Coursework Doctorate Programs

Doctoral Degree Programs

The Graduate Division of the Jacobs School of Music offers course work leading to the degree of Doctor of Music in the areas of music literature and performance, composition, and conducting. The Jacobs School of Music also offers the Doctor of Music Education degree and, through the University Graduate School, the Doctor of Philosophy degree in the areas of musicology, music education, and music theory.

General Information

Proficiency Requirements

All proficiency requirements must be met by the end of the fifth semester of enrollment.  The Graduate Entrance Exams may be taken only in the first two semesters of enrollment.

Coursework Requirements

Most doctoral degrees require 60 credits of coursework, with 36 in the major field, 12 in a minor field, and 12 additional credits inside or outside the major field. Some majors include a small number of additional "tool subject" credits. For information specific to each major, see the "Major field requirements" link below.

Qualifying Exams

Passing the written and oral qualifying exams admits a student to candidacy for the degree. Qualifying exams are typically started after all coursework is complete, though in some cases they may be started during the final semester of coursework. Students are encouraged to begin their exams no later than the October after they complete coursework and to complete all exams within four to six months. All exams must be completed within one year.

Doctoral Final Project, Dissertation, or Piano Essay

The doctoral document presents original research on a topic relevant to the major field. Students are encouraged to consider possible topics for this capstone requirement while they are still completing coursework. While the document is normally completed after the student has passed the qualifying exams, it is permissible to complete any or all of the document requirements except the public presentation/defense before reaching candidacy. 

Choose your program for more information.

You’re passionate about big data. You’re ready to commit to an advanced degree. But should it be the MS or PhD? Our guide to doctoral programs in data science is here to help. It has advice on benefits & downsides, job opportunities, dissertation topics, courses, costs, and more. Just want the schools? Skip ahead to our complete list of data-related PhD programs.

Why Earn a PhD in Data Science?

Purpose

A PhD in Data Science is a research degree designed to give you a deep-rooted knowledge of statistics, programming, data analysis, and subjects relevant to your area of interest (e.g. machine learning, artificial intelligence, etc.).

The key word here is research:

  • You’ll be expected to conduct your own experiments in a specific field.
  • You’ll focus on theory—both pure and applied—to discover why certain methodologies are used.
  • You’ll take apart tools & technologies to determine how they’re built.

Benefits vs. Downsides

The benefits and downsides of a PhD are always being debated, and Quora has plenty of insightful answers from data scientists on this subject! Here are some of our findings…

Benefits of a PhD in Data Science

In a PhD program, you’ll have the opportunity to:

  • Research an area in data science that may a) be about to the change the industry b) have unexpected applications c) solve a long-standing problem.
  • Collaborate with “star” academic advisors in well-funded data science institutes and centers.
  • Become a critical thinker—knowing when, where, and why to apply theoretical concepts.
  • Specialize in an upcoming field (e.g. biomedical informatics).
  • Gain access to massive, real-world data sets through university partnerships.
  • Work with cutting-edge technologies and systems that may not be available in the private sector.
  • Automatically earn a master’s degree on your way to completing a PhD.
  • Qualify for high-level executive or leadership positions.

Downsides of a PhD in Data Science

On the other hand, you should realize that a PhD program:

  • Takes 4-5 years on a full-time schedule to complete. These are years when you could be earning money and learning real-world skills in a major company.
  • Can be prohibitively expensive if you don’t find ways to fund it.
  • May focus too much on pure theory and not enough on pragmatic applications & practical experience.
  • Contains a lot of solitary hours in reading and writing. If you want to work with a team or collaborate with industry partners, choose your program & dissertation topic very carefully.
  • Won’t give you “on-the-job” knowledge of problems and demands in the corporate world.

Bonus Round

As a former PhD student (albeit not in data science), the most important piece of advice I can give you is to pick the right advisor. This is the person who will guide your research, help you with funding, connect you to opportunities & resources, keep you on track, and launch your career. Make people a top priority when considering programs.

