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Statistics and Data Science

Wharton’s phd program in statistics and data science provides the foundational education that allows students to engage both cutting-edge theory and applied problems. these include theoretical research in mathematical statistics as well as interdisciplinary research in the social sciences, biology and computer science..

Wharton’s PhD program in Statistics and Data Science provides the foundational education that allows students to engage both cutting-edge theory and applied problems. These include problems from a wide variety of fields within Wharton, such as finance, marketing, and public policy, as well as fields across the rest of the University such as biostatistics within the Medical School and computer science within the Engineering School.

Major areas of departmental research include:

• analysis of observational studies;
• Bayesian inference, bioinformatics;
• decision theory;
• game theory;
• high dimensional inference;
• information theory;
• machine learning;
• model selection;
• nonparametric function estimation; and
• time series analysis.

Students typically have a strong undergraduate background in mathematics. Knowledge of linear algebra and advanced calculus is required, and experience with real analysis is helpful. Although some exposure to undergraduate probability and statistics is expected, skills in mathematics and computer science are more important. Graduates of the department typically take positions in academia, government, financial services, and bio-pharmaceutical industries.

For information on courses and sample plan of study, please visit the University Graduate Catalog .

Get the Details.

Visit the Statistics and Data Science website for details on program requirements and courses. Read faculty and student research and bios to see what you can do with a Statistics PhD.

Statistics and Data Science Doctoral Coordinator 

Dr. Bhaswar Bhattacharya Associate Professor of Statistics and Data Science Associate Professor of Mathematics (secondary appointment) Email: [email protected] Phone: 215-573-0535

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In recent years, getting a Ph.D. in statistics online has turned into an attractive option for individuals seeking advanced education opportunities. The flexibility and convenience that online programs offer allow students to obtain a prestigious degree without leaving their homes.

Whether you are considering a career in academia, research, or industry, an online Ph.D. in Statistics can open doors to exciting opportunities. Let’s take a closer look at getting an online Ph.D. in statistics and choosing the best program for your needs.

The duration of an online Ph.D. program in statistics can vary depending on several factors, including:

• The program structure – some programs have accelerated full-study options while others accommodate part-time studies.
• The student’s prior qualifications – the more knowledge in the statistics field you have before you start the studies, the faster you are likely to complete the program,
• The place of study – different universities and colleges may require a different number of hours to complete the program.

On average, completing a Ph.D. in statistics online can take anywhere from 3 to 6 years. However, different programs may have different requirements and timelines.

Some programs offer accelerated options, while others provide an opportunity for part-time study. The latter allows students to customize their learning timelines based on their individual circumstances. To determine the specific duration of a program, make sure to review the requirements and curriculum of each program on your list of candidates.

Benefits of Getting a Ph.D. in Statistics Online

Many students worry that getting a Ph.D. in statistics online is less beneficial than doing it on campus. However, by the time you decide to get a Ph.D., you are likely to have a day job and multiple family responsibilities. That’s why an online program may seem like an excellent choice.

In reality, the quality of offline and online Ph.D. programs in the same university doesn’t differ at all. You get all the same learning opportunities online as you would offline.

One of the primary advantages of pursuing an online Ph.D. in Statistics is the flexibility it offers. Online programs allow students to study at their own pace and schedule. This can accommodate work and personal commitments.

This flexibility is particularly beneficial for individuals who wish to keep their jobs while pursuing their degrees or for those who have family responsibilities.

Obtaining a Ph.D. in Statistics online allows for a better study-life balance. Students can manage their studies alongside their professional and personal responsibilities. Meanwhile, they don’t have to worry about relocating or disrupting their current lifestyle. This balance can significantly reduce stress levels and enhance overall well-being.

By opting for an online Ph.D. program, students are not limited to educational institutions within their geographical location.

They have the freedom to choose from a wider range of reputable universities and colleges in their home countries or beyond the border. This opens up opportunities to learn from distinguished faculty members and engage with diverse cohorts of students anywhere in the world.

Choosing to pursue a Ph.D. in statistics online can also result in fewer expenses compared to on-campus programs. Online students can reduce commuting costs, housing expenses, and other associated fees.

If you decide to pursue a Ph.D. in statistics online, there is no need to move to another city or state. Students can pursue their degrees from the comfort of their own homes. This eliminates the need to relocate to a different city or state. This can be particularly advantageous for individuals who have established careers or family commitments.

Contrary to common misconceptions, online Ph.D. programs in statistics provide ample opportunities for students to interact with faculty members. Through virtual classrooms, video conferences, and email communication, students can easily connect with their professors, seek guidance, and engage in academic discussions.

This accessibility to faculty allows students to receive the necessary support and mentorship throughout their doctoral journey.

When applying for an online Ph.D. program in statistics, you need to monitor the admission requirements closely. These may vary slightly between institutions. Some of them may include:

• A master’s degree in statistics or a related field – most programs require applicants to have a relevant master’s degree to make sure they have a solid foundation in statistical theory and methodology. However, a Bachelor’s degree in mathematics can be acceptable for some universities.
• Transcripts –  applicants must submit official transcripts from all previously attended institutions to demonstrate their academic performance and suitability for the program. The GPA should be at least 3.0. Some schools demand 3.4 and higher.
• Letters of recommendation – typically, applicants are required to provide letters of recommendation from academic or professional references who can confirm the student’s abilities and speak for their desire to continue the learning process.
• Statement of purpose – a well-written statement of purpose outlining the applicant’s research interests, career goals, and reasons for pursuing a Ph.D. in statistics.
• GRE scores – some programs may require applicants to demonstrate high GRE scores
• CV or resume – be ready to provide a well-written CV or resume
• English proficiency tests – if English isn’t your native language, the school may demand to see your TOEFL or IELTS scores.

If you can’t meet all admission requirements stated by the university, don’t give up. Many schools allow students to apply anyway. You would likely need to write an essay explaining why you can be an excellent asset to the program, and how the program can help you succeed.

How to Choose the Best Program for Getting a Ph.D. in Statistics Online

Choosing the right program for pursuing a Ph.D. in statistics online is crucial to ensure a successful and fulfilling academic journey. Here are a few things to pay attention to when choosing the best online Ph.D. in statistics program for your needs.

Before applying to any Ph.D. program in statistics, review the admission requirements thoroughly. Check that you meet the prerequisites, such as:

• Holding a bachelor’s or master’s degree in a related field
• Submitting official transcripts
• Obtaining letters of recommendation
• Writing a compelling statement of purpose

Pay attention to the additional requirements, such as GRE or TOEFL scores.

Consider whether you prefer a fully online program or a hybrid option that combines online coursework with some on-campus components.

A fully remote program offers for maximum flexibility, allowing you to study from anywhere at any time. Meanwhile, a hybrid program may provide opportunities for face-to-face interactions with faculty and peers during seminars, conferences, or research collaborations. Evaluate your personal preferences and commitments to determine which mode of learning suits you best.

Review the credit requirements of each program you are considering. Some programs may have a fixed curriculum with a specific number of credits, while others offer flexibility in choosing elective courses.

Check the number of credits required to complete the program and make sure it suits your purposes and timeframe. Additionally, consider the availability of part-time or accelerated options if you need to tailor your studies to fit your schedule.

To verify the quality and credibility of the program and the institution offering it, you need to check the school’s accreditation.

Look for programs that are accredited by recognized accrediting bodies that include:

• The Higher Learning Commission (HLC)
• Accrediting Commission for Senior Colleges and Universities (ACSCU),
• Distance Education Accrediting Commission (DEAC)

Some accreditation organizations can confirm the university’s quality of studies on the national level. Other organizations are regional.

Accreditation shows that the program meets certain educational standards and that your degree will be recognized by employers and other academic institutions.

When evaluating the accreditation status, also consider the reputation of the university or institution offering the program. Look for institutions with a strong track record in offering online education and a solid reputation in the field of statistics.

By carefully considering the admission requirements, program structure, and accreditation, you can make an informed decision about the best program for your Ph.D. in statistics online. Remember to prioritize your goals, preferences, and academic needs.

The Best Online Ph.D. in Statistics Programs

These universities offer robust online doctorate programs in statistics. By reviewing their requirements and credentials, you can make the best choice for your needs.

Located in Minneapolis, Minnesota, North Central University has a robust online Ph.D. in data science program with a specialization in Statistics. You will learn advanced statistical analysis and explore research methodologies.

The estimated time of completing the program is 40 months. This school has affordable tuition options that make high-quality programs accessible to a wide variety of students.

The school is accredited by the Western Association of Schools and Colleges.

Located in Phoenix, Arizona, this largest Christian University in the world has several high-quality online Ph.D. programs . One of them is in data analytics with the focus on statistics. To complete the program, you would need 60 credits.

During your studies at Grand Canyon, you’ll hone your data collection, preparation, and management skills. Several courses focus on analyzing quantitative data and producing research findings and results.

The school is accredited by the Higher Learning Commission.

Located in California (the school has campuses in different cities), National University allows students to complete their Ph.D. studies online. You can get a Ph.D. in Data Science with a focus on statistics.

