’t much data about salaries of data science PhDs vs economics PhD salaries , for instance. But these programs do offer some advantages, including highly specialized domain knowledge (like machine learning, causal inference or advanced analytics) and potential more established career networks.

Ultimately, no matter the field you choose, a PhD offers advanced training that results in a significant increase in salary, job competitiveness in the industry, and specialized knowledge at the forefront of data science.

Other top PhD fields for data science include statistics PhDs and decision science PhDs .

Data Scientist Salary

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The states and districts that pay Data Scientists the highest mean salary are California ($147,390), Washington ($140,780), Virginia ($133,990), Delaware ($133,320), and New Jersey ($129,980).

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Best Doctorates in Data Science: Top PhD Programs, Career Paths, and Salaries

If you are tech-savvy and like to stay up-to-date on the latest developments in the computing field, you might be interested in getting a data science job. The industry is interdisciplinary, with those working within it proficient in statistics, computer science, and operations research.

As such, this career isn’t pursued casually, as extensive education is required to become a data scientist and enter the industry. While some jobs can be obtained with a master’s degree, earning one of the best PhDs in Data Science is a much better option if you want the freedom to be able to conduct your own research.

Find your bootcamp match

Within your data science PhD studies, you will spend the latter two years of the four-year program doing your own unique research and writing a dissertation on your findings, preparing you to do the same in the real world. Those with a master’s degree, by contrast, don’t have as many creative liberties and don’t usually develop their own research, but rather analyze existing studies.

With the amount of schooling and skills required to work within the field, you might be wondering what a PhD in Data Science Salary looks like. Below, we’ll discuss the top schools for getting a PhD in Data Science, as well as the career outlook once you get your degree.

What Is a PhD in Data Science?

A PhD in Data Science is a four- or five-year, full-time degree pursued after a bachelor’s or master’s degree. Faculty in university PhD programs often like students to have a prior master’s degree, but if not, they might offer integrated master’s and PhD studies.

Within a PhD in Data Science program, you’ll spend the first two years of your program learning foundational knowledge, taking advanced courses in statistics, computer programming, data mining, research methodology, and so much more. The latter two years of the degree involve conducting your own unique research. You will then record your findings in a dissertation and defend your research before a committee to get your doctorate.

How to Get Into a Data Science PhD Program: Admission Requirements

You can get into a Data Science PhD program by meeting a university’s admission requirements, which will differ between each school. Typically, the minimum educational requirement is a bachelor’s degree in a related STEM degree, but most programs prefer a prior master’s degree.

If you don’t have a master’s degree, it is highly recommended to at least be proficient in a coding language and to have taken classes in calculus, statistics, and engineering. You will also need to have a minimum 3.0 GPA across your postsecondary studies and send the school your academic transcripts. There are also supplemental materials you would need for an application. These include a statement of purpose, two or three letters of recommendation, GRE scores, and a professional resume.

PhD in Data Science Admission Requirements

• A postsecondary degree in a related field
• Academic transcripts
• Graduate record examination (GRE) scores
• Coursework in data structures, algorithms, calculus, and linear algebra
• A background in a programming language
• Letters of recommendation
• Admission essays
• Personal statement

Data Science PhD Acceptance Rates: How Hard Is It to Get Into a PhD Program in Data Science?

It can be hard to get into a PhD program in Data Science. PhD programs within universities are very exclusive. While they receive a sea of applications, most schools only accept about a dozen of them. For example, Yale, one of the best schools in the country, had over 300 applicants but only made around 13 offers. As such, it is wise to apply for multiple PhD programs in order to increase your chances of getting an offer of admission.

How to Get Into the Best Universities

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Best PhDs in Data Science: In Brief

Best universities for data science phds: where to get a phd in data science.

Since data science is a new field of scientific inquiry, it can be difficult to find the best universities for getting a data science PhD. In order to help you on your educational path, we’ve listed the 10 best schools for a data science PhD, below.

Arizona State University (ASU) was ranked the nation's most innovative university by US News & World Report. Originally founded in 1885, ASU has grown to now offer more than 160 programs at the graduate level in everything from art to engineering. The graduate school is well-known for its research work.

PhD in Data Science, Analytics, and Engineering

This program is geared toward those who want to work in either the data science industry, academia, or government to solve real-world problems through data-informed methods. The 12 credits of core courses within this program include data mining, statistics, security and assurance of information, and database management. 

If you want to focus on engineering, you’ll take nine credits across cloud computing, database systems, and databases for web and other multimedia. If you want to focus on analytics, you'll take Machine Learning Statistics, Regression Analytics, and Data Visualization. 

As a culmination of your studies, you’d produce a thesis. This requires you to propose a topic of study to the dissertation supervisory committee, and upon passing their comprehensive exam, you can begin your research. Ten days before you are to defend your dissertation, which must come less than a year after completing your 60th credit, you’ll submit a version of it to the committee for review.

PhD in Data Science, Analytics, and Engineering Overview

• Program Length: 4-6 years
• Acceptance Rate: 88% (school acceptance rate)
• Tuition: $11,720/year (in state); $23,544/year (out of state)
• PhD Funding Opportunities: Awards, fellowships, and scholarships

PhD in Data Science, Analytics, and Engineering Admission Requirements

• Application
• Application fee
• Official transcripts 
• Three letters of recommendation 
• Letter of intent  
• GRE scores 

Bowling Green State University (BGSU) offers more than 20 PhD programs in a variety of disciplines including engineering, psychology, business, and music. Since its beginnings in 1910, BGSU has been noted for its engineering and scientific research, being one of eight universities in the nation with the Carnegie Foundation’s Community Engagement Classification.

PhD in Data Science

This research-oriented program is interdisciplinary, incorporating teachings from applied statistics, operations research, and computer science. A unique aspect of BGSU’s program is that you’ll need to take ethics classes in order to understand the moral ramifications of gathering data and in communications to learn to effectively present their findings. Before beginning your 16- to 30-credit dissertation, you will need to pass the qualifying examination that involves oral and written sections. 

PhD in Data Science Overview

• Program Length: 4-5 years
• Acceptance Rate: 79% (school acceptance rate)
• Tuition and Fees: $523/credit (in state); $856/credit (out of state)
• PhD Funding Opportunities: Assistantships, scholarships, fellowship 
• Payment of $45 application fee
• Minimum GPA of 3.0
• Official or unofficial transcripts from previous institutions 
• Graduate Record Examination (GRE) scores
• Graduate Management Admission Test (GMAT) scores
• Three letters of recommendation
• Resume 

Chapman offers a variety of graduate programs, with 66 master’s and seven doctoral degrees in disciplines like business, law, education, and health sciences. It was founded in 1861 and is known for its research, with more than 31,000 citations from its 5,283 publications. Chapman University is also known for its strong alumni network, which can help graduates find jobs and networking opportunities.

PhD in Computational and Data Sciences

The PhD in computational and data sciences is designed for students who want to work in fields like population genetics, earth systems, biotechnology, bioinformatics, and economic science. The curriculum includes coursework in mathematical modeling, mining data, data analysis, and computational science, as well as research and thesis guidance from faculty. 

The program is structured so that students can specialize in an area of computational science that interests them, such as scientific computing, data science, or machine learning, allowing students to also choose their dissertation topic. Before becoming doctoral candidates, students take qualifying exams for their core curriculum and do presentations on their elective courses. 

PhD in Computational  Data Sciences Overview

• Acceptance Rate: 60% (school acceptance rate)
• Tuition and Fees: $32,400 tuition
• PhD Funding Opportunities: Assistantships, work-study, loans 

PhD in Computational and Data Sciences Admission Requirements

• Proof of satisfactory coursework in computer programming, differential equations, and statistics
• $60 application fee
• Two letters of recommendation 
• 750-word statement of interest

This circa 1889 school offers more than 130 programs at the graduate level, with 1,687 students currently taking on one of its 50-plus doctoral programs. The school has various innovation clusters of research types, including those related to environmental sustainability, innovations in health, data science and cyberinfrastructure, transportation, and advanced manufacturing. 

PhD in Biomedical Data Science and Informatics

This joint program through the college and the Medical University of South Carolina (MUSC) aims to teach students how to remedy issues in medicine through the combined study of information and computer sciences. Courses within the program will cover statistical theory, data management, machine learning, and bioinformatics. 

Students spend the first two years doing coursework, the third year completing professional development training and research electives, and the fourth year solely researching. Research, seminars, and lab rotations will consist of 24 credit hours. Before completion of the program you’ll need to take a qualifying exam alongside proposing, writing, and defending your dissertation. 

PhD in Biomedical Data Science and Informatics Overview

• Acceptance Rate: 49% (school acceptance rate)
• Tuition and Fees: $691/credit (in state); $1491/credit (out of state) 
• PhD Funding Opportunities: Assistantships, scholarships and fellowships

 PhD in Biomedical Data Science and Informatics Admission Requirements

• Bachelor’s degree in a STEM field, with one year of calculus and biology classes
• Graduate record examination (GRE) scores 
• Prior computer programming work experience or coursework
• Work or research experience (recommended) 
• Personal essay
• Two or three letters of recommendation 

This university provides more than 30 doctoral programs just within the College of Science. Something unique to the circa 1949 school—known for its research in physics, immunology, molecular medicine, and biodiversity—is that its staff encourages research teams to incorporate members across various disciplines, bringing the insight and strengths of those respective fields together.

PhD in Computational Sciences and Informatics

Throughout this 72-credit program in the Department of Computational and Data Sciences, you’ll choose two out of four core courses in statistical and scientific visualization, advanced computing, databases, or numerical methods, and then choose from a rotating list of emphasis courses. Emphasis classes might cover topics like knowledge mining, statistical inference, or Bayesian inference decision theory. 

By the end of your first year, you’ll need to obtain a research advisor, then get your proposal approved by the department committee to be considered a candidate for a PhD. A month before defending their dissertations, students conduct a pre-defense to get final revision recommendations. 

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PhD in Computational Sciences and Informatics Overview

• Acceptance Rate: 91% (school acceptance rate)
• Tuition and Fees: $12,594/year (in state); $33,906/year (out of state)
• PhD Funding Opportunities: Fellowships, assistantships, lecturer positions, faculty grants, scholarships, work-study 

PhD in Computational Sciences and Informatics Admission Requirements

• Mathematics background
• Knowledge of programming languages such as C, C++, and Python
• Personal statement 

Though Harrisburg only joined the Commonwealth of Pennsylvania in 2001, the private, not-for-profit university has grown to have an enrollment of 4,000 students from over 100 countries. While it only offers three PhD degree programs in data science, computational science, and information systems engineering, it is known for its impressive research in supercomputer datamining, aquaponics, and virtual reality. 

PhD in Data Sciences 

This program strives to teach PhD candidates diverse methods of data science and train them to be able to apply their analytical knowledge across disciplines beyond data science. The first two years of the program are Harrisbug's Analytics Master's Degree, and students apply for the actual PhD in the final semester of that program. 

If, however, students have a prior master’s degree from Harrisburg in computer science, they can transfer those credits and complete this four-to-five year doctoral degree in a shorter period. After completing the classwork portion of the degree, taking labs, seminars, classes, and doing fieldwork, they’ll begin their dissertation research. The defense of their dissertation will function as their final exam.

PhD in Data Sciences Overview

• Acceptance Rate: N/A
• Tuition and Fees : $800/credit hour (in state); $4,800/credit hour (out of state)
• PhD Funding Opportunities: Scholarships, grants, loans, work-study

PhD in Data Sciences Admission Requirements

• GRE/GMAT (strongly recommended)
• Essay on career goals
• Proof of research potential (courses or projects) 
• Minimum master’s degree GPA of 3.3
• A letter of intent 

The 1969-founded Indiana University-Purdue University Indianapolis (IUPUI) is an eponymous merger between the two schools and offers 550 programs across all levels. Of those, 57 are PhDs, covering everything from American studies and economics to addiction neuroscience and epidemiology. Some of their latest research breakthroughs were in the fields of informatics and computing, cardiology, nanosystems, and artificial intelligence. 