Career Outcomes

Do You Need a PhD to Land a Job?

In most cases, the answer is “no.” In the past, companies had a habit of demanding a PhD in Statistics or Computer Science for top-level positions. This was often because there weren’t many master’s programs in data science and employers wanted reassurance that candidates knew their stuff.

That situation has changed. Because master’s programs now emphasize hard skills & real-world applications, the number of job listings requiring a PhD is dropping rapidly.

Our best advice is to pay attention to the employer and the job title:

  • Companies & labs that specialize in data science, and major tech players like Amazon and Facebook, will have a reason for specifying a PhD in the education requirements.
  • Other industries may be happy with a strong BS or MS degree and relevant work experience. If you know how to analyze results, make product decisions, increase efficiencies, predict trends, and provide insights, you’re in good shape.

Have a look at the debate on Quora if you’d like more perspective on this question.

Careers for Data Science PhD Holders

PhD holders find careers in academia, industry & university research labs, government departments, and big-name tech companies. These are places that need job candidates who can:

  • Research & develop new methodologies.
  • Build core products, tools & technologies that are based on data science (e.g. ML or AI algorithms for Google or the next generation of big data management systems).
  • Reinvent existing methods & tools for specific purposes.
  • Translate research findings & adopt theory to practice (e.g. evaluating the latest discoveries & finding ways to implement them in the corporate world).
  • Design research projects for teams of statisticians and data scientists.

Sample job titles include:

  • Director of Research
  • Senior Data Scientist/Analyst
  • Data/Analytics Manager
  • Data Science Consultant
  • Laboratory Researcher
  • Strategic Innovation Manager
  • Tenured Professor of Data Science
  • Chief Data Officer (CDO)

Curriculum

Typical Program Structure

Data science PhDs are built on the same structure as most doctoral programs. That means you’ll typically have to:

  • Complete 2 years of full-time coursework.
  • Pass a comprehensive exam—often both oral and written—that shows you have mastered the subject matter.
  • Submit a dissertation proposal and have it approved.
  • Devote 2-3 years to conducting independent research & writing a dissertation. You’ll probably be teaching undergraduate classes at the same time.
  • Defend your work in a “dissertation defense”—usually an oral presentation to academics and the public.

During these years, you can improve your career prospects by attending and speaking at conferences, applying for summer fellowships, consulting, and/or doing paid work for employers (e.g. part-time research).

Dissertation

PhD students are expected to make a creative contribution to the field of data science—that means you’re not allowed to go over old ground or rehash what’s already out there. Your contribution will be summed up in your dissertation, which is a written record of your original research.

Some students go into a PhD already knowing what they want to research. Others use the first couple of years to explore the field and settle on a dissertation topic. Your advisor will be your closest ally in this process – one of the many reasons to choose him/her wisely!

Data Science vs. Business Analytics vs. Specialties

Doctoral programs in data science can also fall under the heading of Statistics, Computational Sciences, Informatics, and the like. Because there’s really no rhyme or reason to the title, evaluate each program on its curriculum instead. Make sure the foundation courses and electives will prepare you for the research area that you want to explore.

The exception to this rule? The PhD in Business Analytics (or Decision/Management Sciences). These programs are typically administered through the School of Business, which means the curriculum often includes corporate topics like management science, marketing, customer analytics, supply chains, etc.

Interested in a particular subset of data science? Some universities are developing specialty PhD programs. Biostatistics and Biomedical/Health Informatics are obvious examples, but you’ll also find a number of doctoral programs in Machine Learning (usually run by the Department of Computer Science) and sub-specialties in fields like Artificial Intelligence and Data Mining.

As always, look for super-smart advisors and find out what’s happening in their research labs!