The school is accredited by WASC Senior College and University Commission (WSCUC).

Frequently Asked Questions About Getting a Ph.D. in Statistics Online:

Yes, a Ph.D. in statistics offers excellent career prospects, as it is a highly sought-after qualification in various industries, including finance, healthcare, and technology.

Yes, there are numerous reputable universities and institutions offering online Ph.D. programs in statistics.

Pursuing a Ph.D. in statistics online provides the flexibility to balance academic commitments with a day job. This flexibility makes it possible for many individuals to continue working while studying.

The Takeaway

An online Ph.D. program in statistics offers flexibility, a better work-life balance, and a wider choice of educational institutions.

By carefully considering admission requirements, program structure, and accreditation, you can select the best program for your needs.

With a Ph.D. in statistics, you can take advantage of excellent career opportunities and make significant contributions to the field. You can take advantage of the above list to choose the program that aligns with your goals.

PHD in Economics, Associate Professor, Department of Business Process Management, Faculty of Market Technologies IOM

Ph.D. in Statistics

Our doctoral program in statistics gives future researchers preparation to teach and lead in academic and industry careers.

Program Description

Degree type.

approximately 5 years

The relatively new Ph.D. in Statistics strives to be an exemplar of graduate training in statistics. Students are exposed to cutting edge statistical methodology through the modern curriculum and have the opportunity to work with multiple faculty members to take a deeper dive into special topics, gain experience in working in interdisciplinary teams and learn research skills through flexible research electives. Graduates of our program are prepared to be leaders in statistics and machine learning in both academia and industry.

The Ph.D. in Statistics is expected to take approximately five years to complete, and students participate as full-time graduate students.  Some students are able to finish the program in four years, but all admitted students are guaranteed five years of financial support.  

Within our program, students learn from global leaders in statistics and data sciences and have:

20 credits of required courses in statistical theory and methods, computation, and applications

18 credits of research electives working with two or more faculty members, elective coursework (optional), and a guided reading course

Dissertation research

Coursework Timeline

Year 1: focus on core learning.

The first year consists of the core courses:

• SDS 384.2 Mathematical Statistics I
• SDS 383C Statistical Modeling I
• SDS 387 Linear Models
• SDS 384.11 Theoretical Statistics
• SDS 383D Statistical Modeling II
• SDS 386D Monte Carlo Methods

In addition to the core courses, students of the first year are expected to participate in SDS 190 Readings in Statistics. This class focuses on learning how to read scientific papers and how to grasp the main ideas, as well as on practicing presentations and getting familiar with important statistics literature.

At the end of the first year, students are expected to take a written preliminary exam. The examination has two purposes: to assess the student’s strengths and weaknesses and to determine whether the student should continue in the Ph.D. program. The exam covers the core material covered in the core courses and it consists of two parts: a 3-hour closed book in-class portion and a take-home applied statistics component. The in-class portion is scheduled at the end of the Spring Semester after final exams (usually late May). The take-home problem is distributed at the end of the in-class exam, with a due-time 24 hours later. 

Year 2: Transitioning from Student to Researcher

In the second year of the program, students take the following courses totaling 9 credit hours each semester:

• Required: SDS 190 Readings in Statistics (1 credit hour)
• Required: SDS 389/489 Research Elective* (3 or 4 credit hours) in which the student engages in independent research under the guidance of a member of the Statistics Graduate Studies Committee
• One or more elective courses selected from approved electives ; and/or
• One or more sections of SDS 289/389/489 Research Elective* (2 to 4 credit hours) in which the student engages in independent research with a member(s) of the Statistics Graduate Studies Committee OR guided readings/self-study in an area of statistics or machine learning. 
• Internship course (0 or 1 credit hour; for international students to obtain Curricular Practical Training; contact Graduate Coordinator for appropriate course options)
• GRS 097 Teaching Assistant Fundamentals or NSC 088L Introduction to Evidence-Based Teaching (0 credit hours; for TA and AI preparation)

* Research electives allow students to explore different advising possibilities by working for a semester with a particular professor. These projects can also serve as the beginning of a dissertation research path. No more than six credit hours of research electives can be taken with a single faculty member in a semester.

Year 3: Advance to Candidacy

Students are encouraged to attend conferences, give presentations, as well as to develop their dissertation research. At the end of the second year or during their third year, students are expected to present their plan of study for the dissertation in an Oral candidacy exam. During this exam, students should demonstrate their research proficiency to their Ph.D. committee members. Students who successfully complete the candidacy exam can apply for admission to candidacy for the Ph.D. once they have completed their required coursework and satisfied departmental requirements. The steps to advance to candidacy are:

• Discuss potential candidacy exam topics with advisor
• Propose Ph.D. committee: the proposed committee must follow the Graduate School and departmental regulations on committee membership for what will become the Ph.D. Dissertation Committee
•   Application for candidacy

Year 4+: Dissertation Completion and Defense

Students are encouraged to attend conferences, give presentations, as well as to develop their dissertation research. Moreover, they are expected to present part of their work in the framework of the department's Ph.D. poster session.

Students who are admitted to candidacy will be expected to complete and defend their Ph.D. thesis before their Ph.D. committee to be awarded the degree. The final examination, which is oral, is administered only after all coursework, research and dissertation requirements have been fulfilled. It is expected that students will be prepared to defend by the end of their fifth year in the doctoral program.

General Information and Expectations for All Ph.D. students

• 2023-24 Student Handbook
• Annual Review At the end of every spring semester, students in their second year and beyond are expected to fill out an annual review form distributed by the Graduate Program Administrator. 
• Seminar Series All students are expected to attend the SDS Seminar Series
• SDS 189R Course Description (when taken for internship)
• Internship Course Registration form
• Intel Corporation
• Berry Consultants

Attending Conferences 

Students are encouraged to attend conferences to share their work. All research-related travel while in student status require prior authorization.

• Request for Travel Authorization (both domestic and international travel)
• Request for Authorization for International Travel  

Statistics & Data Science

Dietrich college of humanities and social sciences, ph.d. programs, our ph.d. programs enable students to pursue a wide range of research opportunities, including constructing and implementing advanced methods of data analysis to address crucial cross-disciplinary questions, along with developing the fundamental theory that supports these methods..

Unique opportunities for our Ph.D. students include:

• We host four cross-disciplinary joint Ph.D. programs for students who want to specialize in machine learning , public policy , neuroscience , and the link between engineering and policy .
• Our faculty have deep involvement in a range of important, data-rich scientific collaborations, including in the areas of genetics, neuroscience, astronomy, and the social sciences. This allows students to have easy access to both the crucial questions in these fields, and to the data that can provide the answers.
• Students begin work on their Advanced Data Analysis Project in the second semester. This year-long, faculty/student collaboration, distinct from the thesis, provides an immediate intensive research experience.
• Carnegie Mellon is home to the first Machine Learning Department . Many of our faculty maintain joint appointments with this Department and they (and our students) have strong connections to this exciting and growing area of research.

The programs leading to the degree of   Doctor of Philosophy in Statistics   seek to strike a balance between theoretical and applied statistics. The Ph.D. program prepares students for university teaching and research careers, and for industrial and governmental positions involving research in new statistical methods. Four to five years are usually needed to complete all requirements for the Ph.D. degree.

These pages present the requirements for each of our Ph.D. programs.

The page   "Core Ph.D. Requirements"   lays out the requirements for all Ph.D. students, while each of the four joint programs are described under the Joint Ph.D. Degrees pages. Our Ph.D. students can also earn a   Master of Science in Statistics   as an intermediate step towards their ultimate goal.

Joint Ph.D. Programs

Statistics/machine learning, statistics/public policy, statistics/engineering and public policy, statistics/neural computation  .

Doctoral Program

Program summary.

Students are required to

• master the material in the prerequisite courses ;
• pass the first-year core program;
• attempt all three parts of the qualifying examinations and show acceptable performance in at least two of them (end of 1st year);
• satisfy the depth and breadth requirements (2nd/3rd/4th year);
• successfully complete the thesis proposal meeting and submit the Dissertation Reading Committee form (winter quarter of the 3rd year);
• present a draft of their dissertation and pass the university oral examination (4th/5th year).

The PhD requires a minimum of 135 units. Students are required to take a minimum of nine units of advanced topics courses (for depth) offered by the department (not including literature, research, consulting or Year 1 coursework), and a minimum of nine units outside of the Statistics Department (for breadth). Courses for the depth and breadth requirements must equal a combined minimum of 24 units. In addition, students must enroll in STATS 390 Statistical Consulting, taking it at least twice.

All students who have passed the qualifying exams but have not yet passed the Thesis Proposal Meeting must take STATS 319 at least once each year. For example, a student taking the qualifying exams in the summer after Year 1 and having the dissertation proposal meeting in Year 3, would take 319 in Years 2 and 3. Students in their second year are strongly encouraged to take STATS 399 with at least one faculty member. All details of program requirements can be found in our PhD handbook (available to Stanford affiliates only, using Stanford authentication. Requests for access from non-affiliates will not be approved).

