phd data science columbia university

M.S. in Data Science

The m.s. in data science is for individuals looking to strengthen their career prospects or make a career change by developing expertise in data science..

Our students have the opportunity to conduct original research, included in a capstone project, and interact with our industry partners and faculty. Students may also choose an elective track focused on entrepreneurship or a subject area covered by one of our eight centers.

Eligibility Requirements

• Undergraduate degree
• Prior quantitative coursework (calculus, linear algebra, etc.)
• Prior introductory computer programming coursework

Application Requirements

• Online application
• Personal Statement
• Uploaded transcripts from every post-secondary institution attended
• Three recommendation letters
• Curriculum vitae / resumé
• Official Graduate Record Examination (GRE) General Test Scores are optional for the 2023 applications (optional)
• $85 non-refundable application fee
• TOEFL, IELTS or PTE Academic test scores, if applicable

Fall Application Deadline

• Priority Deadline: January 15
• Final Deadline: February 15

Upcoming Admissions Sessions

Seas information sessions:.

• https://apply.engineering.columbia.edu/portal/info_sessions

Data Science Sessions:

• Next Steps: MS Data Science Program Snapshot Friday, December 15 at 10:30 AM

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Public Health Data Science

The MS in Biostatistics Public Health Data Science Track (MS/PHDS) is designed for students interested in careers as biostatisticians applying statistical methods in health-related research settings. The MS/PHDS Track provides core training in biostatistical theory, methods, and applications, but adds a distinct emphasis on modern approaches to statistical learning, reproducible and transparent code, and data management. It is an appropriate program for students who intend to conclude their studies with the MS degree as well as those who want to pursue a PhD in biostatistics

All MS/PHDS candidates begin their studies in the fall semester. The length of the MS/PHDS program varies with the background, training, and experience of the candidate, but the usual period needed to complete the 36 credit MS/PHDS degree is two years (four semesters). In addition to fulfilling their course work, all MS/PHDS students also complete a one-term practicum and capstone experience.

Competencies

Through a curriculum of 36 credit hours of course work, a practicum, and the capstone experience, the MS/PHDS track provides students with the skills necessary for a career as a public health data scientist and a rigorous grounding in traditional biostatistics.

In addition to achieving the MS in Biostatistics core competencies, students in the PHDS Track gain the following specific competencies in the areas of public health and collaborative research, the foundations of applied data science, teaching biostatistics and biostatistical research. Upon satisfactory completion of the MS/PHDS, graduates will be able to:

Public Health and Collaborative Research

• Formulate and prepare a written statistical plan for analysis of public health research data that clearly reflects the research hypotheses of the proposal in a manner that resonates with both co-investigators and peer reviewers;
• Prepare written summaries of quantitative analyses for journal publication, presentations at scientific meetings, grant applications, and review by regulatory agencies;

Foundations of Applied Data Science

• Develop expertise in one or more statistical software and database management packages (often R and SQL, among others) routinely used by data science professionals;
• Implement a reproducible workflow for data analysis projects, including robust project organization, transparent data management, and reproducible analysis results;
• Develop and execute analysis strategies that use traditional statistical tools or modern approaches to statistical learning, depending on the nature of the scientific questions of interest;
• Identify the uses to which data management can be put in practical statistical analysis, including the establishment of standards for documentation, archiving, auditing, and confidentiality; guidelines for accessibility; security; structural issues; and data cleaning;

Teaching Biostatistics

• Review and illustrate selected principles of study design, probability theory, estimation, hypothesis testing, statistical learning, and data analytic techniques to public health students enrolled in introductory level graduate public health courses; and

Biostatistical Research

• Apply probabilistic, statistical, and data scientific reasoning to structure thinking and solve a wide range of problems in public health.

Course Requirements

MS/PHDS graduates are expected to master the mathematical and biostatistical concepts and techniques presented in the curriculum’s required courses. Each student's program is designed on an individual basis in consultation with a faculty advisor taking into consideration the student's prior educational experience.

Students who have mastered an academic area through previous training may have the corresponding course requirement waived. Some students, such as those with undergraduate majors in statistics or mathematics, may apply to have several courses waived. Students wishing to waive one or more courses must request approval in writing from their advisors and the Director of Academic Programs. These students must still complete a minimum of 36 points to earn the MS/PHDS degree.