Other Considerations

Typical Admissions Requirements

PhD candidates have to fill out a lengthy application form and pay an application fee. Universities usually want to see applicants who have:

  • A Bachelor of Science (BS) in Computer Science, Statistics, or a relevant discipline (e.g. Engineering) with an official transcript from an accredited institution
  • A GPA of 3.0 or higher on a 4.0 scale
  • GRE test scores
  • TOEFL or IELTS for applicants whose native language is not English
  • Letters of recommendation
  • Statement of purpose/intent
  • Résumé or CV

If you don’t already have certain skills (e.g. stats, calculus, computer programming, etc.), the university may ask you to complete prerequisite courses.

Costs

In the 2017-2018 academic year, many universities were charging between $1,300-$2,000 per credit hour. With a PhD of 70-75 credits, you could be looking at tuition costs that exceed $150,000. On top of that, you’ll have to pay various fees (e.g. lab, health insurance, technology, international student, etc.).

That’s the bad news. The good news is data science is a high-demand field, so almost all universities will help you cover tuition & living costs. The PhD in Big Data program at Brown, for instance, guarantees five years of financial support—including a stipend and health insurance—to students in good academic standing.

One last thing to consider is your “cost of living” expenses (e.g. housing, books & supplies, food, transport, conference travel, etc.). Does the university provide subsidized housing for PhD students? Can you cover rent with your stipend? Do you mind eating packaged noodles? Use Sperling’s Best Places to compare costs in multiple cities.

How to Pay for a PhD

Your first stop should be the PhD program page on the university website. Here you’ll find links to relevant fellowships and advice on financial matters, including:

  • PhD Fellowships: These are scholarships by any other name and they’re usually service-free (i.e. you don’t have to work for them). You’ll find lots of fellowships sponsored by the university, by companies or industries, and by the government (e.g. National Science Foundation). Be aware that some external fellowships will only cover the years of your dissertation research.
  • Teaching/Research Assistantships: Assistantships are a popular way for universities to fund PhD students. In return for teaching undergraduates or working as a researcher, you’ll often receive a break on tuition costs and a living stipend.
  • In-State Tuition: Public universities (e.g. University of North Carolina) usually offer in-state students a much lower cost per credit. This can be a real boon if you live near a strong school.
  • Regional Discounts: Many universities have agreements to offer reduced tuition costs to students from neighboring states (e.g. New England Board of Higher Education Regional Student Program (RSP)). Check to see if this applies to your PhD.
  • Travel Grants: It’s important for doctoral students to attend research conferences and network with future collaborators, and a lot of grants are designed with this purpose in mind.
  • Student Loans: Consider all your other options before going into debt! A doctorate is a long-term commitment—you may not see a financial return on your education investment for good number of years.

Remember, a lot of PhD students in data science are fully funded. Just to take a few examples:

  • Most students in Johns Hopkins University’s PhD in Machine Learning are fully funded through a mixture of research assistantships, teaching assistantships, training grants, and fellowships.
  • U.S. citizens and permanent residents in Stanford’s PhD in Biomedical Informatics are funded by a National Library of Medicine (NLM) Training Grant and Big Data to Knowledge (BD2K) Training Grants.
  • Warwick University in the U.K. offers approximately 10 fully funded 4-year scholarships to U.K. residents and eligible E.U. students.

If you’re coming from overseas, be sure to talk to your school about any differences between funding for citizens and international students.

Online vs. On-Campus Data Science Programs

Distance education is tempting if you can’t afford to move, and a small number of universities now offer online PhDs in data science. These programs often require you to attend a few campus events (e.g. symposiums), but allow you to complete coursework and conduct research in your own hometown.

We advise you to think very carefully about online learning. It may be a great option if you’re a seasoned pro in San Jose with access to state-of-the-art facilities, fabulous research sources, and on-the-ground mentors. It may not be so great if you live in rural Wyoming.