Statistics Department PhD Handbook

All students are expected to abide by the Honor Code and the Fundamental Standard .

Doctoral and Research Advisors

During the first two years of the program, students' academic progress is monitored by the department's Graduate Director. Each student should meet at least once a quarter with the Graduate Director to discuss their academic plans and their progress towards choosing a thesis advisor (before the final study list deadline of spring of the second year). From the third year onward students are advised by their selected advisor.

Qualifying Examinations

Qualifying examinations are part of most PhD programs in the United States. At Stanford these exams are intended to test the student's level of knowledge when the first-year program, common to all students, has been completed. There are separate examinations in the three core subjects of statistical theory and methods, applied statistics, and probability theory, which are typically taken during the summer at the end of the student's first year. Students are expected to attempt all three examinations and show acceptable performance in at least two of them. Letter grades are not given. Qualifying exams may be taken only once. After passing the qualifying exams, students must file for Ph.D. Candidacy, a university milestone, by the end of spring quarter of their second year.

While nearly all students pass the qualifying examinations, those who do not can arrange to have their financial support continued for up to three quarters while alternative plans are made. Usually students are able to complete the requirements for the M.S. degree in Statistics in two years or less, whether or not they have passed the PhD qualifying exams.

Thesis Proposal Meeting and Dissertation Reading Committee 

The thesis proposal meeting is intended to demonstrate a student's depth in some areas of statistics, and to examine the general plan for their research. In the meeting the student gives a 60-minute presentation involving ideas developed to date and plans for completing a PhD dissertation, and for another 60 minutes answers questions posed by the committee. which consists of their advisor and two other members. The meeting must be successfully completed by the end of winter quarter of the third year. If a student does not pass, the exam must be repeated. Repeated failure can lead to a loss of financial support.

The Dissertation Reading Committee consists of the student’s advisor plus two faculty readers, all of whom are responsible for reading the full dissertation. Of these three, at least two must be members of the Statistics Department (faculty with a full or joint appointment in Statistics but excluding for this purpose those with only a courtesy or adjunct appointment). Normally, all committee members are members of the Stanford University Academic Council or are emeritus Academic Council members; the principal dissertation advisor must be an Academic Council member. 

The Doctoral Dissertation Reading Committee form should be completed and signed at the Dissertation Proposal Meeting. The form must be submitted before approval of TGR status or before scheduling a University Oral Examination.

 For further information on the Dissertation Reading Committee, please see the Graduate Academic Policies and Procedures (GAP) Handbook section 4.8.

University Oral Examinations

The oral examination consists of a public, approximately 60-minute, presentation on the thesis topic, followed by a 60 minute question and answer period attended only by members of the examining committee. The questions relate to the student's presentation and also explore the student's familiarity with broader statistical topics related to the thesis research. The oral examination is normally completed during the last few months of the student's PhD period. The examining committee typically consists of four faculty members from the Statistics Department and a fifth faculty member from outside the department serving as the committee chair. Four out of five passing votes are required and no grades are given. Nearly all students can expect to pass this examination, although it is common for specific recommendations to be made regarding completion of the thesis.

The Dissertation Reading Committee must also read and approve the thesis.

For further information on university oral examinations and committees, please see the Graduate Academic Policies and Procedures (GAP) Handbook section 4.7 .

Dissertation

The dissertation is the capstone of the PhD degree. It is expected to be an original piece of work of publishable quality. The research advisor and two additional faculty members constitute the student's dissertation reading committee.

10 Best Online PhD in Data Science Programs [2024 Guide]

If you have a passion for mining information from large amounts of data, you should consider exploring PhD Data Science online programs.

Editorial Listing ShortCode:

Furthering your education in this field can help take your career to the next level. By earning your PhD, you may increase not only your knowledge but also your salary.

Universities Offering Online Data Science Doctorate Degree Programs

Methodology: The following school list is in alphabetical order. To be included, a college or university must be regionally accredited and offer degree programs online or in a hybrid format. In addition, the schools offer online data science programs .

1. Capella University

Founded in 1993, private Capella University offers online doctorate, master’s, and bachelor’s degrees. The Minneapolis-based school’s 38,000 enrolled students represent 50 states and 61 countries. Doctoral students account for more than 27 percent of Capella University’s student body.

• DBA in Business Intelligence – Data Analytics

Capella University is accredited by the Higher Learning Commission.

2. Capitol Technology University

Capitol Technology University is a private university located near the nation’s capital in South Laurel, Maryland. Established in 1927, the university now offers undergraduate and master’s programs in business, computer science, cybrsecurity, and engineering.

Capitol Technology University is a military-friendly school founded by a Navy veteran. It holds the prestigious SC Media Award for Best Cybersecurity Higher Education Program. The school’s annual enrollment is approximately 850 students.

• PhD in Business Analytics and Data Science

Capitol Technology University  is accredited by the Middle States Commission on Higher Education.

3. Colorado Technical University

Colorado Technical University was founded in 1965. This private university offers undergraduate, graduate, and doctoral degrees in business management and technology.

The school has earned the U.S. News & World Report “Best for Veterans” designation, the Council of College and Military Educators (CCME) Institution Award, and recognition as a center of Academic Excellence in Information Assurance and Cyber Defense from the NSA and Department of Homeland Security.

Annual enrollment stands at around 26,000 students.

• Doctor of Computer Science – Big Data Analytics

Colorado Technical University  is accredited by the Higher Learning Commission.

4. Columbia University

New York City’s Columbia University is a private Ivy League research university founded in 1754. It stands today as the oldest university in New York City. Columbia operates four undergraduate schools and 15 graduate/professional schools.

Bachelor’s, master’s, and PhD programs covering business, medicine, liberal arts, technology, and political science are available. Student enrollment at Columbia stands at roughly 33,413.

• PhD in Data Science

Columbia  is accredited by the Middle States Commission on Higher Education.

5. Grand Canyon University

Grand Canyon University is a private Christian college based in Phoenix, Arizona. With a student enrollment of 70,000 students, it is considered to be the world’s largest Christian university.

Grand Canyon University offers bachelor’s, master’s, and doctoral degrees in business, education, health sciences, liberal arts, and nursing. The school offers a total of 200 academic programs throughout its nine colleges.

• DBA in Data Analytics

Grand Canyon University is accredited by the Higher Learning Commission.

6. Harrisburg University of Science and Technology

Founded in 2001, Harrisburg University of Science and Technology is a STEM-focused institution with campuses in Harrisburg and Philadelphia.

This private university offers bachelor’s degrees, master’s degrees, doctoral degrees, and certificate programs. The nearly 6,000 students enrolled study degree paths related to applied science and technology.

• PhD in Data Sciences

Harrisburg University of Science and Technology is accredited by the Middle States Commission on Higher Education.

7. Indiana University-Purdue University Indianapolis

Indiana University-Purdue University Indianapolis is a public research university offering more than 225 options for bachelor’s, master’s, and doctoral degrees across 18 different schools. The university’s campus is based in Indianapolis, Indiana.

The more than 30,000 students enrolled pursue degrees in majors like art and design, business, education, engineering, law, liberal arts, medicine, nursing, and social work.

• PhD in Data Science (on-campus)

Indiana University – Purdue University Indianapolis  is accredited by the Higher Learning Commission.

8. National University

National University is a network of nonprofit educational institutions that is headquartered in San Diego, California. It offers a range of bachelor’s degrees, master’s degrees, doctoral degrees, and certificates in business, education, marriage and family therapy, psychology, and technology.

NU has over 30,000 students enrolled and more than 220,000 alumni from around the world.

National University is accredited by the Western Association of Schools and Colleges.

9. Stevens Institute of Technology

Located in Hoboken, New Jersey, Stevens Institute of Technology is a private research institution with an enrollment of approximately 6,125 students. Founded in 1870, the school has been named among the “Best Value Colleges” by the Princeton Review.

Additionally, the Princeton Review ranks Stevens Institute of Technology among its “Top 15 for Internships.” The school’s undergraduate and graduate students represent 47 states and 60 countries. Students can pursue bachelor’s, master’s, doctoral, and certificate programs.

Stevens Institute of Technology is accredited by the Middle States Commission on Higher Education.

10. University of Central Florida

Located along Orlando’s Space Coast, the University of Central Florida is a public research university with a student enrollment of approximately 69,525. It offers bachelor’s, master’s, and doctoral programs.

Students can pursue degrees in arts and humanities, business, engineering, computer science, health science, medicine, and nursing. The University of Central Florida has been ranked as a “Best Southeastern College” by the Princeton Review.

• PhD in Big Data Analytics

The  University of Central Florida  is accredited by the Southern Association of Colleges and Schools Commission on Colleges.

Online PhD in Data Science Programs

Data science is exactly what it sounds like – the study of data. Data scientists look at sets of data and notice patterns that emerge. They identify key information that data presents which may not seem readily apparent at first.

If you are someone that notices the small details while also keeping an eye on the bigger picture, a career in data science may be right for you. If you find trends and patterns in large amounts of data, you may be well-suited for this field.