Required Courses

Below is the required course work. Students consult their faculty advisors before registering for classes to plan their programs based on their individual background, goals, and the appropriate sequencing of courses. Waiver of any required courses (with prior written approval of their faculty advisor and the Director of Academic Programs) enables students to take other, higher level classes.

*Students who have strong math background and/or have taken basic machine learning methods, can substitute the P8106 Data Science II with P9120 Topics in Statistical Learning and Data Mining I. 

Students choose four or more courses from the list below or from alternatives approved by their academic advisors.

Sample Timeline

Below is a sample timeline for MS/PHDS candidates. Note that course schedules change from year to year, so that class days/times in future years will differ from the sample schedule below; you must check the current course schedule for each year on the course directory page .

Practicum Requirement

One term of practical experience is required of all students, providing educational opportunities that are different from and supplementary to the more academic aspects of the program. The practicum may be fulfilled during the school year or over the summer. Arrangements are made on an individual basis in consultation with faculty advisors who must approve both the proposed practicum project prior to its initiation, and the report submitted at the conclusion of the practicum experience. Students will be required to make a poster presentation at the department’s Annual Practicum Poster Symposium which is held in early May.

Capstone Experience

A formal, culminating experience for the MS degree is required for graduation. The capstone consulting seminar is designed to enable students to demonstrate their ability to integrate their academic studies with the role of biostatistical consultant/collaborator, which will comprise the major portion of their future professional practice.  

As part of the seminar, students are required to attend several sessions of the Biostatistics Consulting Service (BCS). The Consultation Service offers advice on data analysis and appropriate methods of data presentation for publications, and provides design recommendations for public health and clinical research, including preparation of grant proposals. Biostatistics faculty and research staff members conduct all consultation sessions with students observing, modeling, and participating in the consultations.

In the capstone seminar, students present their experience and the statistical issues that emerged in their consultations, developing statistical report writing and presentation skills essential to their professional practice in biomedical and public health research projects.

Paul McCullough Director of Academic Programs Department of Biostatistics Columbia University [email protected]

A growing number of our students decide to pursue doctoral studies after graduation. Graduate placements of MA in Statistics Alumni span several countries, institutions, and diverse fields and disciplines.

To support our qualified MA Students, we offer research and faculty-led hands-on project opportunities.  In particular, the MA Mentored Research Course and select Ph.D. level courses are available. 

MA Mentored Research is a mentored on-campus learning and research program providing our PhD-oriented MA students with research experience. It allows our students to gain experience and skills to grow as researchers.  Topics span various areas, ranging from machine learning and data science to specific problems in financial and medical research.  Meanwhile, faculty mentors receive research support from our students who have rigorous preparation in statistics, data science, and machine learning. 

In addition, our students can apply for the department’s yearly Joint Statistical Meetings (JSM) Conference Awards to present their work, network with statisticians in academia, industry, and government and participate in professional development opportunities. The award covers the full cost of conference registration and a portion of travel and lodging costs.

Our Fall Ph.D. Information Session is open to all MA students. It is moderated by professors and advanced Ph.D. students. The discussion revolves around topics relating to academic preparation and career opportunities in research and the corporate world. 

Students interested in pursuing a Ph.D. are encouraged to register for the Professional Development Course STAT GR 5391 in their first semester.  The first half of the semester of this course supports career building for all MA Students.  The second half offers specific support for the PhD-oriented students. 

• Alumni Profiles

Class of 2017

"I am a first-year mathematical informatics PhD student at the University of Tokyo, Graduate School of Information Science and Technology at the Yamanishi Lab researching model-change detection in online advertising platforms. Studying applied data science taught me how important it was to improve my programming skills. My statistical inference courses gave me a solid theoretical foundation, especially when inventing new algorithms and frameworks, and my machine learning courses gave me an understanding of machine-learning algorithms and their relationships. These are three very essential areas to master in order to become a successful data and/or algorithm scientist."