6 Questions to Ask Yourself Before You Commit

  1. Are you extremely passionate about an area of research?
  2. Do you know which academic(s) you want to work with? Are they willing to work with you?
  3. Do you mind committing to 4-5 years of study when you could be working full-time instead?
  4. Does your university have strong funding sources (private & government) for data science research?
  5. Will you have access to super-cool data resources, labs, and industry partners?
  6. Do you know how you’re going to pay for it?

If you’ve answered “no” to any of these questions, you may wish to reconsider your decision. Trust us, it’s going to be a long haul!

School Listings

We found 58 universities in our directory offering doctorate-level programs in data science. If you represent a university and would like to contact us about editing any of our listings, or adding new programs, please send an email to info (at) mastersindatascience.org.

PhD in Data Science/Analytics Online

Click here to see the full list of on-campus programs instead.

Colorado Technical University

Colorado Springs, Colorado

Doctor of Computer Science - Concentration in Big Data Analytics

OFFERED BY: Computer Science Department

Students in Colorado Technical University's program leading to a Doctor of Computer Science with a concentration in Big Data Analytics can complete most of the coursework online. However, during their course of studies they must attend at least four residential symposiums at the campus in Denver, which are offered four times per year. First-term students must also attend an on-campus orientation, which is scheduled in conjunction with a symposium. The three-year program requires students to complete 96 credits, and to research, write and defend a dissertation. Applicants should have a master's degree with a GPA of at least 3.0, although CTU also offers the option of earning an accelerated master's degree while starting work on the doctoral degree in a program called the Doctoral Advantage.

Indiana University Bloomington

Ph.D. Minor in Data Science

OFFERED BY: School of Informatics and Computing
DELIVERY: Campus or Online

Doctoral students at Indiana University Bloomington can minor in Data Science, gaining skills that are valuable in fields such as education, business, environmental science, public heath, political science, and sociology. In data science classes, student learn to analyze, visualize and report on large amounts of data. To achieve the doctoral minor in data science, students must complete at least 12 credits of data science coursework, selected in consultation with an adviser from the data science program. Candidates must complete each data science course with a grade of B or better. There is no requirement for a written qualifying exam in a minor field.

University of North Texas

Phd in Information Science - Health Informatics

OFFERED BY: Department of Information Science
DELIVERY: Online or Campus

The University of North Texas has a Ph.D. in Information Science that allows students to concentrate in health informatics. The curriculum for the program includes 12 credits in core areas, 12 credits in required classes in the health informatics concentration, electives in the concentration, and 24 credits in research including a dissertation. Applicants must submit applications to the Toulouse Graduate School and to the Department of Information Science. Applicants must have a master's degree with a 3.5 GPA. The application packet for the Department of Information Science must include GRE scores, a personal statement covering research interests and accomplishments, CV, and three recommendations.

PhD in Data Science/Analytics On-Campus

Click here to see the full list of online programs instead.

California Institute of Technology

PhD in Computing and Mathematical Sciences

OFFERED BY: Department of Computing and Mathematical Sciences

CalTech has a new Ph.D. in Computing and Mathematical Sciences that is multidisciplinary and brings together faculty and students from fields including computer science, electrical engineering, applied math, operations research, economics, and the physical sciences. In their first year, all students take courses in math and computing fundamentals, and each student must take three courses in a focus area and meet breadth requirements. All candidates must complete a dissertation. Applicants should be interested in an interdisciplinary field, and those accepted generally received an undergraduate degree in math, computer science, electrical engineering, or economics. Applicants must submit general GRE scores, and the college advises submitting scores from a subject test as well. Students enter the program in the fall.

Carnegie Mellon University

PhD in Machine Learning

OFFERED BY: Machine Learning Department

The Ph.D. in Machine Learning program at Carnegie Mellon University is sponsored by the School of Computer Science and Department of Statistics. This is a five-year program that requires students to complete core courses, electives, and a data analysis project for a master's degree; gain proficiency in teaching, research, and conference presentation; and complete a thesis. Applicants must have at least a bachelor's degree and must submit GRE scores. The program does not list any specific desired undergraduate majors but students who are admitted must be proficient in linear algebra, probability, and proofs and be highly skilled in computer programming. Carnegie Mellon provides funding for machine learning Ph.D. candidates for five years of study.