What kind of job can you expect to have as a data scientist? In the last few years, Glassdoor has continuously ranked data scientist as one of the best jobs to have in the United States. The options for specific jobs are numerous and varied.

For example, one data scientist may work as a statistician and interpret statistical information for the U.S. Department of Agriculture. Another data scientist may be a business intelligence developer for Discover, creating strategies for businesses to make more informed decisions about their company.

Data Science Pros and Cons

As with any financial and length time investment, you should consider both the pros and the cons of earning your PhD in an online data science program.

All salary data in this table was provided by the Bureau of Labor Statistics.

Choosing to pursue an online PhD in a data science program is decision that must be taken into careful consideration, but there are many benefits to completing a program.

Data Science Curriculum & Courses

Curriculum for data science programs is heavily focused on analysis and research. Examples of courses offered by universities like Dakota State University and the University of North Texas are listed below.

• Information Systems – This course is designed to help students learn about the role information systems have for businesses and other organizations.
• Applied Statistics – This class teaches how to use statistical software to study data samples and make inferences based on the data presented.
• Project and Change Management – This class is designed to help students learn the underlying principles for managing information systems and how to utilize software for project management.
• Technology for Mobile Devices – Students in this course study the process of developing apps for mobile devices like smartphones and tablets.
• Advanced Network Technology and Management – This class helps students learn how to work with a model network environment, including how to find solutions for problems with the network.
• Seminar in Research and Research Methodology – Students in this seminar are asked to develop a research proposal and participate in a research study.
• Knowledge Management Tools and Technologies – This course introduces students to a variety of technologies including those associated with knowledge management and IT infrastructure.
• Seminar in Communication and Use of Information – This class explores the roles communication plays at various levels in society.
• Readings in Information Science – Students in this class study texts which emphasize methodological and theoretical issues.
• Medical Geography – In this course, students study the correlation between location and health care and work on their own projects.

Exploring the curriculum offered by different universities can help you determine which online PhD program is best suited for your interests and your needs.

Data Science PhD Admissions

Before applying for a PhD program, you will want to ensure that you have all the application materials on hand, including the commonly required materials listed below.

• Reference letters – You should request these documents well before your application deadline as mentors may not be able to honor a last-minute request due to time constraints.
• All transcripts – These grades will include both undergraduate and graduate level courses.
• Letter of intent – Be prepared to explain in writing why you want to enroll in the program and what you plan to do after its completion.
• Application fee – Fees to cover administrative costs of reviewing your application can add up, so make sure to budget for the costs of each one.
• Resume – Schools want to know your background in not just education but in the job market as well.
• Specific program application – Your prospective school will most likely have its own unique application on its official website.

Save yourself the stress of anxiously waiting to receive documents from an institution or mentor in time and compile them well ahead of the due date.

Data Science PhD Careers & Salaries

According to the U.S. Bureau of Labor Statistics , computer and information research scientists earned a median of $131,490 a year. Data scientists as a group earn increasingly high salaries in various industries including research laboratories, government departments, and a variety of companies focused on technology.

Some of the top companies that utilize data scientists are IBM, Amazon, Microsoft, Facebook, Oracle, Google, and Apple. These multi-billion dollar companies are consistently hiring data scientists to interpret the large amounts of data, or “big data,” that is collected via their services.

Data scientists can expect to work in roles where job duties include designing data models, organizing data from multiple sources, and identifying trends in data.

Data scientists use a comprehensive process for gathering and analyzing information including asking questions, acquiring data, storing data, using models to interpret it, and presenting their findings to stakeholders in the community.

According to the Bureau of Labor Statistics, some careers in the data science field include:

Whatever the job title, data scientists continually earn a significant amount more than employees in other fields.

Data Science Accreditation

Before clicking the “submit” button on your application to a PhD program, you will want to ensure that the university you are applying to is accredited, meaning it is recognized as a legitimate program that offers quality coursework and research opportunities.

If you decide to apply to a program related to computer technology or engineering, the Accreditation Board for Engineering and Technology (ABET) determine which schools offer suitable coursework and requirements for these fields. Also be sure that your prospective university is regionally accredited, the gold-standard for accreditation in the United States.

Search on your prospective schools’ website for information regarding their accreditation status. You will want to ensure that the schools you apply to are regionally accredited so you can get the most out of your PhD experience and your credits will be more likely to transfer should you switch schools while studying.

Data Science Professional Organizations

Joining a professional organization can help to advance your career by connecting you with other individuals who work in the same field.

Professional organizations offer a multitude of benefits, including networking opportunities (which may help to connect you with future employers), and they can also provide inspiration for completing your PhD program, decreasing feelings of isolation that can accompany students.

• Association for Information Science and Technology – This organization states its role “advances the information sciences and similar applications of information technology by helping members build their skills and [develop] their careers” via several different ways, including training and education.
• Association of Information Technology Professionals – This agency gives members advice on how to pursue certain career paths while also providing discounts on certifications and resources for professional development.
• International Association for Social Science Information Services and Technology – IASSIST has 300 members from countries around the world. They offer resources for professionals from sectors such as non-profits, academia, and government.

While some organizations may have a yearly membership fee, the potential gains for job opportunities and professional development through these groups can easily offset those costs.

Financial Aid

Across the nation, the average cost of obtaining a PhD online is between $4,000 and $20,000.  As a student in a PhD program, you can expect to have costs from tuition, books, personal supplies, transportation, etc. Without the time or energy for a full-time or often, even part-time job, you should explore all financial aid options available.

Financial aid for PhD students can come in the form of loans, scholarships, and grants. Grants and scholarships typically do not have to be paid back, but loans are borrowed money which may accrue interest and should be a last resort for students.

Some specific scholarships and grants are designed with scientists, including data scientists, in mind. For example, the National Science Foundation Graduate Research Fellowship is designed to support students who are pursuing research-based doctoral degrees.

Previous recipients include Nobel Prize winners, a U.S. Secretary of Energy, and the founder of Google.

Another common source of money comes from taking on teaching assistant positions within your university or becoming an assistant lecturer. Both positions are great for gaining experience teaching in your academic department while generating income to offset the costs incurred from your years of study.

How long does it take to get a PhD in data science?

It takes an average of 71 credits to complete a PhD in data science. On top of this, students may also have responsibilities to research and/or teach, which can make the process take even longer.

It is not unusual for some PhD programs to take anywhere from four to five years to complete.

Is a PhD in data science worth it?

Whether or not a PhD in data science is “worth it” depends on a number of factors. Do you have the time available for next few years (possibly longer) to invest in this opportunity? Are you motivated enough to complete coursework while also on a shoestring budget?

Search for employment positions you are interested in and take a look at the education requirements employers are requesting. These factors may effect your decision in potentially pursuing an online masters in data science instead.

Can I do a PhD in data science?

Whether or not you complete a PhD in data science depends on your ability to stay focused and motivated. PhD programs are notoriously intensive, and they are not for everyone.

You should have a better reason for applying to a program than simply not knowing what to do in today’s job market.

Getting Your PhD in Data Science Online

Obtaining your doctoral degree in data science is not an easy task, but it is also not an impossible one. If you are serious about pursuing your PhD, talk to experts in the field. The admissions departments at prospective universities can help put you in touch with recruiters who can give you more information about the program.

Joining a professional organization can help you connect with individuals who are working in the field, many of whom will have obtained their higher education degree. With careful planning and the right information to make informed career choices, you can further your education and your sense of accomplishment.

PhD in Statistics

Program description.

The Ph.D. program in statistics prepares students for a career pursuing research in either academia or industry.  The program provides rigorous classroom training in the theory, methodology, and application of statistics, and provides the opportunity to work with faculty on advanced research topics over a wide range of theory and application areas. To enter, students need a bachelor’s degree in mathematics, statistics, or a closely related discipline. Students graduating with a PhD in Statistics are expected to:

• Demonstrate an understanding the core principles of Probability Theory, Estimation Theory, and Statistical Methods.
• Demonstrate the ability to conduct original research in statistics.
• Demonstrate the ability to present research-level statistics in a formal lecture

Requirements for the Ph.D. (Statistics Track)

Course Work A Ph.D. student in our department must complete sixteen courses for the Ph.D. At most, four of these courses may be transferred from another institution. If the Ph.D. student is admitted to the program at the post-Master’s level, then eight courses are usually required.

Qualifying Examinations First, all Ph.D. students in the statistics track must take the following two-semester sequences: MA779 and MA780 (Probability Theory I and II), MA781 (Estimation Theory) and MA782 (Hypothesis Testing), and MA750 and MA751 (Advanced Statistical Methods I and II). Then, to qualify a student to begin work on a PhD dissertation, they must pass two of the following three exams at the PhD level: probability, mathematical statistics, and applied statistics. The probability and mathematical statistics exams are offered every September and the applied statistics exam is offered every April.