With data science being a field in its infancy, IUPU’s School of Informatics and Computing strives to have its graduates be leaders within this ever-evolving industry. Students will take classes in system analysis and design, monitoring social media, and data mining and visualization. 

PhD candidates can collaborate with professors on groundbreaking research in information infrastructures, Android science, computer security, machine learning, dataset integration, and computational social science. After earning this interdisciplinary degree, doctoral graduates will be ready to work in academia, health care, or even business intelligence. 

• Acceptance Rate: 84% (school acceptance rate)
• Tuition and Fees: $425/credit (in state) ; $1,350.00/credit (out of state)
• PhD Funding Opportunities: Faculty grants, work-study, loans, internal funding, foundation or corporate funding, funding agencies
• GPA of 3.5 or higher 
• GRE scores in the 70th percentile or higher for all sections
• Bachelor’s degree (master’s degree preferred)
• Completed classes in computer programming, statistics theory, linear algebra, and multivariable calculus
• Online application
• 500- to 750-word statement of purpose

Founded in 1966, Kennesaw provides more than 170 programs to its 40,000-plus students. Its 11 doctoral programs include studies in computer science, education, engineering, international diplomacy, business administration, and more. The core of the university's studies relates to technology and computing, medicine, human well-being and development, and sustainability. 

Doctoral Degree in Analytics and Data Science 

This interdisciplinary program combines business, math, stats, and computer science to make for well-rounded PhD candidates. Furthering that mission, the school also teaches written and oral communication skills to help graduates thrive in business or research fields. 

In the 78-credit program, students will take classes on machine learning, mining data, analyzing big data, and graph theory in their first year. This is followed by 21 credits of electives in their second year. Though students often participate in research projects during their first two years, the latter two of their programs will involve independently-led studies for their dissertation. 

Doctoral Degree in Analytics and Data Science Overview

• Acceptance Rate: 82% (school acceptance rate)
• Tuition and Fees: Qualified students are given a research stipend and waived tuition
• PhD Funding Opportunities: Foundations and institutes, corporate programs, scholarships, grants, loans, clearinghouses, coalitions, research stipends

Doctoral Degree in Analytics and Data Science Admission Requirements

• Master’s degree in computational-related discipline
• If no master’s degree, apply to the combined Master’s Degree in Applied Statistics or Computer Science program
• Strong proficiency in a programming language like Python
• Online application and $60 application fee
• Official transcripts from previous colleges or universities 
• The graduate record examination (GRE) scores 
• Statement of purpose 
• Completion of math courses through Calculus II
• SAS Certification (preferred) 

This 1874-founded school has 150 graduate programs, with 50-plus PhD programs in disciplines like medicine, physics, economics, and chemical physics. They have research programs in more than just pure and applied mathematics, as they also perform studies on wildfires, disinfectants, and autoregulation. 

PhD in Statistics and Data Science 

Prospective employees in academia, business, or government should consider this interdisciplinary research-based program in the Department of Mathematics and  Statistics in the College of Science. The 72 credit hours of this degree are broken up into 48 hours of classwork covering topics like linear models, statistical theory and computing, and quantitative methods, 30 of which should be at the 700-level. There are 24 dissertation credits and 24 master’s classes from a previously finished graduate degree. 

In order to continue within the PhD program after the third year, candidate hopefuls will need to pass a written qualifying test. After the qualifying test, students need to score highly on an oral exam in their chosen concentration before submitting and defending their dissertations.

PhD in Statistics and Data Science Overview

• Program Length: 4-6 years (8 years max)
• Acceptance Rate: 88% for overall school
• Tuition and Fees: $305.50/credit 
• PhD Funding Opportunities: Work-study, assistantships, scholarships, stipends, tuition waiver, subsidized medical plan

PhD in Statistics and Data Science Admission Requirements

• Online application 
• Bachelor’s and master’s degree transcripts
• Mathematics test scores (recommended)
• Financial aid application

Founded in 1701, Yale University is one of the oldest universities in the United States and is ranked fifth-best school in the nation by US News & World Report. Yale has 12 different professional schools and 73 different graduate degree programs. The university is especially well-known for its research in the humanities, environmental science, social sciences, and biotechnology. 

PhD program in Statistics and Data Science 

Students entering this degree program will focus on probability, statistics, information theory, data mining, machine learning, neural networks, and more as their foundational studies. After that, students take elective classes on one-off special topics classes that change between semesters. 

Those in the program need to take an oral and practical exam in their first year and begin their dissertation work in either their second or third year. This is usually a five-year program, with students getting a dissertation fellowship in their fifth year. Yale is a very exclusive school, and last year only made between 12 and 14 offers to the 300 applicants it received. As such, applying to other schools, in addition to Yale, would be a wise choice. 

PhD program in Statistics and Data Science Overview

• Program Length: 5 years
• Acceptance Rate: 5% (school acceptance rate)
• Tuition and Fees: $45,700/year (waived through provided fellowship)
• PhD Funding Opportunities : PhD students get a fellowship that covers all tuition through first five years in addition to an annual stipend of $36,000, Teaching fellowships, stipends, and health care benefits

PhD program in Statistics and Data Science Admission Requirements

• Graduate record examination (GRE) scores (optional)
• Strong mathematical background 
• Unofficial transcripts from previous colleges

Can You Get a PhD in Data Science Online?

Yes, you can get a PhD in Data Science online. There are a few fully-online PhD programs in data science provided by schools like Northcentral University. If you wish to pursue your PhD online but haven’t been accepted into a program for data science, you can consider a computer science program that has a concentration in data science. Since data science is a subset of computer science, you would learn the same foundational skills in either program.

Best Online PhD Programs in Data Science

How long does it take to get a phd in data science.

It typically takes four to five years to complete a PhD in Data Science. While four years is the standard for most schools, some programs take a fifth year to complete due to the exhaustive research conducted. Most of the programs we’ve covered above require students to complete between 70 to 80 credits.

While that only requires between eight and 10 credits per semester, students’ schedules are filled with doing research, being a teacher’s assistant, and completing a fellowship. The amount of coursework required, the research component, and the dissertation are all factors that can affect the time it takes to earn a PhD in Data Science.

Is a PhD in Data Science Hard?

Yes, a PhD in Data Science is hard as it involves taking incredibly technical classes and conducting your own novel research within data science. The academic discipline is the merging of computer science, statistics, operations research, and more, meaning that successful students must be proficient in a wide range of technical skills.

How Much Does It Cost to Get a PhD in Data Science?

It costs, on average, $19,314 per year to get a PhD in Data Science , according to the National Center of Education Statistics. The cost will change depending on the type of school a student attends. If a doctoral student studies at a public university, the tuition is only $12,171, on average. By contrast, if a doctoral student studies at a private institution, tuition costs about $14,208 at for-profit universities, and $27,776 for nonprofit universities.

How to Pay for a PhD in Data Science: PhD Funding Options

There are many different avenues students can look into to pay for a PhD in Data Science. Some schools, such as Yale and Kennesaw State University, waive tuition for eligible students, and might even give students a yearly stipend. Another common option is to do an assistantship, in which you’d work within your data science department by teaching or doing research.

Students can also apply for various scholarships or grants to help cut down the cost of tuition. While scholarships for undergraduate students are typically merit-based, PhD funding is achieved through a student’s specific field, supporting their research and cutting tuition costs.

Best Online Master’s Degrees

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What Is the Difference Between a Data Science Master’s Degree and PhD?

The difference between a data science master’s degree and PhD is that the former program only takes about two years to complete, while the latter is the educational step past a master’s degree that takes at least four years to complete. In fact, the first two years of a PhD program are usually a master’s degree program.

As such, some schools prefer applicants to have master’s degrees to cut down on the length of time. A master’s degree, and the first two years of a PhD program, are more so classroom-based. For PhD students, this is when students learn the foundations they’ll need to conduct their own research in the final two years of their program. 

Master’s vs PhD in Data Science Job Outlook

The job outlook for people with a Master’s or PhD in Data Science is very positive. Data science is a new scientific field, so workers within its industries are in high demand. For example, computer and information research scientists , which have a minimum requirement of a master’s degree, should see their careers grow by 22 percent between 2020 and 2030, according to the US Bureau of Labor Statistics (BLS).

Medical scientists , which have a minimum educational requirement of a PhD, should see job growth of 17 percent between 2020 and 2030. While The PhD job outlook is lower in this instance, a PhD is highly desirable, which is evident by the salary discrepancy below.

Difference in Salary for Data Science Master’s vs PhD

A PhD typically leads to a higher salary than a master’s degree. For example, the US Bureau of Labor Statistics reports that computer and information research scientists average $131,490 per year, while medical scientists in the scientific research and development services industry make $129,800. By contrast, those with a master’s degree tend to earn an average salary of $106,000 , according to PayScale.

While the BLS states that the minimum educational requirement for that job is a master’s degree, typically those with master’s degrees work in analyzing existing data. With a PhD, you can conduct research within this innovative field.

Related Data Science Degrees

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Why Should You Get a PhD in Data Science?

You should get a PhD in Data Science because you will be one of the pioneering leaders in this budding field. While a master’s program teaches students how to analyze data, a PhD program empowers students to do their own research. Read below for why you should get a PhD in Data Science.

Reasons for Getting a PhD in Data Science

• Lead the Field. Data science is a new field, so those that get a doctoral degree will be at the forefront of new developments. As you’ll be analyzing data, it’s incredibly exciting to think that your PhD research will be groundbreaking.
• Ever-evolving job: Technology is constantly advancing at incredible speeds, so being the one to learn about these advancements will never get old. With artificial intelligence technologies on the rise, one can only imagine in just 10 years’ time how different our understanding of data science will be.
• Specialize in interest. As students go along their educational paths, they go from learning foundational knowledge to increasingly specific information. Thus, if you’re passionate about a subset of data science but didn’t get to focus on it in your bachelor’s or master’s degree program, a PhD is the perfect opportunity to study, research, and work within your interests.
• High salaries. As the field of data science grows, the need for data science experts will also increase. PhD graduates will be uniquely equipped for the industry’s changing landscape and will be highly sought-after.
• Research opportunities. While this is an enriching hands-on experience, it lays the groundwork for you to be able to conduct your own studies in the latter two years of your program. You will be able to follow your passions rather than just helping a faculty member succeed in their work.
• Job Market. The BLS projects that job openings in computer and information research sciences will grow by 22 percent from 2020 to 2030. In getting a PhD, you will be a more competitive applicant than those with a lesser degree. It’s likely that you can even negotiate higher salaries because of your specialties.

Getting a PhD in Data Science: Data Science PhD Coursework

Data science is an interdisciplinary field, involving bioinformatics, computer science, statistics, and operations research. As such, the coursework PhD students undertake is diverse, including data mining, bioinformatics, ethics, and data visualization. Below, we’ll discuss some of the common classes found throughout most PhD programs in data science.

Introduction to Data Science

This class will teach you the baseline information you’ll need to know to advance in your data science career. Since you’re in an advanced degree program, you’ll likely be working with real data from case studies. You’ll take that information and learn how to build and manage databases, visualize data, and run statistical analyses.

Data Mining

Raw data, though important, isn’t useful until it can be contextualized and analyzed. Data mining is also called “knowledge discovery,” meaning that mining is the process of digging through mounds of data to learn information. Students will code, select and visualize data, use machine learning, and clean information to make novel findings.

Bioinformatics

As the combination of terms implies, bioinformatics is where biology and informatics meet and involves the study of biological data. This field of study is essential for those that want to go into medicine as data scientists. If you haven’t yet completed your bachelor’s degree, pursuing one of the best undergraduate degrees in bioinformatics is a wise choice.

Ethics of Data Science

Since data science involves collecting and storing information, mostly on people, there are possible moral ramifications to this. Within an ethics class, you’ll learn the proper methodology for conducting research to assure that your work meets codes of conduct.

Data Visualization

An important aspect of conducting research is being able to articulate your discoveries. Through visualization in programming languages like R, you’ll learn how to plot data and make reports. This process helps you organize your findings as well as snuff out any errors made during computation.