Columbia University in the City of New York

PhD in Biomedical Informatics

OFFERED BY: Department of Biomedical Informatics
PRE-REQUISITE TECHNICAL COURSEWORK: technical bachelor's degree

Columbia University has a Ph.D. in Biomedical Informatics that is offered as a full-time program. Students spend about two to three years completing coursework before focusing on independent research. In addition, students in the program are required to perform as teaching assistant for two courses, submit papers and posters to national conferences, attend conferences, pass two oral exams, and complete a dissertation. All students in this program are fully funded. The program is open to students who have at least a bachelor's degree in a discipline such as math, computer science, nursing, medicine, pubic health, information management, physics, or biology. Applicants must submit GRE scores, personal statement, three recommendations, resume, and official transcripts. Students start the program in the fall.

Indiana University-Purdue University-Indianapolis

Ph.D. in Data Science

OFFERED BY: School of Informatics and Computing

The School of Informatics and Computing at IUPUI recently introduced a program leading to a Ph.D. in Data Science. To earn the degree, students must complete 24 credits in data science core courses, 18 credits in methods courses, a minor appropriate to their chosen specialty with all courses taken from outside the data science program, and 30 credits in a dissertation. Applicants must have at least a bachelor's degree with a 3.0 or higher GPA. However, most admitted students have a master's degree in a field such as data science, computing, health, or a social science with a GPA of 3.5. GRE scores are required, with admitted students typically scoring above the 70th percentile. The doctoral program requires students to start in the fall.

PhD in Health and Biomedical Informatics

OFFERED BY: BioHealth Informatics Department
PRE-REQUISITE TECHNICAL COURSEWORK: computer science

The School of Informatics and Computing at IUPUI offers a Ph.D. in Health and Biomedical Informatics for students interested in working for health care agencies, insurance companies, or related organizations. This 90-credit program requires students to complete a minor that aligns with the subdiscipline of informatics they are interested in. Typically, students can complete the degree in about four years, including some summers. Applicants should have a bachelor's degree with an undergraduate GPA of 3.0 or higher. Accepted applicants usually have a degree in a technical field such as math, engineering, computer science, or statistics, or in a health-related field such as biology, biochemistry, or nursing. All applicants should have completed coursework in a programming language, databases, medical terminology, anatomy, and physiology.

Kennesaw State University

Ph.D. in Analytics and Data Science

OFFERED BY: Department of Statistics and Analytical Sciences
PRE-REQUISITE TECHNICAL COURSEWORK: math,statistics,computer science

The Ph.D. in Analytics and Data Science from Kennesaw State University has a strong focus on application, with all candidates required to engage with sponsoring organizations, including completion of a doctoral internship. The 78-credit program includes 48 credits in core coursework: 24 credits in statistics, nine credits in math, and 15 credits in computer science. Students must pass a comprehensive exam on those subject areas. Applicants to the program should have a master's degree in a computational field such as statistics, math, engineering, computer science, or finance and must submit GRE scores. Prerequisites include successful completion of two semesters of calculus, programming experience, and supervised modeling experience. While not required, KSU recommends applicants have Base SAS Certification.

Doctor of Science in Information Systems

OFFERED BY: Graduate Office
DELIVERY: Campus or Online

Students earning a Doctor of Science in Information Systems at Dakota State University choose from three research specializations: analytics and decision support, health care information systems, or information assurance and computer security. Students in the online degree program must complete 27 credits in master's level IS courses, 27 credits in the area of specialization, nine credits in research methods, and 25 credits in the dissertation. Candidates also must present a portfolio and pass an exam. Applicants must have at least a bachelor's degree with a GPA of 3.0. Applicants with a master's in information systems can waive the master's level classes. All applicants must submit GRE scores and must know the fundamentals of business and information systems. Program entry is offered in the fall.