• PhD Exam in Probability: This exam covers the material covered in MA779 and MA780 (Probability Theory I and II).
• PhD Exam in Mathematical Statistics: This exam covers material covered in MA781 (Estimation Theory) and MA782 (Hypothesis Testing).
• PhD Exam in Applied Statistics: This exam covers the same material as the M.A. Applied exam and is offered at the same time, except that in order to pass it at the PhD level a student must correctly solve all four problems.

Note: Students concentrating in probability may choose to do so either through the statistics track or through the mathematics track. If a student wishes to do so through the mathematics track, the course and exam requirements are different. Details are available here .

Dissertation The dissertation is the major requirement for a Ph.D. student. After the student has completed all course work, the Director of Graduate Studies, in consultation with the student, selects a three-member dissertation committee. One member of this committee is designated by the Director of Graduate Studies as the Major Advisor for the student. Once completed, the dissertation must be defended in an oral examination conducted by at least five members of the Department.

The Dissertation and Final Oral Examination follows the   GRS General Requirements for the Doctor of Philosophy Degree .

Satisfactory Progress Toward the Degree Upon entering the graduate program, each student should consult the Director of Graduate Studies (Prof. David Rohrlich) and the Associate Director of the Program in Statistics (Prof. Konstantinos Spiliopoulos). Initially, the Associate Director of the Program in Statistics will serve as the default advisor to the student. Eventually the student’s advisor will be determined in conjunction with their dissertation research. The Associate Director of the Program in Statistics, who will be able to guide the student through the course selection and possible directed study, should be consulted often, as should the Director of Graduate Studies. Indeed, the Department considers it important that each student progress in a timely manner toward the degree. Each M.A. student must have completed the examination by the end of their second year in the program, while a Ph.D. student must have completed the qualifying examination by the third year. Students entering the Ph.D. program with an M.A. degree must have completed the qualifying examination by October of the second year. Failure to meet these deadlines may jeopardize financial aid. Some flexibility in the deadlines is possible upon petition to the graduate committee in cases of inadequate preparation.

Students enrolled in the Graduate School of Arts & Sciences (GRS) are expected to adhere to a number of policies at the university, college, and departmental levels. View the policies on the Academic Bulletin and GRS website .

Residency Post-BA students must complete all of the requirements for a Ph.D. within seven years of enrolling in the program and post-MA students must complete all requirements within five years. This total time limit is set by the Graduate School. Students needing extra time must petition the Graduate School. Also, financial aid is not guaranteed after the student’s fifth year in the program.

Financial Aid

As with all Ph.D. students in the Department of Mathematics and Statistics, the main source of financial aid for graduate students studying statistics is a Teaching Fellowship. These awards carry a stipend as well as tuition remission for six courses per year. Teaching Fellows are required to assist a faculty member who is teaching a course, usually a large lecture section of an introductory statistics course. Generally, the Teaching Fellow is responsible for conducting a number of discussion sections consisting of approximately twenty-five students each, as well as for holding office hours and assisting with grading. The Teaching Fellowship usually entails about twenty hours of work per week. For that reason, Teaching Fellows enroll in at most three courses per semester. A Teaching Fellow Seminar is conducted to help new Teaching Fellows develop as instructors and to promote the continuing development of experienced Teaching Fellows.

Other sources of financial aid include University Fellowships and Research Assistantships. The University Fellowships are one-year awards for outstanding students and are service-free. They carry stipends plus full tuition remission. Students do not need to apply for these fellowships. Research Assistantships are linked to research done with individual faculty, and are paid for through those faculty members’ grants. As a result, except on rare occasions, Research Assistantships typically are awarded to students in their second year and beyond, after student and faculty have had sufficient time to determine mutuality of their research interests.

Regular reviews of the performance of Teaching Fellows and Research Assistants in their duties as well as their course work are conducted by members of the Department’s Graduate Committee.

Ph.D. Program Milestones

The department considers it essential that each student progress in a timely manner toward completion of the degree. The following are the deadlines for achieving the milestones described in the Degree Requirements and constitute the basis for evaluating satisfactory progress towards the Ph.D. These deadlines are not to be construed as expected times to complete the various milestones, but rather as upper bounds. In other words,   a student in good standing expecting to complete   the degree within the five years of guaranteed funding will meet these milestones by the much e arlier target dates indicated below.   Failure to achieve these milestones in a timely manner may affectfinancial aid.

• Target: April of Year 1
• Deadline: April of Year 2
• Target: Spring of Year 2 post-BA/Spring of Year 1 post-MA
• Deadline: End of Year 3 post-BA/Fall of Year 2 post-MA
• Target: Spring of Year 2
• Deadline: End of Year 3
• Target: Spring of Year 2 or Fall of Year 3 post-BA/October of Year 2 post-MA
• Deadline: End of Year 3 post-BA/October of Year 2 post-MA
• Target: end of Year 3
• Deadline: End of Year 4
• Target: End of Year 5
• Deadline: End of Year 6

If you have any questions regarding our PhD program in Statistics, please reach out to us at [email protected]

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PhD by Distance

Our PhD by Distance programme allows you to benefit from our world-class support and the expertise of a Reading-based supervisor, while conducting your research in a location that suits your circumstances. 

The programme is available to candidates who need to study for most of their registration period at another site, whether in the UK, or worldwide. Acceptance for PhD by Distance is subject to the approval of the supervisors and the relevant school or department.

There are several reasons why you may wish to base yourself away from the University of Reading while undertaking your PhD:

• You have an extensive fieldwork element to your research
• You have responsibilities (e.g. caring or employment) that prevent you from attending on campus
• You are already based and/or employed in an environment that is relevant and conducive to their research
• You do not reside within travelling distance of the University
• You are based in a UK research organisation/institution with a collaborative agreement with the University of Reading in place  

What the programme offers

On the PhD by Distance programme, you will benefit from:

• the opportunity to study on  either a part-time (4-6 years duration) or full-time basis (3-4 years duration)
• supervision from one or more leading University of Reading academics, working at the forefront of their field
• access to a range of high-quality training, delivered on campus or online (see section below on Training)
• access to extensive online Library resources
• a PhD qualification which is delivered and examined at the same high academic standard as a campus-based PhD and a standard PhD degree certificate which does not state the mode of study on it.

Training for PhD by Distance students

The doctoral and researcher college provides a suite of generic researcher training open to all doctoral researchers at reading; this complements subject-specific training available via schools. the specific content of the programme changes from year to year but broadly includes the following: .

• Live online training (available off-campus) – A selection of our training sessions in the Reading Researcher Development Programme (RRDP) are delivered live online each year. UK time zone applies
• Recorded tutorials (available off-campus) – A small but growing selection of recorded tutorials on key topics such as managing data, academic English and research funding 
• Face-to-face training (available on campus only) - Other RRDP training sessions and selected longer programmes are offered face-to-face only. Students registered By Distance have access to face-to-face training when physically present on campus.     

By Distance study entails reduced access to training opportunities and on-campus experience. By Distance candidates are encouraged to discuss and mutually agree overall training plans with their supervisor and seek supplementary training external to Reading (e.g. via an employer or nearby institution) as appropriate.

Induction and physical attendance

A University-wide induction event is held by the Doctoral and Researcher College near the start of each term for all new doctoral researchers. PhD by Distance students studying at Reading in the first term are expected to attend. Where it is not possible for PhD by Distance students to attend in person (i.e. they are not on campus), a recording of the induction will be made available on the virtual learning environment.

Students registered By Distance will benefit from being physically present on campus for periods of time, for instance to attend induction and selected training, to meet their supervisor and peers, and to attend at key points such as Confirmation of Registration. It is strongly recommended that the first month of their programme be spent on campus. 

Eligibility

You must meet the following criteria:

• Satisfy the academic and English language entry requirements for PhD study
• The candidate has a research project which can be undertaken successfully by distance; regular access to campus should not be essential for successful completion of the research
• The candidate must be able to demonstrate that they have the time to undertake a PhD on the selected mode (full-time or part-time). Part-time doctoral study at Reading typically equates to between 50% and 60% of full-time
• The candidate has independent access to the resources needed to successfully complete their research; this will vary between candidates and projects but may include access to archives, facilities, data collection, digital infrastructure or space in their off-campus location
• The candidate is well motivated to work alone; they understand that By Distance registration is not an online learning programme and has reduced access to campus training
• The candidate and School/Department have discussed the suitability of their research project and personal circumstances for study by distance
• The candidate, where appropriate, has access to support from a local supervisor or mentor with experience in the academic field and also of supporting students or equivalent researchers. Advisors/mentors may be appointed to provide a regular point of contact, and are normally in a position to act as an ‘advocate’ and provide pastoral care if needed.
• Will study at least two-thirds of your minimum registration period off campus   

If you are intending to study in the Henley Business School, then please check with the  relevant Department within the Business School  about whether PhD by Distance is available before you apply.

A PhD by Distance is not suitable for all candidates. In offering a PhD By Distance programme specific consideration is given to the suitability of the research project, and whether it can be completed successfully without regular access to campus. Further consideration is given to areas of supervision, support, transferable and subject-specific skills training, research environment , progression milestones and the examination process in order to ensure off campus research students receive a comparable (although, not similar) experience to on campus students registered on ‘standard’ PhD programmes.