Class of 2014

"I am currently a Research Engineer at Verizon Media where I am responsible for identifying business trends and discovering information hidden in vast amounts of data, and working closely with the business team to develop and improve new product features. My daily work typically includes creating various machine learning-based tools, conducting end-to-end testing to deliver better products, performing statistical analysis, and doing ad-hoc analysis and presenting results in a clear manner. After completing my MA at Columbia, I joined North Carolina State’s statistics department where I obtained my PhD. My experience in the MA program provided me with a solid foundation in research and methodology. The faculty supported my interests and guided me through the program and after."

Yiran Jiang

Class of 2018

“I am a second year PhD student in Statistics at Purdue University. I received my Bachelors degree in Economics at Fudan University and Masters degree in Statistics at Columbia. I took 15 courses during my MA study, most of them are from the Department of Statistics. These courses helped me build concepts in Statistics and allowed me to continue my study in PhD level courses.”

For more Alumni Profiles, click here.

PhD Placement

Graduates of the MA in Statistics program have been accepted into the following Ph.D. programs ( *Note: Information provided is self-reported by graduates ):

2023 – 2022 (partial survey results)

2021 – 2018 (partial survey results)

2017 and Earlier  (partial survey results)

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Doctoral Program Requirements

Unless otherwise stated, all doctoral program requirements apply equally to PhD and DES students.  

ALGORITHMS PREREQUISITE

Analysis of Algorithms is the core of Computer Science, which unites the many disparate subfields.  All doctoral students are expected to complete an acceptable lecture course (graduate or upper-level undergraduate) in Analysis of Algorithms, with grade B+ or higher, prior to entering the program.  Sometimes new doctoral students are admitted without a prior Analysis of Algorithms course.  Those students are required to complete CSOR W4231 during their first or second semester in the program.  Further details about course requirements are posted here . 

BREADTH REQUIREMENT

A total of ten  distinct courses are required.  All ten courses should be completed by the end of the fifth semester, at the pace of two courses per semester.   An undergraduate Analysis of Algorithms course that satisfies the prerequisite does not satisfy the breadth requirement, only graduate lecture courses can be counted towards the breadth requirement.  B+ (“PhD pass”) is the minimum acceptable grade for doctoral students in all courses.  A grade of B or lower is considered failure and does not count towards the course requirement.  The same course may be repeated until the minimum B+ grade is obtained or a different course substituted. The average grade across all courses applied to the course requirement must be A- or higher. Further details about course requirements are posted here . 

DISTRIBUTION COURSES

Doctoral students must complete at least four graduate lecture courses from the approved distribution course lists, including at least one from each of the Artificial Intelligence and Applications, Systems and Theory lists, and the fourth from any of these three approved lists.  The currently approved distribution courses are as follows:

Further details about doctoral course requirements are posted here . 

ELECTIVE COURSES

In addition to the four distribution courses, doctoral students must complete six elective graduate lecture courses approved by the student’s advisor.  Additional courses from the approved lists, beyond the four needed to satisfy the distribution requirement, may be taken as electives. Most other graduate lecture courses offered by the Computer Science Department (or offered by Computer Science jointly with other departments) may be taken as electives, including 4995 and 6998 topics courses.  At most two of the six electives may be graduate lecture courses offered by other departments besides Computer Science.  Further details about course requirements are posted here . 

All DES students and most PhD students arrange a research advisor during the admissions process prior to enrollment, and work closely with him or her on directed research from their first day in the program.  Some doctoral students have two or more co-advisors.  Almost all doctoral research advisors are tenured or tenure-track faculty members in the Computer Science Department.  But in rare cases a PhD student’s research may be advised by a research scientist or an affiliated faculty member from another department, in which case the PhD student must also have a departmental advisor who is a tenured or tenure-track faculty member in Computer Science.  The departmental advisor is responsible for tracking the student’s progress through doctoral program milestones, but is not responsible for the student’s research or funding.  Both advisors are expected to represent their students at the Semi-Annual Review of all doctoral students held near the end of the fall and spring semesters.  Further details on the department’s advising policy and Semi-Annual Review are posted here . 

DIRECTED RESEARCH

The primary focus of our doctoral program is research, with the philosophy that students learn best by doing – beginning as apprentices and becoming junior colleagues working with faculty on scholarly research projects.  All PhD and DES students are required to conduct productive research under the direction of their advisor throughout the program.  For PhD students, this should be half-time until completion of the coursework, teaching and candidacy exam requirements, and thereafter full-time until distribution of the dissertation.  PhD s tudents are also expected to participate in departmental and laboratory activities throughout all fall and spring semesters of the program.  The policy on outside activities by PhD students is here .   