DrPh in Public Health Informatics

OFFERED BY: Bloomberg School of Public Health

The Bloomberg School of Public Health at Johns Hopkins University offers a DrPH in Health Policy and Management with a public health informatics track. The program is designed for public health professionals and students who have a master's degree in a related field. The program is intended for part-time students, and students have up to nine years to complete the program, which requires at least 64 term credits and a dissertation. Applicants must have a master's degree, at least three years of experience in relevant public health work, and a variety of prerequisite courses, including three courses in statistical methods in public health and a course in principles of epidemiology. Applicants must submit GRE or GMAT scores.

PhD in Biomedical Informatics

OFFERED BY: College of Health Solutions
PRE-REQUISITE TECHNICAL COURSEWORK: technical bachelor's degree

The College of Health Solutions at Arizona State University in Tempe offers a Ph.D. in Biomedical Informatics that allows students to focus on areas such as bioinformatics or clinical informatics. The curriculum requires 84 credits, including 22 core credits, 38 elective credits, 12 credits in research, and 12 in the dissertation. Applicants should have a bachelor's degree with a 3.0 GPA for their last 60 credits or a master's with a 3.0 GPA. The degree should be in a related field such as biology, computer science, statistics, or engineering or the applicant should have training in a field such as nursing or pharmacy. All students must meet prerequisites in anatomy and physiology, calculus, computer programming, biology, and statistics. Students enter the program in the fall.

PhD in Business - Business Analytics Specialization

OFFERED BY: Department of Mathematical Sciences

Students earning a Ph.D. in Business from Bentley University can specialize in business analytics. The program is housed in the Department of Mathematical Sciences, which has a focus on applied research relevant to business. The Ph.D. program requires four years, with two years of coursework and two years when candidates teach one course per semester and work on their dissertation. Applicants should have a master's degree, although the program sometimes accepts students with just a bachelor's degree. Applicants whose degree is not in business must demonstrate their understanding of business subjects. The program prefers students with professional work experience. Applicants must submit GRE or GMAT scores and a five-page research paper outlining their proposed research topic. New students enter in the fall of odd-numbered years.

Ph.D. in Biostatistics

OFFERED BY: School of Public Health
PRE-REQUISITE TECHNICAL COURSEWORK: math

The School of Public Health at Brown University offers a Ph.D. in Biostatistics for individuals interested in leading interdisciplinary research projects in the fields of public health, medicine, or social sciences. During their doctoral studies, students must complete required coursework, teaching, research, and a dissertation. Applicants can have a bachelor's degree in any field but they must have completed three semesters of calculus, as well as advanced undergraduate courses in linear algebra and probability. Experience with numerical computing is recommended. Applicants must submit GRE scores, three recommendations, and personal statement. Transcripts should indicate a history of academic excellence. Doctoral students begin the program in the fall.

Ph.D. in Computer Science - Concentration in Data Science

OFFERED BY: Computer Science Department

The Ph.D. in Computer Science program at Brown University is interested in students who want to conduct research in data science, big data, and next-generation data management systems. To advance to candidacy, students must pass a programming requirement, coursework requirements, and complete a research project. All students must also train as teaching assistants for at least one semester. As candidates, they must complete a depth requirement and complete and defend a dissertation. Applicants are required to submit GRE scores, letters of recommendation, a statement of purpose, and transcripts. Other factors that can strengthen an application packet include work experience, research experience, awards, and previous high academic performance in STEM subjects. New students enter the program in September.

Doctorate in Computational and Data Sciences

OFFERED BY: Schmid College of Science & Technology
PRE-REQUISITE TECHNICAL COURSEWORK: math,statistics

Chapman University offers a Ph.D. in Computational and Data Sciences that students can complete in about four years of full-time study, but the program also accepts individuals interested in part-time study. The curriculum requires 70 to 73 credits, including 10 to 13 core credits, 45 credits in electives and research, and 12 credits for the dissertation. Applicants must have a bachelor's degree and must submit GRE scores. Prerequisite courses are differential equations, data structures, and probability and statistics. Accepted student who do not meet those prerequisites are required to take foundation courses. The college will accept up to 30 credits from a master's program. Students begin the program in the fall.