Fees for PhD by Distance programmes can be found on the fees webpage

• How to apply

Before starting your application, you are strongly advised to  navigate to the PhD webpages of your chosen school or department  to read the specific guidance on how to apply, as the requirements can vary. Once you have read the guidance, you will need to make a formal application through the University's  online application system , highlighting that you wish to study for a PhD by Distance (full or part-time). If you have questions about PhD by Distance in a specific school or department, then please contact the relevant School/Department PGR Administrator in the  School PGR Support Team . 

Immigration considerations for international students

PhD by Distance students must ensure that when visiting the University that they obtain the correct visa. The correct type of visa depends upon the period of time which a student intends to spend physically at the University.

PhD by Distance students can visit the University for up to six months within each academic year with a maximum of eighteen months in total for the duration of their programme. Students must discuss their intentions with the Doctoral Research Office (DRO) well in advance of any planned visit to the University. The DRO is well-placed to provide information on a range of immigration-related matters related to Postgraduate Research Programmes [email protected]

Examination

The normal expectation is that the viva of a PhD by Distance student will take place in Reading. Where this is not feasible, online examinations will be arranged.

Further information

Further information on PhD by Distance study can be found in the University's guidance on PhD by Distance .

Take the next step

• Get a prospectus
• Ask us a question

Graduate Student Handbook (Coming Soon: New Graduate Student Handbook)

Phd program overview.

The PhD program prepares students for research careers in probability and statistics in academia and industry. Students admitted to the PhD program earn the MA and MPhil along the way. The first year of the program is spent on foundational courses in theoretical statistics, applied statistics, and probability. In the following years, students take advanced topics courses. Research toward the dissertation typically begins in the second year. Students also have opportunities to take part in a wide variety of projects involving applied probability or applications of statistics.

Students are expected to register continuously until they distribute and successfully defend their dissertation. Our core required and elective curricula in Statistics, Probability, and Machine Learning aim to provide our doctoral students with advanced learning that is both broad and focused. We expect our students to make Satisfactory Academic Progress in their advanced learning and research training by meeting the following program milestones through courseworks, independent research, and dissertation research:

By the end of year 1: passing the qualifying exams;

By the end of year 2: fulfilling all course requirements for the MA degree and finding a dissertation advisor;

By the end of year 3: passing the oral exam (dissertation prospectus) and fulfilling all requirements for the MPhil degree

By the end of year 5: distributing and defending the dissertation.

We believe in the Professional Development value of active participation in intellectual exchange and pedagogical practices for future statistical faculty and researchers. Students are required to serve as teaching assistants and present research during their training. In addition, each student is expected to attend seminars regularly and participate in Statistical Practicum activities before graduation.

We provide in the following sections a comprehensive collection of the PhD program requirements and milestones. Also included are policies that outline how these requirements will be enforced with ample flexibility. Questions on these requirements should be directed to ADAA Cindy Meekins at [email protected] and the DGS, Professor John Cunningham at [email protected] .

Applications for Admission

• Our students receive very solid training in all aspects of modern statistics. See Graduate Student Handbook for more information.
• Our students receive Fellowship and full financial support for the entire duration of their PhD. See more details here .
• Our students receive job offers from top academic and non-academic institutions .
• Our students can work with world-class faculty members from Statistics Department or the Data Science Institute .
• Our students have access to high-speed computer clusters for their ambitious, computationally demanding research.
• Our students benefit from a wide range of seminars, workshops, and Boot Camps organized by our department and the data science institute .
• Suggested Prerequisites: A student admitted to the PhD program normally has a background in linear algebra and real analysis, and has taken a few courses in statistics, probability, and programming. Students who are quantitatively trained or have substantial background/experience in other scientific disciplines are also encouraged to apply for admission.
• GRE requirement: Waived for Fall 2024.
• Language requirement: The English Proficiency Test requirement (TOEFL) is a Provost's requirement that cannot be waived.
• The Columbia GSAS minimum requirements for TOEFL and IELTS are: 100 (IBT), 600 (PBT) TOEFL, or 7.5 IELTS. To see if this requirement can be waived for you, please check the frequently asked questions below.
• Deadline: Jan 8, 2024 .
• Application process: Please apply by completing the Application for Admission to the Columbia University Graduate School of Arts & Sciences .
• Timeline: P.hD students begin the program in September only.  Admissions decisions are made in mid-March of each year for the Fall semester.

Frequently Asked Questions

• What is the application deadline? What is the deadline for financial aid? Our application deadline is January 5, 2024 .
• Can I meet with you in person or talk to you on the phone? Unfortunately given the high number of applications we receive, we are unable to meet or speak with our applicants.
• What are the required application materials? Specific admission requirements for our programs can be found here .
• Due to financial hardship, I cannot pay the application fee, can I still apply to your program? Yes. Many of our prospective students are eligible for fee waivers. The Graduate School of Arts and Sciences offers a variety of application fee waivers . If you have further questions regarding the waiver please contact  gsas-admissions@ columbia.edu .
• How many students do you admit each year? It varies year to year. We finalize our numbers between December - early February.
• What is the distribution of students currently enrolled in your program? (their background, GPA, standard tests, etc)? Unfortunately, we are unable to share this information.
• How many accepted students receive financial aid? All students in the PhD program receive, for up to five years, a funding package consisting of tuition, fees, and a stipend. These fellowships are awarded in recognition of academic achievement and in expectation of scholarly success; they are contingent upon the student remaining in good academic standing. Summer support, while not guaranteed, is generally provided. Teaching and research experience are considered important aspects of the training of graduate students. Thus, graduate fellowships include some teaching and research apprenticeship. PhD students are given funds to purchase a laptop PC, and additional computing resources are supplied for research projects as necessary. The Department also subsidizes travel expenses for up to two scientific meetings and/or conferences per year for those students selected to present. Additional matching funds from the Graduate School Arts and Sciences are available to students who have passed the oral qualifying exam.
• Can I contact the department with specific scores and get feedback on my competitiveness for the program? We receive more than 450 applications a year and there are many students in our applicant pool who are qualified for our program. However, we can only admit a few top students. Before seeing the entire applicant pool, we cannot comment on admission probabilities.
• What is the minimum GPA for admissions? While we don’t have a GPA threshold, we will carefully review applicants’ transcripts and grades obtained in individual courses.
• Is there a minimum GRE requirement? No. The general GRE exam is waived for the Fall 2024 admissions cycle. 
• Can I upload a copy of my GRE score to the application? Yes, but make sure you arrange for ETS to send the official score to the Graduate School of Arts and Sciences.
• Is the GRE math subject exam required? No, we do not require the GRE math subject exam.
• What is the minimum TOEFL or IELTS  requirement? The Columbia Graduate School of Arts and Sciences minimum requirements for TOEFL and IELTS are: 100 (IBT), 600 (PBT) TOEFL, or 7.5 IELTS
•  I took the TOEFL and IELTS more than two years ago; is my score valid? Scores more than two years old are not accepted. Applicants are strongly urged to make arrangements to take these examinations early in the fall and before completing their application.
• I am an international student and earned a master’s degree from a US university. Can I obtain a TOEFL or IELTS waiver? You may only request a waiver of the English proficiency requirement from the Graduate School of Arts and Sciences by submitting the English Proficiency Waiver Request form and if you meet any of the criteria described here . If you have further questions regarding the waiver please contact  gsas-admissions@ columbia.edu .
• My transcript is not in English. What should I do? You have to submit a notarized translated copy along with the original transcript.

Can I apply to more than one PhD program? You may not submit more than one PhD application to the Graduate School of Arts and Sciences. However, you may elect to have your application reviewed by a second program or department within the Graduate School of Arts and Sciences if you are not offered admission by your first-choice program. Please see the application instructions for a more detailed explanation of this policy and the various restrictions that apply to a second choice. You may apply concurrently to a program housed at the Graduate School of Arts and Sciences and to programs housed at other divisions of the University. However, since the Graduate School of Arts and Sciences does not share application materials with other divisions, you must complete the application requirements for each school.

How do I apply to a dual- or joint-degree program? The Graduate School of Arts and Sciences refers to these programs as dual-degree programs. Applicants must complete the application requirements for both schools. Application materials are not shared between schools. Students can only apply to an established dual-degree program and may not create their own.

With the sole exception of approved dual-degree programs , students may not pursue a degree in more than one Columbia program concurrently, and may not be registered in more than one degree program at any institution in the same semester. Enrollment in another degree program at Columbia or elsewhere while enrolled in a Graduate School of Arts and Sciences master's or doctoral program is strictly prohibited by the Graduate School. Violation of this policy will lead to the rescission of an offer of admission, or termination for a current student.

When will I receive a decision on my application? Notification of decisions for all PhD applicants generally takes place by the end of March.

Notification of MA decisions varies by department and application deadlines. Some MA decisions are sent out in early spring; others may be released as late as mid-August.