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How to Get a PhD in Data Science: Doctoral Program Requirements

You can get a PhD in Data Science by meeting your chosen university’s degree requirements. Though these can vary, there are commonalities across different schools, such as completing a set number of credits, taking exams, and crafting a dissertation. We’ll now go into more detail about these common components of the doctoral degree.

Data Science PhD programs typically require the completion of 70 and 80 credit hours. This is often split down the middle, with the first half of credits being done in a classroom, and the latter half being done through your research and dissertation. 

While full-time undergraduate students take 15 or so credits per semester, the number of PhD students is lower as they conduct work outside of classroom hours through assistantships. Candidates typically complete this degree studying full-time for four to five years, taking between eight and 10 credits per semester. Most schools have a cap on the maximum number of years a student has to complete their PhD. For example, University of Nevada’s maximum allowance is eight years.

Before entering a PhD program, students already have a bachelor’s or master’s degree in a relevant STEM discipline. With the basics in coding languages, a statistical method, calculus, and engineering out of the way, doctorate students can take a deep dive into more difficult and focused courses. Some examples of classes PhD students will take include machine learning, data visualization, and bioinformatics. 

Often students will be able to choose a specialization to narrow the focus of their research. This allows them to take more niche classes on topics like asymptotics, stochastic processes, and Bayes theorem. After completing two years of classwork, students then begin their dissertation studies.

In order to be considered candidates for a PhD, students will need to pass exams between the first and third year of their degree program. The tests, which often consist of a verbal exam and written assessment, determine what the candidate-hopefuls have learned so far and whether they will be effective researchers with the school’s department.

After completing two years of coursework and passing their qualifying exams, PhD candidates begin research for their eventual dissertations. Candidates collaborate with a chosen faculty member to help guide them in their approved topic of study. Students then write about their findings in a dissertation or thesis, which they will need to defend in front of a committee before being considered doctors. 

Potential Careers With an Data Science Degree

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PhD in Data Science Salary and Job Outlook

The salary and job outlook for those with a PhD in Data Science is very positive. The field of study is fairly new, and while there might not be many jobs that specifically require a PhD, having a terminal degree will make you a competitive applicant for prospective employers.

Data science PhD holders can work as medical scientists in the scientific research and development services industry or as information and computer research scientists. For the former industry, which requires a PhD, there will be a 17-percent increase in job openings between 2020 and 2030, per the US Bureau of Labor Statistics (BLS). The US BLS reports that computational and informational research sciences will see a 22-percent increase in job openings during that decade. Average salaries for those professionals are $116,430 and $131,490, respectively.

Though the salary for those in computational and informational research sciences is higher and typically only requires a master’s degree, those with a PhD are more likely to work in those positions. This is because PhD holders often conduct research, having done so in their doctoral programs, while former graduate students often analyze existing findings instead.

What Can You Do With a PhD in Data Science?

With a PhD in Data Science, there are a plethora of jobs within reach . The field of study is interdisciplinary, meaning that you’d be equipped with the skills to thrive in careers relating to computer science, bioinformatics, engineering, data management, and so much more. Let’s discuss further some of the highest-paying jobs that you can get with a PhD in Data Science.

Best Jobs with a PhD in Data Science

• Computer and Information Research Scientist
• Mathematicians/Statistician
• Medical Scientist
• Machine Learning Engineer
• Data Scientist

What Is the Average Salary for a PhD in Data Science?

The average salary for a PhD in Data Science is around $131,000. Payscale reports this is the average salary for those with a PhD in Computer Science, and since data science is a specialization of computer science, one can infer the salaries would be similar.

While $131,000 can be the expected salary for those with a Doctorate in Computer Science—and data science, by extension—the average salary you might earn will depend on a few variables. These include the amount of work experience you have, the industry you are working in, the organization you are working for, and the region of the country you are working in.

Highest-Paying Data Science Jobs for PhD Grads

Best data science jobs with a doctorate.

The best data science jobs with a doctorate are as a computer and information research scientist, mathematician or statistician, medical scientist, machine learning engineer, or data scientist. All of the above careers earn over $100,000 per year, but the actual salary a job might offer can differ.

These professionals are found across health care, corporate, and scientific fields and work to optimize the computer systems for their organization. This is done through distilling overly-complicated algorithms, troubleshooting issues with other engineers, and conducting research into developing new electronic programs. 

Though these jobs usually have a minimum education requirement of a master’s degree, those with a PhD are likely to also populate this sector and will likely be given preference by employers. This is because those with a PhD conduct research more frequently than those with a master’s degree, who usually analyze existing data. 

• Salary with a Data Science PhD: $131,490
• Job Outlook: 22% job growth from 2020 to 2030
• Number of Jobs: 33,000
• Highest-Paying States: Oregon, Texas, Arizona, Washington, Massachusetts

While both statisticians and data scientists analyze sets of information, a difference between the two is that while the field of computational statistics can be broad, data science is more focused on computer science and machine learning. They do, however, use the same methodology for analysis, so with your PhD in Data Science, you’d be equipped to be a statistician in a variety of industries.

According to the US Bureau of Labor Statistics, statisticians and mathematicians that work in technical, professional, and scientific services make on average $129,800, and those that work in life, engineering, and physical science development and research earn $114,770 per year. 

• Salary with a Data Science PhD: $129,800
• Job Outlook: 33% job growth from 2020 to 2030
• Number of Jobs: 44,800
• Highest-Paying States: Connecticut, New York, Massachusetts, Wyoming, and California  

Working in what the BLS calls the “ scientific research and development services industry ,” you could use your data science know-how in the medical field, especially if you concentrated or did your dissertation in health care. You’d likely data mine through patient information, analyzing it to then make recommendations to those in your health system organization. 

• Salary with a Data Science PhD: $116,430
• Job Outlook: 17% job growth from 2020 to 2030
• Number of Jobs: 133,900
• Highest-Paying States: Maine, New Jersey, Tennessee, Connecticut, Delaware

Artificial intelligence is growing alongside the data science field. Pursuing this career would allow you to be able to help foster artificial intelligence (AI) programs. You’d code your own AI system, teaching it how to analyze large amounts of data and how the system should respond to it. 

• Salary with a Data Science PhD: $112,709
• Job Outlook: 22% job growth from 2020 to 2030 
• Number of Jobs: 33,000 (for computer and information research scientists) 
• Highest-Paying States: Oregon, Texas, Arizona, Washington, Massachusetts (for computer and information research scientists) 

Those that work within this field combine their knowledge of informatics, computer programming, data mining and management, and more to conduct research by studying data sets. Data Scientists can work in business, devising avenues to optimize profits by looking at reports.health care, presenting their studies to guide decisions bettering the medical system; and academia, working to innovate this budding field of study. 

• Salary with a Data Science PhD: $108,660
• Number of Jobs: 105,980
• Highest-Paying States: New Jersey, New York, Delaware, Washington, California

Is a PhD in Data Science Worth It?

Yes, a PhD in Data Science is much worth it. Though not all data science jobs require a PhD, with some upper-level careers only requiring a master’s degree, you would have an advantage over others with lower levels of education. You’d have experience conducting your own research method, which would prepare you for running your own studies in the real world.

Most with master’s degrees don’t actually develop their own studies, rather, they analyze existing information. A PhD would give you a competitive edge, making you a more impressive candidate to prospective employers. You’d be more likely to get hired, and more plausibly able to negotiate a higher salary.

Data science is a new field of inquiry, so by having a doctorate in it, you would be at the forefront of the technological advancements within the industry. You would likely make at least $100,000 yearly in data science and have the interdisciplinary skills to work in other industries if you desire.

Additional Reading About Data Science

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PhD in Data Science FAQ

Yes, many data scientists have PhDs, but it is not a requirement for many jobs in the industry. Some require only master’s degrees instead, but there are advantages to having a PhD. With a PhD, you’ll have conducted your own research to get your doctorate, allowing you to more easily create your own studies in the real world. Those with a master’s degree, by contrast, usually only analyze existing studies, giving you less creative liberties with what you work on.

There are many career options for those with a PhD in Data Science, as the course of study is interdisciplinary. This means that those with the degree are taught technical skills that can apply to multiple different industries. Often, those with a data science degree go on to work in either business, medicine, or academia.

The US Bureau of Labor Statistics (BLS) reports that the median annual salary for data scientists is $131,490 . This report states that those within this job often have a master’s degree, meaning that with your PhD, you’d be a more impressive candidate and be able to negotiate a higher salary. Additionally, the BLS estimates that demand for data scientists will grow by 22 percent from 2020 to 2030, a much higher rate than the national average of eight percent.

A PhD is the best degree to become a data scientist if you want to conduct your own studies. Many within data science have lesser degrees, usually a master’s and sometimes a bachelor’s, which in turn gives them less responsibility within the field. If you have a scientific inquiry that you are passionate about and want the freedom to study what you want, a PhD is the best degree to obtain.

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PhD in Data Science – Your Guide to Choosing a Doctorate Degree Program

Created by aasif.faizal

Professional opportunities in data science are growing incredibly fast. That’s great news for students looking to pursue a career as a data scientist. But it also means that there are a lot more options out there to investigate and understand before developing the best educational path for you.

A PhD is the most advanced data science degree you can get, reflecting a depth of knowledge and technical expertise that will put you at the top of your field.

This means that PhD programs are the most time-intensive degree option out there, typically requiring that students complete dissertations involving rigorous research. This means that PhDs are not for everyone. Indeed, many who work in the world of big data hold master’s degrees rather than PhDs, which tend to involve the same coursework as PhD programs without a dissertation component. However, for the right candidate, a PhD program is the perfect choice to become a true expert on your area of focus.

If you’ve concluded that a data science PhD is the right path for you, this guide is intended to help you choose the best program to suit your needs. It will walk through some of the key considerations while picking graduate data science programs and some of the nuts and bolts (like course load and tuition costs) that are part of the data science PhD decision-making process.

Data Science PhD vs. Masters: Choosing the right option for you

If you’re considering pursuing a data science PhD, it’s worth knowing that such an advanced degree isn’t strictly necessary in order to get good work opportunities. Many who work in the field of big data only hold master’s degrees, which is the level of education expected to be a competitive candidate for data science positions.

So why pursue a data science PhD?

Simply put, a PhD in data science will leave you qualified to enter the big data industry at a high level from the outset.

You’ll be eligible for advanced positions within companies, holding greater responsibilities, keeping more direct communication with leadership, and having more influence on important data-driven decisions. You’re also likely to receive greater compensation to match your rank.

However, PhDs are not for everyone. Dissertations require a great deal of time and an interest in intensive research. If you are eager to jumpstart a career quickly, a master’s program will give you the preparation you need to hit the ground running. PhDs are appropriate for those who want to commit their time and effort to schooling as a long-term investment in their professional trajectory.

For more information on the difference between data science PhD’s and master’s programs, take a look at our guide here.

Topics include:

• Can I get an Online Ph.D in Data Science?
• Overview of Ph.d Coursework

Preparing for a Doctorate Program

Building a solid track record of professional experience, things to consider when choosing a school.

• What Does it Cost to Get a Ph.D in Data Science?
• School Listings

Data Science PhD Programs, Historically

Historically, data science PhD programs were one of the main avenues to get a good data-related position in academia or industry. But, PhD programs are heavily research oriented and require a somewhat long term investment of time, money, and energy to obtain. The issue that some data science PhD holders are reporting, especially in industry settings, is that that the state of the art is moving so quickly, and that the data science industry is evolving so rapidly, that an abundance of research oriented expertise is not always what’s heavily sought after.

Instead, many companies are looking for candidates who are up to date with the latest data science techniques and technologies, and are willing to pivot to match emerging trends and practices.

One recent development that is making the data science graduate school decisions more complex is the introduction of specialty master’s degrees, that focus on rigorous but compact, professional training. Both students and companies are realizing the value of an intensive, more industry-focused degree that can provide sufficient enough training to manage complex projects and that are more client oriented, opposed to research oriented.

However, not all prospective data science PhD students are looking for jobs in industry. There are some pretty amazing research opportunities opening up across a variety of academic fields that are making use of new data collection and analysis tools. Experts that understand how to leverage data systems including statistics and computer science to analyze trends and build models will be in high demand.