Doctor of Philosophy in Biomedical Data Science and Informatics

OFFERED BY: Clemson Graduate School
PRE-REQUISITE TECHNICAL COURSEWORK: math, computer science

Clemson University and the Medical University of South Carolina jointly offer a Ph.D. in Biomedical Data Science and Informatics. The program has three specialty tracks: precision medicine, population health, and clinical and translational informatics. Students must complete 65 to 68 credits in coursework and a dissertation. Coursework is in five areas: biomedical informatics; computing, math, statistics, and engineering; population health, systems, and policy; biological and medical; lab rotations, seminars, and doctoral research. Applicants must have a bachelor's degree in math, statistics, health sciences, or engineering. Prerequisites include a year of calculus, a year of college biology, and an advanced computer programming course. GRE scores are required. Most students are expected to complete the degree in five years or less.

Ph.D. in Quantitative Biomedical Sciences

OFFERED BY: Interdepartmental
PRE-REQUISITE TECHNICAL COURSEWORK: math, computer science

Dartmouth University offers a Ph.D. in Quantitative Biomedical Sciences that provides a strong foundation in areas such as bioinformatics, biostatistics, and epidemiology. In addition to coursework, students must take part in research rotations in which they work on research with faculty members, supervised teaching, a weekly journal club, a qualifying exam, research, and a dissertation. Applicants should have a background and academic degree in a quantitative area such as biology, epidemiology, bioinformatics, math, or computer science. Students who have not taken adequate undergraduate courses in related topics will be required to complete deficiency coursework in their first semester. The program is open to full-time students, and the university provides fellowship funding for all students in the program.

Doctor of Philosophy in Computational Sciences and Informatics

OFFERED BY: Department of Computational and Data Sciences

The Ph.D. in Computational Sciences and Informatics at George Mason University is a 72-credit program that requires a dissertation. All students must select a focus area from either data science or computer modeling and simulation. Students who have completed a master's in computational science or a similar field may be able to eliminate up to 24 required credits. The program is designed for part-time students, with courses offered in the late afternoon or early evening. Applicants should have at least a bachelor's degree in a STEM field with a GPA of 3.0 or higher. They must have completed math coursework through differential equations and must know a computer programming language. GRE scores are required for applicants without a master's degree.

Ph.D. in Computer Science - Bioinformatics Concentration

OFFERED BY: Department of Computer Science
PRE-REQUISITE TECHNICAL COURSEWORK: technical bachelor's degree

Georgia State University's Department of Computer Science has a Ph.D. in Computer Science program that offers a bioinformatics concentration. Requirements for the degree include completing 48 credits of graduate coursework, which must include three courses in bioinformatics, three in biology, one in chemistry, and one in biostatistics. Candidates must also pass a candidacy exam and complete a dissertation. Applicants must have, at minimum, a bachelor's degree in computer science or a related field with a GPA of 3.0 or higher. Students whose background is not in computer science will have to complete foundation work before beginning work on the doctorate. Applicants must submit GRE scores, three recommendations, and a personal statement. Students may only enter the Ph.D. program in the fall.

Ph.D in Bioinformatics and Computational Biology

OFFERED BY: Bioinformatics and Computational Biology Department

Iowa State University's Ph.D in Bioinformatics and Computational Biology has a strong research focus. In their first year, all Ph.D. students must participate in research exploration rotations in which they work in three different professors' labs. Typically, students can complete the courses, research, and thesis requirements for the program in about five years. Applicants should have a bachelor's degree in molecular biology, computer science, math, statistics, physics, or a closely related field. The undergraduate GPA should be 3.3 or higher, and applicants should be in the upper quarter of their college graduating class. GRE scores are required. First-year students should expect to take some background coursework. Prerequisites include three calculus courses, probability and statistics, genetics, biological evolution, data structures, and object oriented programming.