Can I apply to both MA Statistics and PhD statistics simultaneously?  For any given entry term, applicants may elect to apply to up to two programs—either one PhD program and one MA program, or two MA programs—by submitting a single (combined) application to the Graduate School of Arts and Sciences.  Applicants who attempt to submit more than one Graduate School of Arts and Sciences application for the same entry term will be required to withdraw one of the applications.

The Graduate School of Arts and Sciences permits applicants to be reviewed by a second program if they do not receive an offer of admission from their first-choice program, with the following restrictions:

• This option is only available for fall-term applicants.
• Applicants will be able to view and opt for a second choice (if applicable) after selecting their first choice. Applicants should not submit a second application. (Note: Selecting a second choice will not affect the consideration of your application by your first choice.)
• Applicants must upload a separate Statement of Purpose and submit any additional supporting materials required by the second program. Transcripts, letters, and test scores should only be submitted once.
• An application will be forwarded to the second-choice program only after the first-choice program has completed its review and rendered its decision. An application file will not be reviewed concurrently by both programs.
• Programs may stop considering second-choice applications at any time during the season; Graduate School of Arts and Sciences cannot guarantee that your application will receive a second review.
• What is the mailing address for your PhD admission office? Students are encouraged to apply online . Please note: Materials should not be mailed to the Graduate School of Arts and Sciences unless specifically requested by the Office of Admissions. Unofficial transcripts and other supplemental application materials should be uploaded through the online application system. Graduate School of Arts and Sciences Office of Admissions Columbia University  107 Low Library, MC 4303 535 West 116th Street  New York, NY 10027
• How many years does it take to pursue a PhD degree in your program? Our students usually graduate in 4‐6 years.
• Can the PhD be pursued part-time? No, all of our students are full-time students. We do not offer a part-time option.
• One of the requirements is to have knowledge of linear algebra (through the level of MATH V2020 at Columbia) and advanced calculus (through the level of MATH V1201). I studied these topics; how do I know if I meet the knowledge content requirement? We interview our top candidates and based on the information on your transcripts and your grades, if we are not sure about what you covered in your courses we will ask you during the interview.
• Can I contact faculty members to learn more about their research and hopefully gain their support? Yes, you are more than welcome to contact faculty members and discuss your research interests with them. However, please note that all the applications are processed by a central admission committee, and individual faculty members cannot and will not guarantee admission to our program.
• How do I find out which professors are taking on new students to mentor this year?  Applications are evaluated through a central admissions committee. Openings in individual faculty groups are not considered during the admissions process. Therefore, we suggest contacting the faculty members you would like to work with and asking if they are planning to take on new students.

For more information please contact us at [email protected] .

For more information please contact us at  [email protected]

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Ph.D. in Geospatial Analytics

• How to Apply
• Prospective Student FAQs
• Student Success
• Mapping a Dynamic Planet
• Forecasting Landscape and Environmental Change
• Creating Near Real-Time Decision Analytics
• Exploring Models through Tangible Interaction
• Engaging Communities with Participatory Modeling
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Our innovative Ph.D. program brings together researchers from across NC State University to train a new generation of interdisciplinary data scientists skilled in developing novel understanding of spatial phenomena and in applying new knowledge to grand challenges.

This one-of-a-kind degree focuses on integrative thinking and experiential learning:

• Collaborative, cross-disciplinary teamwork  unites students and faculty from many research fields
• Guaranteed funding  for four years includes a competitive minimum stipend of $30,000, health insurance, and tuition
• Professional seminar  supports student success through training in science communication, proposal writing and geospatial data ethics
• Travel funding is available for students to attend scientific conferences
• Program values include prioritizing student mental health and work/life balance, open data, environmental and social justice, and a commitment to collaboration, community and equity

If your research goals intersect geospatial problem-solving from any number of fields, you will find your fit here.  Our  Faculty Fellows  advise students interested in a range of disciplines––from design, to social and behavioral sciences, natural resources and the environment, computer science, engineering and more––and approach their work in a range of  geospatial research areas . Students with strong backgrounds in quantitative methods in geography, data science, remote sensing and earth sciences are strongly encouraged to apply. We are especially committed to increasing the representation of students that have been historically excluded from participation in U.S. higher education.

Find recent publications by our students and faculty through NC State’s  Libraries Citation Index and learn more about the achievements of our students and alumni.

Program news

August 09, 2024

Using A.I. to Boost Team USA’s Olympic Breaking Performance

Geospatial Analytics Ph.D. student Christopher Dunstan’s passion for combining data science and dance could help Team USA’s breaking squad land top podium spots at the Paris 2024 Olympics.

August 01, 2024

New model uses satellite imagery, machine learning to map flooding in urban environments

A recent paper from Ph.D. student Rebecca Composto and co-authored by Ph.D. students Varun Tiwari and Mollie Gaines, along with Faculty Fellow Mirela Tulbure, describes a new model to predict urban flooding.

Insights from the IALE 2024 Conference

Geospatial Analytics Ph.D. student Erin O’Connell presented her research forecasting locations at greatest risk for spotted lanternfly infestation at the International Association for Landscape Ecology conference this past April.

Apply for a Ph.D. in Geospatial Analytics

Ten fully funded Ph.D.  graduate assistantships  with $30,000 salary, benefits, and tuition waiver are available for Fall 2024 through the Center for Geospatial Analytics.

Applications for Fall 2024 admissions are now closed. Applications for Fall 2025 will open in late September or early October. The application deadline is February 1, 2024 – all recommendations and test scores must be received by this date.

There are several opportunities for students to receive a stipend above the base rate of $30,000. These fellowships do not require an additional application:

• Goodnight Doctoral Fellowship. One to two incoming students with a track record of exceptional achievement in the sciences will earn an additional $10,000 per year + all student fees waived for four years
• University Graduate Fellowship. Five incoming students will receive an additional $4,000 in their first year
• Diversity Enhancement Fellowship. Two incoming students will receive an additional $2,000 in their first year
• Mansour Doctoral Fellowship. One incoming international student will be nominated to receive an additional $10,000 in their first year

Admission Requirements

Our most competitive applicants will have

• Significant quantitative research experience outside of the classroom, beyond basic data collection or data entry
• Computational/quantitative background, including a combination of the following coursework or demonstrated skills: statistics, advanced mathematics, quantitative research methods, R, Python
• Prior coursework, background and/or research interests in the area of geospatial analytics
• For international applicants: IBT TOEFL score ≥ 80 overall (18 in each section), IELTS score ≥ 6.5 on each section, Duolingo English ≥ 110. Scores are not required for citizens of  these countries  or who have completed at least one year of full time study at U.S. college or university

Supporting Documents

• Official NC State Graduate School  application.
• Unofficial transcripts  from all colleges/universities attended (official transcripts are only required if admitted to the program).
• Your academic and career goals as well as your motivation in pursuing a Ph.D.
• Research experiences and background/skills that would make you a successful Ph.D. student in geospatial analytics
• Relevant research interests
• Your specific interest in the Ph.D. in Geospatial Analytics at NC State
• 3 letters of recommendation.  Submit the names and contact information for your recommenders through the online application, and they will receive an email with instructions for submitting their letters online. Please select recommenders who can speak to your academic and/or research potential.
• Curriculum vitae/resume.
• Optional GRE scores. Taking the GRE is strongly recommended for international students who have not previously studied in the U.S.

If you have questions about the application process, please contact  Rachel Kasten , Graduate Services Coordinator ([email protected], 919-515-2800). Please note that there is a required application fee of $75 for domestic applicants and $85 for international applicants. McNair Scholars will have the application fee waived. This fee cannot be waived or reduced for international students.

More information for prospective international students can be  found here .

Degree Requirements

The Ph.D. program consists of

• 72 credit hours beyond the Bachelor’s degree .  The core required courses comprise 18 credit hours. The remaining 54 credit hours are devoted to an individually tailored selection of electives and research.
• an off-campus professional experience.  By the beginning of their third year in the program, students participate in an experiential learning activity within government (local, state, federal), industry, private and academic research institutions, or other organizations in the geospatial arena. Students consult with their advisors to identify specific opportunities that will enhance their doctoral program.
• a comprehensive exam.  Students will complete both written and oral exams by the end of their fifth semester in order to be admitted to candidacy.
• a   written dissertation  and  final dissertation oral defense  required to complete the degree.

Core Curriculum

The core curriculum includes the following courses; click course names to view descriptions. Students are required to take GIS 710 and any three additional core courses, as well as six elective credits:

GIS 710: Geospatial Analytics for Grand Challenges

Students examine why sustainable solutions to grand societal challenges need geospatial analytics. Emphasis is placed on the roles that location, spatial interaction and multi-scale processes play in scientific discovery and communication. Discussion of seminal and leading-edge approaches to problem-solving is motivated by grand challenges such as controlling the spread of emerging infectious disease, providing access to clean water and creating smart and connected cities. Students also engage in several written and oral presentation activities focused on data science communication skills and professionalization.