Can You Get a PhD in Data Science Online?

While it is not common to get a data science Ph.D. online, there are currently two options for those looking to take advantage of the flexibility of an online program.

Indiana University Bloomington and Northcentral University both offer online Ph.D. programs with either a minor or specialization in data science.

Given the trend for schools to continue increasing online offerings, expect to see additional schools adding this option in the near future.

Overview of PhD Coursework

A PhD requires a lot of academic work, which generally requires between four and five years (sometimes longer) to complete.

Here are some of the high level factors to consider and evaluate when comparing data science graduate programs.

How many credits are required for a PhD in data science?

On average, it takes 71 credits to graduate with a PhD in data science — far longer (almost double) than traditional master’s degree programs. In addition to coursework, most PhD students also have research and teaching responsibilities that can be simultaneously demanding and really great career preparation.

What’s the core curriculum like?

In a data science doctoral program, you’ll be expected to learn many skills and also how to apply them across domains and disciplines. Core curriculums will vary from program to program, but almost all will have a core foundation of statistics.

All PhD candidates will have to take a qualifying exam. This can vary from university to university, but to give you some insight, it is broken up into three phases at Yale. They have a practical exam, a theory exam and an oral exam. The goal is to make sure doctoral students are developing the appropriate level of expertise.

Dissertation

One of the final steps of a PhD program involves presenting original research findings in a formal document called a dissertation. These will provide background and context, as well as findings and analysis, and can contribute to the understanding and evolution of data science. A dissertation idea most often provides the framework for how a PhD candidate’s graduate school experience will unfold, so it’s important to be thoughtful and deliberate while considering research opportunities.

Since data science is such a rapidly evolving field and because choosing the right PhD program is such an important factor in developing a successful career path, there are some steps that prospective doctoral students can take in advance to find the best-fitting opportunity.

Join professional associations

Even before being fully credentials, joining professional associations and organizations such as the Data Science Association and the American Association of Big Data Professionals is a good way to get exposure to the field. Many professional societies are welcoming to new members and even encourage student participation with things like discounted membership fees and awards and contest categories for student researchers. One of the biggest advantages to joining is that these professional associations bring together other data scientists for conference events, research-sharing opportunities, networking and continuing education opportunities.

Leverage your social network

Be on the lookout to make professional connections with professors, peers, and members of industry. There are a number of LinkedIn groups dedicated to data science. A well-maintained professional network is always useful to have when looking for advice or letters of recommendation while applying to graduate school and then later while applying for jobs and other career-related opportunities.

Kaggle competitions

Kaggle competitions provide the opportunity to solve real-world data science problems and win prizes. A list of data science problems can be found at Kaggle.com . Winning one of these competitions is a good way to demonstrate professional interest and experience.

Internships

Internships are a great way to get real-world experience in data science while also getting to work for top names in the world of business. For example, IBM offers a data science internship which would also help to stand out when applying for PhD programs, as well as in seeking employment in the future.

Demonstrating professional experience is not only important when looking for jobs, but it can also help while applying for graduate school. There are a number of ways for prospective students to gain exposure to the field and explore different facets of data science careers.

Get certified

There are a number of data-related certificate programs that are open to people with a variety of academic and professional experience. DeZyre has an excellent guide to different certifications, some of which might help provide good background for graduate school applications.

Conferences

Conferences are a great place to meet people presenting new and exciting research in the data science field and bounce ideas off of newfound connections. Like professional societies and organizations, discounted student rates are available to encourage student participation. In addition, some conferences will waive fees if you are presenting a poster or research at the conference, which is an extra incentive to present.

It can be hard to quantify what makes a good-fit when it comes to data science graduate school programs. There are easy to evaluate factors, such as cost and location, and then there are harder to evaluate criteria such as networking opportunities, accessibility to professors, and the up-to-dateness of the program’s curriculum.

Nevertheless, there are some key relevant considerations when applying to almost any data science graduate program.

What most schools will require when applying:

• All undergraduate and graduate transcripts
• A statement of intent for the program (reason for applying and future plans)
• Letters of reference
• Application fee
• Online application
• A curriculum vitae (outlining all of your academic and professional accomplishments)

What Does it Cost to Get a PhD in Data Science?

The great news is that many PhD data science programs are supported by fellowships and stipends. Some are completely funded, meaning the school will pay tuition and basic living expenses. Here are several examples of fully funded programs:

• University of Southern California
• University of Nevada, Reno
• Kennesaw State University
• Worcester Polytechnic Institute
• University of Maryland

For all other programs, the average range of tuition, depending on the school can range anywhere from $1,300 per credit hour to $2,000 amount per credit hour. Remember, typical PhD programs in data science are between 60 and 75 credit hours, meaning you could spend up to $150,000 over several years.

That’s why the financial aspects are so important to evaluate when assessing PhD programs, because some schools offer full stipends so that you are able to attend without having to find supplemental scholarships or tuition assistance.

Can I become a professor of data science with a PhD.? Yes! If you are interested in teaching at the college or graduate level, a PhD is the degree needed to establish the full expertise expected to be a professor. Some data scientists who hold PhDs start by entering the field of big data and pivot over to teaching after gaining a significant amount of work experience. If you’re driven to teach others or to pursue advanced research in data science, a PhD is the right degree for you.

Do I need a master’s in order to pursue a PhD.? No. Many who pursue PhDs in Data Science do not already hold advanced degrees, and many PhD programs include all the coursework of a master’s program in the first two years of school. For many students, this is the most time-effective option, allowing you to complete your education in a single pass rather than interrupting your studies after your master’s program.

Can I choose to pursue a PhD after already receiving my master’s? Yes. A master’s program can be an opportunity to get the lay of the land and determine the specific career path you’d like to forge in the world of big data. Some schools may allow you to simply extend your academic timeline after receiving your master’s degree, and it is also possible to return to school to receive a PhD if you have been working in the field for some time.

If a PhD. isn’t necessary, is it a waste of time? While not all students are candidates for PhDs, for the right students – who are keen on doing in-depth research, have the time to devote to many years of school, and potentially have an interest in continuing to work in academia – a PhD is a great choice. For more information on this question, take a look at our article Is a Data Science PhD. Worth It?

Complete List of Data Science PhD Programs

Below you will find the most comprehensive list of schools offering a doctorate in data science. Each school listing contains a link to the program specific page, GRE or a master’s degree requirements, and a link to a page with detailed course information.

Note that the listing only contains true data science programs. Other similar programs are often lumped together on other sites, but we have chosen to list programs such as data analytics and business intelligence on a separate section of the website.

Boise State University  – Boise, Idaho PhD in Computing – Data Science Concentration

The Data Science emphasis focuses on the development of mathematical and statistical algorithms, software, and computing systems to extract knowledge or insights from data.  

In 60 credits, students complete an Introduction to Graduate Studies, 12 credits of core courses, 6 credits of data science elective courses, 10 credits of other elective courses, a Doctoral Comprehensive Examination worth 1 credit, and a 30-credit dissertation.

Electives can be taken in focus areas such as Anthropology, Biometry, Ecology/Evolution and Behavior, Econometrics, Electrical Engineering, Earth Dynamics and Informatics, Geoscience, Geostatistics, Hydrology and Hydrogeology, Materials Science, and Transportation Science.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $7,236 total (Resident), $24,573 total (Non-resident)

View Course Offerings

Bowling Green State University  – Bowling Green, Ohio Ph.D. in Data Science

Data Science students at Bowling Green intertwine knowledge of computer science with statistics.

Students learn techniques in analyzing structured, unstructured, and dynamic datasets.

Courses train students to understand the principles of analytic methods and articulating the strengths and limitations of analytical methods.

The program requires 60 credit hours in the studies of Computer Science (6 credit hours), Statistics (6 credit hours), Data Science Exploration and Communication, Ethical Issues, Advanced Data Mining, and Applied Data Science Experience.

Students must also complete 21 credit hours of elective courses, a qualifying exam, a preliminary exam, and a dissertation.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $8,418 (Resident), $14,410 (Non-resident)

Brown University  – Providence, Rhode Island PhD in Computer Science – Concentration in Data Science

Brown University’s database group is a world leader in systems-oriented database research; they seek PhD candidates with strong system-building skills who are interested in researching TupleWare, MLbase, MDCC, Crowd DB, or PIQL.

In order to gain entrance, applicants should consider first doing a research internship at Brown with this group. Other ways to boost an application are to take and do well at massive open online courses, do an internship at a large company, and get involved in a large open-source software project.

Coding well in C++ is preferred.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $62,680 total

Chapman University  – Irvine, California Doctorate in Computational and Data Sciences

Candidates for the doctorate in computational and data science at Chapman University begin by completing 13 core credits in basic methodologies and techniques of computational science.

Students complete 45 credits of electives, which are personalized to match the specific interests and research topics of the student.

Finally, students complete up to 12 credits in dissertation research.

Applicants must have completed courses in differential equations, data structures, and probability and statistics, or take specific foundation courses, before beginning coursework toward the PhD.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $37,538 per year

Clemson University / Medical University of South Carolina (MUSC) – Joint Program – Clemson, South Carolina & Charleston, South Carolina Doctor of Philosophy in Biomedical Data Science and Informatics – Clemson

The PhD in biomedical data science and informatics is a joint program co-authored by Clemson University and the Medical University of South Carolina (MUSC).

Students choose one of three tracks to pursue: precision medicine, population health, and clinical and translational informatics. Students complete 65-68 credit hours, and take courses in each of 5 areas: biomedical informatics foundations and applications; computing/math/statistics/engineering; population health, health systems, and policy; biomedical/medical domain; and lab rotations, seminars, and doctoral research.

Applicants must have a bachelor’s in health science, computing, mathematics, statistics, engineering, or a related field, and it is recommended to also have competency in a second of these areas.

Program requirements include a year of calculus and college biology, as well as experience in computer programming.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $10,858 total (South Carolina Resident), $22,566 total (Non-resident)

View Course Offerings – Clemson

George Mason University  – Fairfax, Virginia Doctor of Philosophy in Computational Sciences and Informatics – Emphasis in Data Science

George Mason’s PhD in computational sciences and informatics requires a minimum of 72 credit hours, though this can be reduced if a student has already completed a master’s. 48 credits are toward graduate coursework, and an additional 24 are for dissertation research.

Students choose an area of emphasis—either computer modeling and simulation or data science—and completed 18 credits of the coursework in this area. Students are expected to completed the coursework in 4-5 years.

Applicants to this program must have a bachelor’s degree in a natural science, mathematics, engineering, or computer science, and must have knowledge and experience with differential equations and computer programming.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $13,426 total (Virginia Resident), $35,377 total (Non-resident)

Harrisburg University of Science and Technology  – Harrisburg, Pennsylvania Doctor of Philosophy in Data Sciences

Harrisburg University’s PhD in data science is a 4-5 year program, the first 2 of which make up the Harrisburg master’s in analytics.

Beyond this, PhD candidates complete six milestones to obtain the degree, including 18 semester hours in doctoral-level courses, such as multivariate data analysis, graph theory, machine learning.

Following the completion of ANLY 760 Doctoral Research Seminar, students in the program complete their 12 hours of dissertation research bringing the total program hours to 36.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $14,940 total

Icahn School of Medicine at Mount Sinai  – New York, New York Genetics and Data Science, PhD

As part of the Biomedical Science PhD program, the Genetics and Data Science multidisciplinary training offers research opportunities that expand on genetic research and modern genomics. The training also integrates several disciplines of biomedical sciences with machine learning, network modeling, and big data analysis.

Students in the Genetics and Data Science program complete a predetermined course schedule with a total of 64 credits and 3 years of study.

Additional course requirements and electives include laboratory rotations, a thesis proposal exam and thesis defense, Computer Systems, Intro to Algorithms, Machine Learning for Biomedical Data Science, Translational Genomics, and Practical Analysis of a Personal Genome.

Delivery Method: Campus GRE: Not Required 2022-2023 Tuition: $31,303 total

Indiana University-Purdue University Indianapolis  – Indianapolis, Indiana PhD in Data Science PhD Minor in Applied Data Science

Doctoral candidates pursuing the PhD in data science at Indiana University-Purdue must display competency in research, data analytics, and at management and infrastructure to earn the degree.