Ph.D. in Computational and Data-Enabled Science and Engineering

OFFERED BY: Department of Mathematics & Statistical Sciences
PRE-REQUISITE TECHNICAL COURSEWORK: technical bachelor's degree

Jackson State University offers a Ph.D. in Computational and Data-Enabled Science and Engineering that has a concentration track in Computational Mathematics and Statistical Science. To earn the degree, students must complete 12 credits in a common core, 12 credits in core concentration track courses, 24 credits in concentration electives, and 24 credits in a dissertation. Applicants should have a bachelor's or master's degree in a STEM field or another closely related field, as long as they have a strong computational and quantitative background. Applicants must have attained a GPA of 3.0 or higher on the highest degree they have earned. GRE scores, statement of purpose, and three recommendations are required. JSU has several funding opportunities for doctoral students.

PhD in Health Sciences Informatics

OFFERED BY: Division of Health Sciences Informatics

Johns Hopkins University has a Ph.D. in Health Sciences Informatics geared toward individuals who want to become researchers in health informatics. The program requires students to complete 125 quarter credits, including core courses, electives, practicum and research rotations, and mentored research. The requirements are spread over several learning areas, including biomedical informatics, computer science, research, clinical informatics, public health informatics, and practical experience. Applicants with a bachelor's degree are required to submit GRE scores or have at least five years of professional experience in a relevant field, such as medicine, dentistry, nursing, public health, bioengineering, or computer science. Applicants with a master's or Ph.D. do not have to submit GRE scores or provide relevant experience. Students enter the program in the fall.

PhD in Machine Learning

OFFERED BY: Department of Computer Science

Students interested in machine learning can earn a Ph.D. in the field at Johns Hopkins University through one of the departments involved in the cross-departmental interest area. Relevant departments include computer science, applied math and statistics, biostatistics, biomedical engineering, cognitive science, and electrical and computer engineering. Students can take coursework from multiple departments and choose dissertation committee members from a variety of departments. Applicants should apply for admission to the department that is most closely related to their interests or educational background. Degree requirements may vary by department. Applicants must submit GRE scores, and some departments, such as math, require the GRE subject test as well as the general test. Doctoral programs start in the fall.

Ph.D. in Computer Science with Specialization in Visualization, Databases and Big Data

OFFERED BY: Tandon School of Engineering
PRE-REQUISITE TECHNICAL COURSEWORK: technical bachelor's degree

NYU's Tandon School of Engineering offers a Ph.D. in Computer Science where students can benefit from the school's research strengths in visualization, databases, and big data. To earn the doctoral degree, students must complete at least 75 credits beyond a bachelor's degree, including a Ph.D. thesis. Students may transfer up to 30 credits from a master's program. Applicants must have at least a bachelor's degree in computer science or a closely related field, such as computer engineering. Potential students with a bachelor's degree in a different field are advised to earn a master's in computer science before applying. Applicants must submit GRE scores. The program accepts full-time students only, and they enter in the fall.

PhD in Biostatistics

OFFERED BY: School of Medicine

The NYU School of Medicine offers a Ph.D. in Biostatistics that has an interdisciplinary focus and covers a broad range of research areas. Candidates collaborate with faculty in a variety of studies. Students must attend full-time and complete 32 credits of coursework and 40 credits in research and seminar work. Additional requirements include passing a qualifying exam, submitting one research article to a peer-reviewed journal, and a thesis. Applicants should have a background in statistics or math and biology and must have at least a bachelor's degree. Admission decisions are based on research experience, academic achievement, recommendations, scientific potential, and GRE scores. Applicants who advance past initial screening may be interviewed. Students typically complete the program in less than six years.

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