GIS 711: Geospatial Data Management

Applied experience in the architecture of geospatial data management, including open source options. The course introduces students to: (i) spatial and temporal data types (OGC specification, GPS and accelerometer matching), (ii) spatial predicates, (iii) spatial indices and (iv) spatial query processing. In addition, students will be exposed to modern spatial data management systems like NoSQL and graph databases, and data integration principles including protected health information (PHI/HIPAA).

GIS 712: Environmental Earth Observation and Remote Sensing

Advanced understanding of physical principles of remote sensing, image processing and applications from earth observations. Awareness of tradeoffs between earth observing sensors, platforms and analysis techniques will help prepare the students to critically assess remote sensing products and devise solutions to environmental problems. Students will be able to communicate the complexities of image analysis and will be better prepared to integrate earth observations into their areas of expertise. Topics include electromagnetic energy and radiative transfer; US and international orbital and suborbital data acquisition platforms; passive and active imaging and scanning sensors; spatial, spectral, radiometric, and temporal resolutions; geometric corrections and radiometric calibrations; preprocessing of digital remotely sensed data; advanced image analysis including enhancement, enhancement, classification, geophysical variable retrieval, error and sensitivity analysis; data fusion; data assimilation; and integration of remotely  sensed data with other data types in a geospatial modeling context.

GIS 713: Geospatial Data Mining and Analysis

Spatial data mining is the process of discovering interesting and previously unknown, but potentially useful, patterns from spatial and spatiotemporal data. However, explosive growth in the spatial and spatiotemporal data (~70% of all digital data), and the emergence of geosocial media and location sensing technologies has transformed the field in recent years. This course reviews the current state of the art in spatial, temporal and spatiotemporal data mining and looks at real-world applications ranging from geosocial networks to climate change impacts. Course introduces various spatial and temporal pattern families and teaches how to incorporate spatial relationships and constraints into data mining approaches like clustering, classification, anomalies and colocations.

GIS 714: Geospatial Computation and Simulation

Methods, algorithms and tools for geospatial modeling and predicting spatio-temporal dimensions of environmental systems. The course covers the physical, biological, and social processes that drive dynamics of landscape change. Deterministic, stochastic, and multi-agent simulations are explained, with emphasis on coupling empirical and process based models, techniques for model calibration and validation and sensitivity analysis. Applications to real-world problems are explored, such as modeling multi-scale flow and mass transport, spread of wildfire, biological invasions and urbanization.

GIS 715: Geovisualization

Principles of visualization design and scripting for geospatial visualization. This course provides a systematic framework of visualization design principles based on the human visual system and explores open-source geospatial data visualization tools. Topics include challenges and techniques for visualizing large multivariate dataset, spatio-temporal data and landscape changes over time. Students have the opportunity to work with broad range of visualization technologies, including frontiers in immersive visualization, tangible interaction with geospatial data and eye tracking.

Frequently Asked Questions

Below are some of the most frequently asked questions we have received about the Ph.D. program in Geospatial Analytics. If your questions are still not answered here, please feel free to contact us through the form below.

Can the program be completed online or part-time?

No, the Ph.D. in Geospatial Analytics is a traditional full-time on-campus program.

I am currently in a master’s degree program and will complete my degree in the spring. Can I still apply now to start the Ph.D. program in the fall?

Yes. We accept unofficial transcripts with your application. Official transcripts will be requested if you are admitted to the program.

Do I need to have been a geography major to apply?

No, we welcome applications from students with strong computational skills from diverse backgrounds, including computer science, data science, environmental science, ecology, engineering, and more.

Do I need a master’s degree to apply?

No, students may enroll without a master’s degree. Successful applicants, however, will have had previous academic research experience.

Do you offer application fee waivers?

Application fee waivers are offered only for domestic students who have participated in specific research programs (i.e. McNair Scholars).

Is financial assistance available?

Incoming doctoral students receive a tuition waiver, health insurance benefits, and a $30,000 stipend.

Do I need to secure an advisor before applying?

While you are encouraged to connect with faculty who share your interests prior to applying (the application will ask you to name a preferred advisor), students can be admitted on program funding without a specific advisor/position.

What kinds of projects might I work on?

Students in the Geospatial Analytics doctoral program work on a diverse range of data science frontiers intersecting multiple disciplines, with funding available from the Ph.D. program as well as from external grants secured by faculty. Assistantships are each fully funded for four years. Below are a sample of the opportunities that were available in previous years. For more details about each opportunity, and to learn about past projects, visit our Graduate Assistantships page .

• Landscape Connectivity Dynamics in Surface Water Networks — Join the Geospatial Analysis for Environmental Change Lab to investigate climate and land-use change effects on landscape connectivity dynamics.
• Seasonality from Space — Join the Spatial Ecosystem Analytics Lab on a NASA-funded project investigating satellite data fusion and time series analysis.
• Winter Weather — Join the Environment Analytics group to study the complex interactions within snow storms and wintery mix storms.
• Modeling Forest and Water Resources under Changing Conditions — Join the Watershed Ecology lab group and combine various data sources to create projections of future landscape conditions.
• Modeling Agricultural and Water Resource Dynamics — Join the Biosystems Analytics Lab to study the effects of global and local change on fresh and estuarine water quality, land-sea connectivity and agroecosystem productivity.
• Surface Water Dynamics from Space — Join the Geospatial Analysis for Environmental Change Lab to investigate hydroclimatic drivers of surface water extent dynamics and advance quantification of water extent and volume.
• Remote Sensing Forest Gap Dynamics — Join the Applied Remote Sensing and Analysis lab group to examine the role and influence of forest gaps in relation to localized large-scale disturbances.

Funding is available for additional projects, and in all cases students are encouraged to develop research questions and methods that suit their interests and career goals.

We’re here to help! Contact us for more information about the Ph.D. in Geospatial Analytics.

Explore Opportunities

Our graduate assistantships are fully funded with a yearly stipend, tuition support, and benefits. Learn more about opportunities at NC State and the Research Triangle to enrich your graduate experience.

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• Remote Statistics MS

Remote Statistics Masters Degree, Newton Mount Ida Campus (Boston Area)

The Statistics M.S. Degree can be earned 100% remotely.

All required courses are offered remotely through the Newton Mount Ida campus. Classes are offered from 6:00-8:30 p.m. once per week from Monday to Thursday at the Newton campus, or during the day on Saturdays.

For more information on the program, see  https://people.math.umass.edu/~conlon/statmtida , or contact  Erin Conlon .

Applications

Applications are accepted on a rolling basis for Fall through June 30 (domestic students) and May 31 (international students). Initial deadline is January 10.

Please make sure that your application clearly indicates that you are applying for the Newton campus. In the application process, choose "Masters Degree", then "Mathematics [Statistics] (M.S.) Newton".

For more information on the application process, see  https://people.math.umass.edu/~conlon/statmtida/admissions.html

Degree Requirements

Prerequisites:  Students entering the Statistics M.S. Degree program are expected to have had Linear Algebra and Calculus up through Multivariate Calculus (this is typically covered by a three-semester sequence in U.S. schools).

The requirements for the Masters Degree in Statistics involve coursework, a project and qualifying exams.

The Masters Degree in Statistics requires 30 hours of coursework (10 courses). Students can complete the program in as little as 1.5 years or on a part-time basis.

The required 10 courses include the following 5 core courses, which are all offered remotely:

Core Courses

•     Stat 535: Statistical Computing
•     Stat 607: Mathematical Statistics I
•     Stat 608: Mathematical Statistics II
•     Stat 625: Regression Modeling
•     Stat 691P: Project Seminar

Elective Courses

In addition, students must complete at least 5 other courses which are either Statistics courses numbered 526 or above, from within the department, or courses outside the department numbered 500 and above subject to prior approval by the Statistics coordinator.

Electives in Statistics currently offered and to be offered at Newton Mount Ida include the following, which are all offered remotely:

•     Stat 526: Design of Experiments
•     Stat 610: Bayesian Statistics
•     Stat 630: Statistical Methods for Data Science
•     Stat 631: Categorical Data Analysis
•     Stat 632: Applied Multivariate Statistics
•     Stat 633: Data Visualization

Students can also take elective courses in other graduate programs at UMass-Amherst including Computer Science, Biostatistics, Business & Analytics and Geosciences, among many others, with prior approval by the Statistics Coordinator. These courses are offered either remotely or online.

For further information on requirements, see  https://people.math.umass.edu/~conlon/statmtida

The project requirement is fulfilled by completing the course Stat 691P: Project Seminar.

• Qualifying Exams

Students completing the M.S. Degree in Statistics are required to pass two of three basic exams we offer: applied statistics, probability, and statistics, which are based on the courses Stat 535 and 625 (applied statistics); Stat 607 (probability); and Stat 608 (statistics). The basic exams are given twice a year, in January and August.

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• Financial Aid
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Award-winning teaching, research opportunities, and interdisciplinary programs in a diverse, inclusive community of excellence.

Lederle Graduate Research Tower, 1654 University of Massachusetts Amherst 710 N. Pleasant Street Amherst, MA 01003-9305, USA

Department Phone: (413) 545-2762 Department Fax: (413) 545-1801 Department Office: LGRT 1622