The PhD is comprised of 24 credits of a data science core, 18 credits of methods courses, 18 credits of a specialization, written and oral qualifying exams, and 30 credits of dissertation research. All requirements must be completed within 7 years.

Applicants are generally expected to have a master’s in social science, health, data science, or computer science. 

Currently a majority of the PhD students at IUPUI are funded by faculty grants and two are funded by the federal government. None of the students are self funded.

IUPUI also offers a PhD Minor in Applied Data Science that is 12-18 credits. The minor is open to students enrolled at IUPUI or IU Bloomington in a doctoral program other than Data Science.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $9,228 per year (Indiana Resident), $25,368 per year (Non-resident)

Jackson State University – Jackson, Mississippi PhD Computational and Data-Enabled Science and Engineering

Jackson State University offers a PhD in computational and data-enabled science and engineering with 5 concentration areas: computational biology and bioinformatics, computational science and engineering, computational physical science, computation public health, and computational mathematics and social science.

Students complete 12 credits of common core courses, 12 credits in the specialization, 24 credits of electives, and 24 credits in dissertation research.

Students may complete the doctoral program in as little as 5 years and no more than 8 years.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $8,270 total

Kennesaw State University  – Kennesaw, Georgia PhD in Analytics and Data Science

Students pursuing a PhD in analytics and data science at Kennesaw State University must complete 78 credit hours: 48 course hours and 6 electives (spread over 4 years of study), a minimum 12 credit hours for dissertation research, and a minimum 12 credit-hour internship.

Prior to dissertation research, the comprehensive examination will cover material from the three areas of study: computer science, mathematics, and statistics.

Successful applicants will have a master’s degree in a computational field, calculus I and II, programming experience, modeling experience, and are encouraged to have a base SAS certification.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $5,328 total (Georgia Resident), $19,188 total (Non-resident)

New Jersey Institute of Technology  – Newark, New Jersey PhD in Business Data Science

Students may enter the PhD program in business data science at the New Jersey Institute of Technology with either a relevant bachelor’s or master’s degree. Students with bachelor’s degrees begin with 36 credits of advanced courses, and those with master’s take 18 credits before moving on to credits in dissertation research.

Core courses include business research methods, data mining and analysis, data management system design, statistical computing with SAS and R, and regression analysis.

Students take qualifying examinations at the end of years 1 and 2, and must defend their dissertations successfully by the end of year 6.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $21,932 total (New Jersey Resident), $32,426 total (Non-resident)

New York University  – New York, New York PhD in Data Science

Doctoral candidates in data science at New York University must complete 72 credit hours, pass a comprehensive and qualifying exam, and defend a dissertation with 10 years of entering the program.

Required courses include an introduction to data science, probability and statistics for data science, machine learning and computational statistics, big data, and inference and representation.

Applicants must have an undergraduate or master’s degree in fields such as mathematics, statistics, computer science, engineering, or other scientific disciplines. Experience with calculus, probability, statistics, and computer programming is also required.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $37,332 per year

View Course Offering

Northcentral University  – San Diego, California PhD in Data Science-TIM

Northcentral University offers a PhD in technology and innovation management with a specialization in data science.

The program requires 60 credit hours, including 6-7 core courses, 3 in research, a PhD portfolio, and 4 dissertation courses.

The data science specialization requires 6 courses: data mining, knowledge management, quantitative methods for data analytics and business intelligence, data visualization, predicting the future, and big data integration.

Applicants must have a master’s already.

Delivery Method: Online GRE: Required 2022-2023 Tuition: $16,794 total

Stevens Institute of Technology – Hoboken, New Jersey Ph.D. in Data Science

Stevens Institute of Technology has developed a data science Ph.D. program geared to help graduates become innovators in the space.

The rigorous curriculum emphasizes mathematical and statistical modeling, machine learning, computational systems and data management.

The program is directed by Dr. Ted Stohr, a recognized thought leader in the information systems, operations and business process management arenas.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $39,408 per year

University at Buffalo – Buffalo, New York PhD Computational and Data-Enabled Science and Engineering

The curriculum for the University of Buffalo’s PhD in computational and data-enabled science and engineering centers around three areas: data science, applied mathematics and numerical methods, and high performance and data intensive computing. 9 credit course of courses must be completed in each of these three areas. Altogether, the program consists of 72 credit hours, and should be completed in 4-5 years. A master’s degree is required for admission; courses taken during the master’s may be able to count toward some of the core coursework requirements.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $11,310 per year (New York Resident), $23,100 per year (Non-resident)

University of Colorado Denver – Denver, Colorado PhD in Big Data Science and Engineering

The University of Colorado – Denver offers a unique program for those students who have already received admission to the computer science and information systems PhD program.

The Big Data Science and Engineering (BDSE) program is a PhD fellowship program that allows selected students to pursue research in the area of big data science and engineering. This new fellowship program was created to train more computer scientists in data science application fields such as health informatics, geosciences, precision and personalized medicine, business analytics, and smart cities and cybersecurity.

Students in the doctoral program must complete 30 credit hours of computer science classes beyond a master’s level, and 30 credit hours of dissertation research.

The BDSE fellowship requires students to have an advisor both in the core disciplines (either computer science or mathematics and statistics) as well as an advisor in the application discipline (medicine and public health, business, or geosciences).

In addition, the fellowship covers full stipend, tuition, and fees up to ~50k for BDSE fellows annually. Important eligibility requirements can be found here.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $55,260 total

University of Marylan d  – College Park, Maryland PhD in Information Studies

Data science is a potential research area for doctoral candidates in information studies at the University of Maryland – College Park. This includes big data, data analytics, and data mining.

Applicants for the PhD must have taken the following courses in undergraduate studies: programming languages, data structures, design and analysis of computer algorithms, calculus I and II, and linear algebra.

Students must complete 6 qualifying courses, 2 elective graduate courses, and at least 12 credit hours of dissertation research.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $16,238 total (Maryland Resident), $35,388 total (Non-resident)

University of Massachusetts Boston  – Boston, Massachusetts PhD in Business Administration – Information Systems for Data Science Track

The University of Massachusetts – Boston offers a PhD in information systems for data science. As this is a business degree, students must complete coursework in their first two years with a focus on data for business; for example, taking courses such as business in context: markets, technologies, and societies.

Students must take and pass qualifying exams at the end of year 1, comprehensive exams at the end of year 2, and defend their theses at the end of year 4.

Those with a degree in statistics, economics, math, computer science, management sciences, information systems, and other related fields are especially encouraged, though a quantitative degree is not necessary.

Students accepted by the program are ordinarily offered full tuition credits and a stipend ($25,000 per year) to cover educational expenses and help defray living costs for up to three years of study.

During the first two years of coursework, they are assigned to a faculty member as a research assistant; for the third year students will be engaged in instructional activities. Funding for the fourth year is merit-based from a limited pool of program funds

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $18,894 total (in-state), $36,879 (out-of-state)

University of Nevada Reno – Reno, Nevada PhD in Statistics and Data Science

The University of Nevada – Reno’s doctoral program in statistics and data science is comprised of 72 credit hours to be completed over the course of 4-5 years. Coursework is all within the scope of statistics, with titles such as statistical theory, probability theory, linear models, multivariate analysis, statistical learning, statistical computing, time series analysis.

The completion of a Master’s degree in mathematics or statistics prior to enrollment in the doctoral program is strongly recommended, but not required.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $5,814 total (in-state), $22,356 (out-of-state)

University of Southern California – Los Angles, California PhD in Data Sciences & Operations

USC Marshall School of Business offers a PhD in data sciences and operations to be completed in 5 years.

Students can choose either a track in operations management or in statistics. Both tracks require 4 courses in fall and spring of the first 2 years, as well as a research paper and courses during the summers. Year 3 is devoted to dissertation preparation and year 4 and/or 5 to dissertation defense.

A bachelor’s degree is necessary for application, but no field or further experience is required.

Students should complete 60 units of coursework. If the students are admitted with Advanced Standing (e.g., Master’s Degree in appropriate field), this requirement may be reduced to 40 credits.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $63,468 total

University of Tennessee-Knoxville  – Knoxville, Tennessee The Data Science and Engineering PhD

The data science and engineering PhD at the University of Tennessee – Knoxville requires 36 hours of coursework and 36 hours of dissertation research. For those entering with an MS degree, only 24 hours of course work is required.

The core curriculum includes work in statistics, machine learning, and scripting languages and is enhanced by 6 hours in courses that focus either on policy issues related to data, or technology entrepreneurship.

Students must also choose a knowledge specialization in one of these fields: health and biological sciences, advanced manufacturing, materials science, environmental and climate science, transportation science, national security, urban systems science, and advanced data science.

Applicants must have a bachelor’s or master’s degree in engineering or a scientific field. 

All students that are admitted will be supported by a research fellowship and tuition will be included.

Many students will perform research with scientists from Oak Ridge national lab, which is located about 30 minutes drive from campus.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $11,468 total (Tennessee Resident), $29,656 total (Non-resident)

University of Vermont – Burlington, Vermont Complex Systems and Data Science (CSDS), PhD

Through the College of Engineering and Mathematical Sciences, the Complex Systems and Data Science (CSDS) PhD program is pan-disciplinary and provides computational and theoretical training. Students may customize the program depending on their chosen area of focus.

Students in this program work in research groups across campus.

Core courses include Data Science, Principles of Complex Systems and Modeling Complex Systems. Elective courses include Machine Learning, Complex Networks, Evolutionary Computation, Human/Computer Interaction, and Data Mining.

The program requires at least 75 credits to graduate with approval by the student graduate studies committee.

Delivery Method: Campus GRE: Not Required 2022-2023 Tuition: $12,204 total (Vermont Resident), $30,960 total (Non-resident)

University of Washington Seattle Campus – Seattle, Washington PhD in Big Data and Data Science

The University of Washington’s PhD program in data science has 2 key goals: training of new data scientists and cyberinfrastructure development, i.e., development of open-source tools and services that scientists around the world can use for big data analysis.

Students must take core courses in data management, machine learning, data visualization, and statistics.

Students are also required to complete at least one internship that covers practical work in big data.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $17,004 per year (Washington resident), $30,477 (non-resident)

University of Wisconsin-Madison – Madison, Wisconsin PhD in Biomedical Data Science

The PhD program in Biomedical Data Science offered by the Department of Biostatistics and Medical Informatics at UW-Madison is unique, in blending the best of statistics and computer science, biostatistics and biomedical informatics. 

Students complete three year-long course sequences in biostatistics theory and methods, computer science/informatics, and a specialized sequence to fit their interests.

Students also complete three research rotations within their first two years in the program, to both expand their breadth of knowledge and assist in identifying a research advisor.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $10,728 total (in-state), $24,054 total (out-of-state)

Vanderbilt University – Nashville, Tennessee Data Science Track of the BMI PhD Program

The PhD in biomedical informatics at Vanderbilt has the option of a data science track.

Students complete courses in the areas of biomedical informatics (3 courses), computer science (4 courses), statistical methods (4 courses), and biomedical science (2 courses). Students are expected to complete core courses and defend their dissertations within 5 years of beginning the program.

Applicants must have a bachelor’s degree in computer science, engineering, biology, biochemistry, nursing, mathematics, statistics, physics, information management, or some other health-related field.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $53,160 per year

Washington University in St. Louis – St. Louis, Missouri Doctorate in Computational & Data Sciences

Washington University now offers an interdisciplinary Ph.D. in Computational & Data Sciences where students can choose from one of four tracks (Computational Methodologies, Political Science, Psychological & Brain Sciences, or Social Work & Public Health).

Students are fully funded and will receive a stipend for at least five years contingent on making sufficient progress in the program.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $59,420 total

Worcester Polytechnic Institute – Worcester, Massachusetts PhD in Data Science

The PhD in data science at Worcester Polytechnic Institute focuses on 5 areas: integrative data science, business intelligence and case studies, data access and management, data analytics and mining, and mathematical analysis.

Students first complete a master’s in data science, and then complete 60 credit hours beyond the master’s, including 30 credit hours of research.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $28,980 per year

Yale University – New Haven, Connecticut PhD Program – Department of Stats and Data Science

The PhD in statistics and data science at Yale University offers broad training in the areas of statistical theory, probability theory, stochastic processes, asymptotics, information theory, machine learning, data analysis, statistical computing, and graphical methods. Students complete 12 courses in the first year in these topics.

Students are required to teach one course each semester of their third and fourth years.

Most students complete and defend their dissertations in their fifth year.

Applicants should have an educational background in statistics, with an undergraduate major in statistics, mathematics, computer science, or similar field.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $46,900 total

• Related Programs

NYU Center for Data Science

Harnessing Data’s Potential for the World

PhD in Data Science

An NRT-sponsored program in Data Science

• Areas & Faculty
• Admission Requirements
• Medical School Track
• NRT FUTURE Program

Advances in computational speed and data availability, and the development of novel data analysis methods, have birthed a new field: data science. This new field requires a new type of researcher and actor: the rigorously trained, cross-disciplinary, and ethically responsible data scientist. Launched in Fall 2017, the pioneering CDS PhD Data Science program seeks to produce such researchers who are fluent in the emerging field of data science, and to develop a native environment for their education and training. The CDS PhD Data Science program has rapidly received widespread recognition and is considered among the top and most selective data science doctoral programs in the world. It has recently been recognized by the NSF through an NRT training grant.

The CDS PhD program model rigorously trains data scientists of the future who (1) develop methodology and harness statistical tools to find answers to questions that transcend the boundaries of traditional academic disciplines; (2) clearly communicate to extract crisp questions from big, heterogeneous, uncertain data; (3) effectively translate fundamental research insights into data science practice in the sciences, medicine, industry, and government; and (4) are aware of the ethical implications of their work.

Our programmatic mission is to nurture this new generation of data scientists, by designing and building a data science environment where methodological innovations are developed and translated successfully to domain applications, both scientific and social. Our vision is that combining fundamental research on the principles of data science with translational projects involving domain experts creates a virtuous cycle: Advances in data science methodology transform the process of discovery in the sciences, and enable effective data-driven governance in the public sector. At the same time, the demands of real-world translational projects will catalyze the creation of new data science methodologies. An essential ingredient of such methodologies is that they embed ethics and responsibility by design.

These objectives will be achieved by a combination of an innovative core curriculum, a novel data assistantship mechanism that provides training of skills transfer through rotations and internships, and communication and entrepreneurship modules. Students will be exposed to a wider range of fields than in more standard PhD programs while working with our interdisciplinary faculty. In particular, we are proud to offer a medical track for students eager to explore data science as applied to healthcare or to develop novel theoretical models stemming from medical questions.

In short, the CDS PhD Data Science program prepares students to become leaders in data science research and prepares them for outstanding careers in academia or industry. Successful candidates are guaranteed financial support in the form of tuition and a competitive stipend in the fall and spring semesters for up to five years.* We invite you to learn more through our webpage or by contacting  [email protected] .

*The Ph.D. program also offers students the opportunity to pursue their study and research with Data Science faculty based at NYU Shanghai. With this opportunity, students generally complete their coursework in New York City before moving full-time to Shanghai for their research. For more information, please visit the NYU Shanghai Ph.D. page .

U.S. News Releases 2024 Best Graduate Programs Rankings

Find the top-ranked graduate schools in business, education, law, nursing and other fields.

U.S. News Ranks Best Graduate Schools

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To help prospective graduate students find a school that fits their needs, U.S. News released the 2024 rankings for multiple graduate fields.

Depending on the job or field, earning a graduate degree may lead to higher earnings, career advancement and specialized skill development.

But with several types of degrees and hundreds of graduate schools, it can be difficult to narrow down the options. To help prospective graduate students find a school that fits their needs, U.S. News released its 2024 Best Graduate Schools rankings today. They evaluate business, education, fine arts, health, law, library studies, nursing, public affairs, science, and social sciences and humanities graduate programs. Medical school and engineering rankings are not being released at this time.

A notable methodology change includes a new salary indicator based on profession in the business rankings.

Additionally, for the first time in four years, there are new rankings for a blend of doctoral and master's programs in audiology, occupational therapy, physical therapy, pharmacy, nurse midwifery and speech-language pathology. Graduate programs in nurse anesthesia and social work are also ranked for the first time since 2016 and 2022, respectively. Those and other specialty rankings are based on reputation ratings from scholars at other surveyed schools.

Read each program's specific methodology for the most detailed explanations of all the changes. The rankings are one source of information among many that prospective college students can use to inform their college decision. Below is a summary of the top-ranked schools in four major graduate program areas:

Best Law Schools

Best business schools, best nursing schools, best education schools.

Among the top 10 law schools . Yale Law School in Connecticut and California-based  Stanford Law School shared the top spot again. The  University of Chicago Law School in Illinois maintained its No. 3 rank, followed by a four-way tie at No. 4: Duke University School of Law in North Carolina, Harvard Law School in Massachusetts, the University of Pennsylvania Carey Law School and the University of Virginia School of Law .

Columbia Law School in New York ranked No. 8 again, while there was a three-way tie for No. 9: New York University School of Law , Northwestern University's Pritzker School of Law in Illinois and the University of Michigan—Ann Arbor Law School .

Looking beyond the top 10, multiple law schools moved up in the rankings. William & Mary Law School in Virginia, for instance, jumped nine spots from a tie at No. 45 to a five-way tie at No. 36.

U.S. News also ranked 13 law specialties: business/corporate, clinical training, constitutional, contracts/commercial, criminal, dispute resolution, environmental, health care, intellectual property, international, legal writing, tax and trial advocacy. (You can filter by specialty on the  main ranking page .)

Meanwhile, in the  part-time law school rankings – which consists of law schools with at least 20 part-time students enrolled in fall 2022 and fall 2023 – the top three stayed the same. The  Georgetown University Law Center in Washington, D.C., is once again at the top while D.C.-based  George Washington University Law School , now No. 3, switched places with the  Fordham University School of Law in New York City, which claimed second place.

Previously ranked at No. 3 and No. 6 respectively, the University of Pennsylvania's Wharton School and Stanford Graduate School of Business took the top spot in this year's full-time MBA program rankings . Northwestern's Kellogg School of Management and the University of Chicago's Booth School of Business moved down from their previous places in the top two to tie at No. 3.

While the top 10 mostly consists of the same schools as last year, both the Haas School of Business at the University of California, Berkeley and the University of Virginia's Darden School of Business joined those ranks this year. UC Berkeley rose from a three-way tie at No. 11 to a three-way tie at No. 7, while UVA moved up four spots from No. 14 to a tie at No. 10.

Farther down the full-time MBA rankings, there were some big changes. For example, Pitt's Joseph M. Katz Graduate School of Business soared 39 spots from a tie at No. 86 to a tie at No. 47.

Meanwhile, the very top of the part-time MBA rankings looks similar to last year, with the same schools in the top 5: UChicago, UC Berkeley, Northwestern, NYU's Leonard N. Stern School of Business and the Anderson School of Management at the University of California—Los Angeles. But UChicago took the No. 1 spot from UC Berkeley this year.

Moving up from No. 2, Johns Hopkins University School of Nursing in Maryland tied with Emory University's Nell Hodgson Woodruff School of Nursing in Georgia to claim the top spot in this year's nursing master's program rankings. Duke University School of Nursing in North Carolina climbed up by one to claim the third spot.

Johns Hopkins ranked No. 1, as it did last year, in the Doctor of Nursing Practice program rankings. George Mason University School of Nursing in Virginia – which reported more graduates and resources per faculty – soared from a four-way tie at No. 39 to take the No. 2 spot. Duke tied with the University of Washington School of Nursing to round out the top three.

Duke also ranked No. 1 in all of the ranked nursing master's nursing practice specialties, including administration, family, both acute and primary care adult gerontology, and mental health.

Once again, Teachers College, Columbia University in New York was No. 1 in the graduate education schools rankings. This year, however, it tied with the University of Wisconsin—Madison School of Education , which climbed two spots.

The University of Michigan—Ann Arbor's School of Education dropped from the top position to tie with the UCLA School of Education and Information Studies at No. 3. UCLA was previously tied at No. 7.

U.S. News also ranks nine education specialties, with the College of Education at Michigan State University claiming the top spot in the following categories: curriculum and instruction, educational administration, elementary teacher education, higher education administration and secondary teacher education.

Searching for a grad school of education? Access our  complete rankings  of Best Graduate Schools.

Grad Degree Jobs With $100K+ Salaries

Tags: students , graduate schools , medical school , business school , law school , education graduate school , engineering graduate school , MBAs , nursing programs

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Data science phd salary in california.

How much does a Data Science Phd make in California? The salary range for a Data Science Phd job is from $126,932 to $160,746 per year in California. Click on the filter to check out Data Science Phd job salaries by hourly, weekly, biweekly, semimonthly, monthly, and yearly.

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• Escalon, CA
• Escondido, CA
• Esparto, CA
• Fair Oaks, CA
• Fairfax, CA
• Fairfield, CA
• Fall River Mills, CA
• Fallbrook, CA
• Farmersville, CA
• Farmington, CA
• Fawnskin, CA
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• Ferndale, CA
• Fiddletown, CA
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• Fillmore, CA
• Firebaugh, CA
• Fish Camp, CA
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• Floriston, CA
• Flournoy, CA
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• Foothill Ranch, CA
• Forbestown, CA
• Forest Falls, CA
• Forest Knolls, CA
• Forest Ranch, CA
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• Fort Bidwell, CA
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• Goodyears Bar, CA
• Granada Hills, CA
• San Juan Bautista, CA
• San Juan Capistrano, CA
• San Leandro, CA
• San Lorenzo, CA
• San Lucas, CA
• San Luis Obispo, CA
• San Luis Rey, CA
• San Marcos, CA
• San Marino, CA
• San Martin, CA
• San Mateo, CA
• San Miguel, CA
• San Pablo, CA
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• San Quentin, CA
• San Rafael, CA
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• Greenbrae, CA
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• Grenada, CA
• Gridley, CA
• Grizzly Flats, CA
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• Gustine, CA
• Hacienda Heights, CA
• Half Moon Bay, CA
• Hamilton City, CA
• Hanford, CA
• Happy Camp, CA
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• Huntington Beach, CA
• Huntington Park, CA
• Hyampom, CA
• Hydesville, CA
• Idyllwild, CA
• Imperial, CA
• Imperial Beach, CA
• Independence, CA
• Santa Barbara, CA
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• Santa Cruz, CA
• Santa Fe Springs, CA
• Santa Margarita, CA
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• Santa Paula, CA
• Santa Rita Park, CA
• Santa Rosa, CA
• Santa Ynez, CA
• Santa Ysabel, CA
• Saratoga, CA
• Sausalito, CA
• Scott Bar, CA
• Scotts Valley, CA
• Seal Beach, CA
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• Seiad Valley, CA
• Sequoia National Park, CA
• Shafter, CA
• Shandon, CA
• Shasta Lake, CA
• Indian Wells, CA
• Inglewood, CA
• Inverness, CA
• Inyokern, CA
• Isleton, CA
• Ivanhoe, CA
• Jackson, CA
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• June Lake, CA
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• Kirkwood, CA
• Kit Carson, CA
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• Klamath River, CA
• Kneeland, CA
• Knights Landing, CA
• Knightsen, CA
• La Canada Flintridge, CA
• La Crescenta, CA
• La Grange, CA
• La Habra, CA
• La Honda, CA
• La Jolla, CA
• La Mesa, CA
• La Mirada, CA
• La Palma, CA
• La Puente, CA
• Shaver Lake, CA
• Sheridan, CA
• Sherman Oaks, CA
• Shingle Springs, CA
• Shingletown, CA
• Shoshone, CA
• Sierra City, CA
• Sierra Madre, CA
• Sierraville, CA
• Signal Hill, CA
• Silverado, CA
• Simi Valley, CA
• Skyforest, CA
• Sloughhouse, CA
• Smartsville, CA
• Smith River, CA
• Snelling, CA
• Soda Springs, CA
• Solana Beach, CA
• Soledad, CA
• Solvang, CA
• Somerset, CA
• Somes Bar, CA
• Soulsbyville, CA
• South Dos Palos, CA
• South El Monte, CA
• South Gate, CA
• South Lake Tahoe, CA
• South Pasadena, CA
• South San Francisco, CA
• Spreckels, CA
• Spring Valley, CA
• Springville, CA
• Squaw Valley, CA
• Standard, CA
• Standish, CA
• Stanford, CA
• Stanton, CA
• Stevenson Ranch, CA
• Stevinson, CA
• Stewarts Point, CA
• Stinson Beach, CA
• Stirling City, CA
• Stockton, CA
• Stonyford, CA
• Storrie, CA
• Stratford, CA
• Strathmore, CA
• La Quinta, CA
• La Verne, CA
• Ladera Ranch, CA
• Lafayette, CA
• Laguna Beach, CA
• Laguna Hills, CA
• Laguna Niguel, CA
• Laguna Woods, CA
• Lagunitas, CA
• Lake Arrowhead, CA
• Lake City, CA
• Lake Elsinore, CA
• Lake Forest, CA
• Lake Hughes, CA
• Lake Isabella, CA
• Lakehead, CA
• Lakeport, CA
• Lakeshore, CA
• Lakeside, CA
• Lakewood, CA
• Lancaster, CA
• Landers, CA
• Larkspur, CA
• Lathrop, CA
• Lawndale, CA
• Laytonville, CA
• Le Grand, CA
• Lee Vining, CA
• Leggett, CA
• Lemon Cove, CA
• Lemon Grove, CA
• Lemoore, CA
• Lewiston, CA
• Lincoln, CA
• Lincoln Acres, CA
• Lindsay, CA
• Litchfield, CA
• Little Lake, CA
• Little River, CA
• Littlerock, CA
• Live Oak, CA
• Livermore, CA
• Livingston, CA
• Lockeford, CA
• Lockwood, CA
• Loma Linda, CA
• Loma Mar, CA
• Lone Pine, CA
• Long Barn, CA
• Long Beach, CA
• Strawberry, CA
• Strawberry Valley, CA
• Studio City, CA
• Sugarloaf, CA
• Suisun City, CA
• Sultana, CA
• Summerland, CA
• Sun City, CA
• Sun Valley, CA
• Sunland, CA
• Sunnyvale, CA
• Sunset Beach, CA
• Surfside, CA
• Susanville, CA
• Sutter Creek, CA
• Tahoe City, CA
• Tahoe Vista, CA
• Talmage, CA
• Tarzana, CA
• Taylorsville, CA
• Tehachapi, CA
• Temecula, CA
• Temple City, CA
• Templeton, CA
• Terra Bella, CA
• The Sea Ranch, CA
• Thermal, CA
• Thornton, CA
• Thousand Oaks, CA
• Thousand Palms, CA
• Three Rivers, CA
• Tollhouse, CA
• Toluca Lake, CA
• Tomales, CA
• Topanga, CA
• Torrance, CA
• Trabuco Canyon, CA
• Tranquillity, CA
• Travis Afb, CA
• Tres Pinos, CA
• Trinidad, CA
• Trinity Center, CA
• Truckee, CA
• Tujunga, CA
• Lookout, CA
• Los Alamitos, CA
• Los Alamos, CA
• Los Altos, CA
• Los Angeles, CA
• Tulelake, CA
• Tuolumne, CA
• Turlock, CA
• Twain Harte, CA
• Twentynine Palms, CA
• Twin Bridges, CA
• Twin Peaks, CA
• Union City, CA
• Universal City, CA
• Upper Lake, CA
• Vacaville, CA
• Valencia, CA
• Vallecito, CA
• Vallejo, CA
• Valley Center, CA
• Valley Ford, CA
• Valley Springs, CA
• Valley Village, CA
• Valyermo, CA
• Van Nuys, CA
• Ventura, CA
• Verdugo City, CA
• Vernalis, CA
• Victorville, CA
• Villa Grande, CA
• Villa Park, CA
• Vineburg, CA
• Visalia, CA
• Volcano, CA
• Wallace, CA
• Walnut Creek, CA
• Los Banos, CA
• Los Gatos, CA
• Los Molinos, CA
• Los Olivos, CA
• Los Osos, CA
• Lost Hills, CA
• Lower Lake, CA
• Loyalton, CA
• Lucerne, CA
• Lucerne Valley, CA
• Lynwood, CA
• Lytle Creek, CA
• Macdoel, CA
• Mad River, CA
• Madeline, CA
• Madison, CA
• Magalia, CA
• Mammoth Lakes, CA
• Manchester, CA
• Manhattan Beach, CA
• Manteca, CA
• March Air Reserve Base, CA
• Maricopa, CA
• Marina Del Rey, CA
• Mariposa, CA
• Markleeville, CA
• Marshall, CA
• Martell, CA
• Martinez, CA
• Marysville, CA
• Maxwell, CA
• Maywood, CA
• Mc Farland, CA
• Mc Kittrick, CA
• Mcarthur, CA
• Mcclellan, CA
• Mccloud, CA
• Mckinleyville, CA
• Meadow Valley, CA
• Meadow Vista, CA
• Mendocino, CA
• Mendota, CA
• Menifee, CA
• Walnut Grove, CA
• Warner Springs, CA
• Washington, CA
• Waterford, CA
• Watsonville, CA
• Waukena, CA
• Weaverville, CA
• West Covina, CA
• West Hills, CA
• West Hollywood, CA
• West Point, CA
• West Sacramento, CA
• Westlake Village, CA
• Westley, CA
• Westminster, CA
• Westmorland, CA
• Westport, CA
• Westwood, CA
• Wheatland, CA
• Whiskeytown, CA
• Whitethorn, CA
• Whitewater, CA
• Whitmore, CA
• Whittier, CA
• Wildomar, CA
• Williams, CA
• Willits, CA
• Willow Creek, CA
• Willows, CA
• Wilmington, CA
• Wilseyville, CA
• Winchester, CA
• Windsor, CA
• Winnetka, CA
• Winterhaven, CA
• Winters, CA
• Witter Springs, CA
• Wofford Heights, CA
• Woodacre, CA
• Woodbridge, CA
• Woodlake, CA
• Woodland, CA
• Woodland Hills, CA
• Wrightwood, CA
• Yorba Linda, CA
• Yorkville, CA
• Yosemite National Park, CA
• Yountville, CA
• Yuba City, CA
• Menlo Park, CA
• Yucaipa, CA
• Yucca Valley, CA
• Mentone, CA
• Meridian, CA
• Mi Wuk Village, CA
• Middletown, CA
• Midpines, CA
• Midway City, CA
• Milford, CA
• Mill Creek, CA
• Mill Valley, CA
• Millbrae, CA
• Millville, CA
• Milpitas, CA
• Mineral, CA
• Mira Loma, CA
• Miramonte, CA
• Miranda, CA
• Mission Hills, CA
• Mission Viejo, CA
• Moccasin, CA
• Modesto, CA
• Mokelumne Hill, CA
• Mono Hot Springs, CA
• Monrovia, CA
• Montague, CA
• Montara, CA
• Montclair, CA
• Monte Rio, CA
• Montebello, CA
• Monterey, CA
• Monterey Park, CA
• Montgomery Creek, CA
• Montrose, CA
• Moorpark, CA
• Moreno Valley, CA
• Morgan Hill, CA
• Morongo Valley, CA
• Morro Bay, CA
• Moss Beach, CA
• Moss Landing, CA
• Mount Aukum, CA
• Mount Hamilton, CA
• Mount Hermon, CA
• Mount Laguna, CA
• Mount Shasta, CA
• Mount Wilson, CA
• Mountain Center, CA
• Mountain Pass, CA
• Mountain Ranch, CA
• Mountain View, CA
• Adelanto, CA
• Agoura Hills, CA
• Aguanga, CA
• Ahwahnee, CA
• Alameda, CA
• Alderpoint, CA
• Alhambra, CA
• Aliso Viejo, CA
• Alleghany, CA
• Alpaugh, CA
• Altadena, CA
• Altaville, CA
• Alturas, CA
• Amador City, CA
• American Canyon, CA
• Anaheim, CA
• Anderson, CA
• Angels Camp, CA
• Angelus Oaks, CA
• Annapolis, CA
• Antelope, CA
• Antioch, CA
• Mt Baldy, CA
• Murphys, CA
• Murrieta, CA
• Myers Flat, CA
• National City, CA
• Navarro, CA
• Needles, CA
• Nevada City, CA
• New Almaden, CA
• New Cuyama, CA
• Newberry Springs, CA
• Newbury Park, CA
• Newcastle, CA
• Newhall, CA
• Newport Beach, CA
• Newport Coast, CA
• Nicasio, CA
• Nicolaus, CA
• North Fork, CA
• North Highlands, CA
• North Hills, CA
• North Hollywood, CA
• North Palm Springs, CA
• North San Juan, CA
• Northridge, CA
• Norwalk, CA
• Nubieber, CA
• O Neals, CA
• Oak Park, CA
• Oak Run, CA
• Oak View, CA
• Oakdale, CA
• Oakhurst, CA
• Oakland, CA
• Apple Valley, CA
• Oakville, CA
• Occidental, CA
• Oceanside, CA
• Ocotillo, CA
• Olancha, CA
• Old Station, CA
• Olivehurst, CA
• Olympic Valley, CA
• Ontario, CA
• Orange Cove, CA
• Orangevale, CA
• Oregon House, CA
• Orleans, CA
• Oro Grande, CA
• Oroville, CA
• Pacific Grove, CA
• Pacific Palisades, CA
• Pacifica, CA
• Pacoima, CA
• Paicines, CA
• Palermo, CA
• Palm Desert, CA
• Palm Springs, CA
• Palmdale, CA
• Palo Alto, CA
• Palo Cedro, CA
• Applegate, CA
• Arbuckle, CA
• Arcadia, CA
• Arroyo Grande, CA
• Artesia, CA
• Atascadero, CA
• Atherton, CA
• Atwater, CA
• Auberry, CA
• Avila Beach, CA
• Bakersfield, CA
• Baldwin Park, CA
• Ballico, CA
• Banning, CA
• Barstow, CA
• Bass Lake, CA
• Bayside, CA
• Beale Afb, CA
• Beaumont, CA
• Beckwourth, CA
• Bell Gardens, CA
• Bella Vista, CA
• Bellflower, CA
• Belmont, CA
• Belvedere Tiburon, CA
• Ben Lomond, CA
• Benicia, CA
• Berkeley, CA
• Berry Creek, CA
• Bethel Island, CA
• Beverly Hills, CA
• Palo Verde, CA
• Palomar Mountain, CA
• Palos Verdes Peninsula, CA
• Panorama City, CA
• Paradise, CA
• Paramount, CA
• Parker Dam, CA
• Parlier, CA
• Pasadena, CA
• Paskenta, CA
• Paso Robles, CA
• Patterson, CA
• Pauma Valley, CA
• Paynes Creek, CA
• Pearblossom, CA
• Pebble Beach, CA
• Penn Valley, CA
• Penngrove, CA
• Pescadero, CA
• Petaluma, CA
• Petrolia, CA
• Phillipsville, CA
• Pico Rivera, CA
• Piedmont, CA
• Pilot Hill, CA
• Pine Grove, CA
• Pine Mountain Club, CA
• Pine Valley, CA
• Pinecrest, CA
• Pinon Hills, CA
• Pioneer, CA
• Pioneertown, CA
• Pismo Beach, CA
• Pittsburg, CA
• Placentia, CA
• Placerville, CA
• Planada, CA
• Platina, CA
• Playa Del Rey, CA
• Playa Vista, CA
• Pleasant Grove, CA
• Big Bar, CA
• Big Bear City, CA
• Big Bear Lake, CA
• Big Bend, CA
• Big Creek, CA
• Big Oak Flat, CA
• Big Pine, CA
• Big Sur, CA
• Birds Landing, CA
• Blairsden Graeagle, CA
• Blocksburg, CA
• Bloomington, CA
• Blue Jay, CA
• Blue Lake, CA
• Bodega Bay, CA
• Bodfish, CA
• Bolinas, CA
• Bonsall, CA
• Boonville, CA
• Borrego Springs, CA
• Boulder Creek, CA
• Boulevard, CA
• Boyes Hot Springs, CA
• Bradley, CA
• Brandeis, CA
• Branscomb, CA
• Brawley, CA
• Brentwood, CA
• Bridgeport, CA
• Bridgeville, CA
• Brisbane, CA
• Brookdale, CA
• Browns Valley, CA
• Brownsville, CA
• Bryn Mawr, CA
• Buellton, CA
• Buena Park, CA
• Burbank, CA
• Burlingame, CA
• Burnt Ranch, CA
• Butte City, CA
• Buttonwillow, CA
• Cabazon, CA
• Calabasas, CA
• Calexico, CA
• Caliente, CA
• California City, CA
• California Hot Springs, CA
• Calimesa, CA
• Calipatria, CA
• Calistoga, CA
• Callahan, CA
• Calpella, CA
• Calpine, CA
• Camarillo, CA
• Pleasant Hill, CA
• Pleasanton, CA
• Plymouth, CA
• Point Arena, CA
• Point Mugu Nawc, CA
• Point Reyes Station, CA
• Pollock Pines, CA
• Pope Valley, CA
• Port Costa, CA
• Port Hueneme, CA
• Port Hueneme Cbc Base, CA
• Porter Ranch, CA
• Porterville, CA
• Portola, CA
• Portola Valley, CA
• Potrero, CA
• Potter Valley, CA
• Prather, CA
• Princeton, CA
• Proberta, CA
• Rackerby, CA
• Rail Road Flat, CA
• Raisin City, CA
• Ranchita, CA
• Rancho Cordova, CA
• Rancho Cucamonga, CA
• Rancho Mirage, CA
• Rancho Palos Verdes, CA
• Rancho Santa Fe, CA
• Rancho Santa Margarita, CA
• Randsburg, CA
• Ravendale, CA
• Raymond, CA
• Red Bluff, CA
• Red Mountain, CA
• Redcrest, CA
• Redding, CA
• Redlands, CA
• Redondo Beach, CA
• Redwood City, CA
• Redwood Estates, CA
• Redwood Valley, CA
• Reedley, CA
• Represa, CA
• Richgrove, CA
• Richmond, CA
• Richvale, CA
• Ridgecrest, CA
• Rimforest, CA
• Cambria, CA
• Camp Meeker, CA
• Camp Nelson, CA
• Camp Pendleton, CA
• Campbell, CA
• Campo Seco, CA
• Camptonville, CA
• Canoga Park, CA
• Cantua Creek, CA
• Canyon Country, CA
• Canyon Dam, CA
• Capistrano Beach, CA
• Capitola, CA
• Cardiff By The Sea, CA
• Carlotta, CA
• Carlsbad, CA
• Carmel By The Sea, CA
• Carmel Valley, CA
• Carmichael, CA
• Carnelian Bay, CA
• Carpinteria, CA
• Caruthers, CA
• Casmalia, CA
• Castaic, CA
• Castella, CA
• Castro Valley, CA
• Castroville, CA
• Cathedral City, CA
• Catheys Valley, CA
• Cayucos, CA
• Cazadero, CA
• Cedar Glen, CA
• Cedar Ridge, CA
• Cedarpines Park, CA
• Cedarville, CA
• Cerritos, CA
• Challenge, CA
• Chatsworth, CA
• Chester, CA
• Chicago Park, CA
• Chilcoot, CA
• Chinese Camp, CA
• Chino Hills, CA
• Chowchilla, CA
• Chualar, CA
• Chula Vista, CA
• Rio Dell, CA
• Rio Linda, CA
• Rio Nido, CA
• Rio Oso, CA
• Rio Vista, CA
• River Pines, CA
• Riverbank, CA
• Riverdale, CA
• Riverside, CA
• Robbins, CA
• Rocklin, CA
• Rohnert Park, CA
• Rosamond, CA
• Rosemead, CA
• Roseville, CA
• Rough And Ready, CA
• Round Mountain, CA
• Rowland Heights, CA
• Running Springs, CA
• Rutherford, CA
• Sacramento, CA
• Citrus Heights, CA
• City Of Industry, CA
• Claremont, CA
• Clarksburg, CA
• Clayton, CA
• Clearlake, CA
• Clearlake Oaks, CA
• Clearlake Park, CA
• Clements, CA
• Clipper Mills, CA
• Cloverdale, CA
• Coachella, CA
• Coalinga, CA
• Coarsegold, CA
• Coleville, CA
• Columbia, CA
• Comptche, CA
• Compton, CA
• Concord, CA
• Copperopolis, CA
• Corcoran, CA
• Corning, CA
• Corona Del Mar, CA
• Coronado, CA
• Corte Madera, CA
• Costa Mesa, CA
• Cottonwood, CA
• Coulterville, CA
• Courtland, CA
• Crescent City, CA
• Crescent Mills, CA
• Cressey, CA
• Crest Park, CA
• Crestline, CA
• Creston, CA
• Crockett, CA
• Crows Landing, CA
• Culver City, CA
• Cupertino, CA
• Cypress, CA
• Daggett, CA
• Daly City, CA
• Saint Helena, CA
• Salinas, CA
• Salton City, CA
• San Andreas, CA
• San Anselmo, CA
• San Ardo, CA
• San Bernardino, CA
• San Bruno, CA
• San Carlos, CA
• Dana Point, CA
• San Clemente, CA
• Danville, CA
• Davenport, CA
• Davis Creek, CA
• Death Valley, CA
• Deer Park, CA
• Del Mar, CA
• Del Rey, CA
• Descanso, CA
• Desert Center, CA
• Desert Hot Springs, CA
• Diamond Bar, CA
• Diamond Springs, CA
• Dillon Beach, CA
• Discovery Bay, CA
• Dobbins, CA
• Dodgertown, CA
• Dos Palos, CA
• Dos Rios, CA
• Douglas City, CA
• Douglas Flat, CA
• Downieville, CA
• Drytown, CA
• Dulzura, CA
• Duncans Mills, CA
• Dunnigan, CA
• Dunsmuir, CA
• Dutch Flat, CA
• Eagleville, CA
• Earlimart, CA
• East Irvine, CA
• Echo Lake, CA
• Edwards, CA
• El Cajon, CA
• El Centro, CA
• El Cerrito, CA
• El Dorado, CA
• El Dorado Hills, CA
• El Granada, CA
• El Monte, CA
• El Nido, CA
• El Portal, CA
• El Segundo, CA
• El Sobrante, CA
• El Toro, CA
• El Verano, CA
• Eldridge, CA
• Elk Creek, CA
• Elk Grove, CA
• Elverta, CA
• Emeryville, CA
• Emigrant Gap, CA
• Encinitas, CA
• San Diego, CA
• San Dimas, CA
• San Fernando, CA
• San Francisco, CA

HOW SHOULD YOU USE THIS DATA?

Employees: Use this as a guide for salary expectations, but be aware that responsibilities can change across companies for the same job title, so there may be differences between this data, other free site and our subscription products bought by employers.

Employers: This data could be used as reference point in your market pricing, but not the only source, due to differences across jobs locations, and sizes of companies.Consider additional sources like our Employer reported data.

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Highest Paying Cities for Data Science Phd in California

States with higher salaries for data science phd, how much do similar professions get paid in california, how much should you be earning, what does a data science phd do.

The issue that some data science PhD holders are reporting, especially in industry settings, is that that the state of the art is moving so quickly, and that the data science industry is evolving so rapidly, that an abundance of research oriented expertise is not always what’s heavily sought after.

Historically, data science PhD programs were one of the main avenues to get a good data-related position in academia or industry.

The guide will walk through some of the key considerations while picking graduate data science programs and some of the nuts and bolts (like course load and tuition costs) that are part of the data science PhD decision-making process.

At the end of a day, whether a data science PhD is worth it will be entirely dependent upon your personal interests and career goals.

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Most Common Benefits for Data Science Phd

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PhD in Data Science

• Updated on  
• Dec 6, 2023

If you’re interested in working independently in research fields after completing a master’s in mathematics, statistics , science, etc, then pursuing a PhD may be the right choice for you. It offers many reputed career choices along with satisfaction. The field is continuously growing and top companies Microsoft, Wipro, HCL, etc, are demanding data science graduates. So, read about PhD in Data Science to know about the course, its duration, after options, salary, top recruiting companies, and more.

This Blog Includes:

What is a phd in data science, eligibility criteria, list of top universities offering phd in data science abroad, universities offering phd in data science in india, researchers , college professors , senior data scientist, business analysts, machine learning professionals, average salary for phd graduates, top companies hiring data science graduates.

A PhD in Data Science gives students an edge and provides them with relevant knowledge in the fields like AI, Statistics, Machine Learning , Data Analysis, and the list goes on. Like all other PhD-level courses, it enables students to work independently in research fields and helps them apply both their practical and theoretical knowledge in data models.

Also Read: Fully-Funded PhD Programs

A PhD in Data Science has emerged as one of the leading courses as it enhances the career prospects of the candidates providing them with research and in-depth academic knowledge. Different universities have different specific criteria for admission that has to be met by the candidates. The basic eligibility requirements are given below:

• Candidates willing to pursue a PhD in Data Science and related courses must have a master’s degree in related fields like computer science, Statistics, Mathematics, etc
• Some universities have their own entrance exam for PhD level courses
• Marks scored by the candidate in related entrance examinations such as GATE, UGC-NET, CSIR-UGC-NET, etc are also considered for the admission process and these candidates are exempted from giving university entrance exams. But these candidates have to undertake a research skill assessment test. (Only valid for Indian Universities)
• Some colleges also take personal interviews and written tests in shortlisting candidates.
• English test scores of exams like IELTS / TOEFL etc (Only valid for foreign universities)
• Candidates require a set of documents like a 10th Marksheet, 12th Marksheet, Bachelor’s Degree & Marksheets, and Master’s Degree & Marksheet.

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When applying abroad it’s always wise to know your options beforehand! Sometimes choosing the right university for you might look like a daunting task.

The following are the top universities in India offering PhD in Data science:

Must Read: How to Write Acknowledgement for Dissertation?

Career Choice after PhD in Data Science

The following are the top career choices after PhD in Data Science:

Researchers conduct their research in an organized and systematic way. They are responsible for communicating their research honestly and ethically. Also, they transfer and exploit their knowledge for the betterment of employees, the environment, and society as a whole and also collaborate with others whenever necessary. 

A data science college professor is expected to design, develop and teach courses in the Data Science domain and related areas. Sometimes, they engage in exploring new methodologies and technologies through research. Also, they mentor and supervise students in their projects and internships too.

A senior data scientist engages in formulating, suggesting, and managing data-driven projects which are in favour of business interests. They also collate and clean data from various entities in order to help their junior data scientists . 

Business analysts help organizations in improving their processes and systems. They conduct research and analysis to solve business problems and introduce solutions to businesses and their clients. 

A machine learning professional is an expert in using data to train models. These models are used to automate processes like image classification, market forecasting, speech recognition etc. 

The average salary in Data Science after doing a PhD can range from Rs 70-75 Lakhs per annum. With more experience, it can be raised to Rs 1 Crore or more. Although it depends on what career path you are choosing and in which state or country you are employed.

Must Read: What is a PhD Stipend?

• Thomson Reuters 

Yes definitely! You can prove to be a leader in the field by doing a PhD in Data Science. And also, a PhD in Data Science also has a higher return on investment as compared to other data science master’s programs.

A PhD in Data Science takes around 4-5 years depending on the university and country you’re pursuing from.

Researcher, college professor, machine learning professional, senior data scientist, and business analyst are some of the positions you can opt for after completing your PhD in Data Science. 

We hope this blog helps you in understanding all the essential aspects of PhD in Data Science. Aiming to study a similar course or thinking of doing a PhD in another field? Sign up for your free 30 minutes of e-counselling with the Leverage Edu team now!

Team Leverage Edu

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