The directed research requirement is indeed a requirement , never waived, regardless of funding source, including employer-supported DES students.  Insufficient or inadequate research progress is deemed unsatisfactory progress: the doctoral student is normally placed on probation and can be immediately dismissed from the program.  However, on appeal of the student’s advisor, one semester’s grace can be granted by the full faculty.  

CANDIDACY EXAM

The candidacy exam is an oral exam based on a syllabus prepared jointly by the student and his/her candidacy committee. Admission to candidacy (i.e., passing the exam) certifies that the student has demonstrated a depth of scholarship in the literature and the methods of the student’s chosen area of research, and has demonstrated a facility with the scholarly skills of critical evaluation and verbal expression. The candidacy exam should be completed by the end of the sixth semester or earlier, typically the semester after completing all courses, and must be completed prior to the thesis proposal. More detailed information, including the permitted composition of the candidacy committee, is  here .

Doctoral students are required to register at least two weeks in advance for their Candidacy Exam using the department’s Doctoral Program Milestones Registration Form .  Contact the PhD Program Administrator with any questions about the registration form. 

THESIS PROPOSAL

In the thesis proposal, the student lays out his or her intended course of research for the dissertation.  If the student passes the written and oral components of the proposal, the thesis proposal committee signs a form to recommend that the candidate proceed.  The proposal should be completed by the end of the eighth semester.  The university’s permitted composition of the dissertation prospectus committee and other requirements for the proposal are specified here .  Additional department-specific requirements are   here .

Doctoral students are required to register at least two weeks in advance for their Thesis Proposal using the department’s Doctoral Program Milestones Registration Form .  Contact the PhD Program Administrator with any questions about the registration form. 

DISSERTATION AND DEFENSE

The doctoral dissertation and defense is typically completed during the fifth or sixth year in the program. Some very highly motivated students, particularly in theoretical areas, may finish in less time.

Various forms and instructions for filling out the forms, composition of the dissertation committee, handling of remote participants in the defense, revision and deposit of the dissertation, and many other topics, are available from the  GSAS Dissertation Office .   Dissertation formatting requirements, including a latex template, are here .  It’s particularly important for both the student and the advisor to review the university’s detailed requirements here about forming the dissertation committee, distributing the dissertation, and scheduling the defense.  

Defenses are typically accompanied by a public seminar.  In CS, we always hold that public seminar immediately before the defense.  When a student schedules their “defense”, they should schedule enough time (~2 hours) for both that public seminar and the official defense.   The department’s Doctoral Program Milestones Registration Form and the university’s Application for the Dissertation Defense form for PhD ( Application for the degree of Doctor of Engineering Science for DES) must be submitted by the student to the department’s PhD Program Administrator at least six weeks in advance of the anticipated defense date.  

OTHER REQUIREMENTS:

All doctoral students are required to fulfill two “teaching units”, ideally approximately the total workload of half-time for one semester, but the actual workload may vary widely.  Both teaching units must be for courses approved by the department’s Academic Committee as Computer Science courses, where the CS department is responsible for staffing ( assigning Instruction assistants ), and occur during a regular academic semester while the student is enrolled in the doctoral program. Most students complete their teaching units during their second or third year, but there are no timing restrictions on which semesters (prior to MPhil ) students can do their teaching units.  When students complete their teaching units is determined by their advisor.  More detailed information is here .

COMMUNITY SERVICE

The Department of Computer Science takes pride in maintaining a well-developed sense of community, and sees as an essential part of its doctoral program the preparation of its students for this important aspect of their future careers.  It therefore strongly encourages students to contribute a year of service to the department’s professional, operational, or social needs, preferably during their second and/or third year in the program. A list of community service positions normally held by doctoral students is available in mice .

MPHIL FOR PHD STUDENTS

The en-course degree of Master of Philosophy is conferred upon a PhD student who has satisfactorily fulfilled all milestones except the proposal and dissertation. This includes all courses, teaching, and candidacy exam. The MPhil also requires completion of six Residency Units (RUs) and sixty graduate points beyond the undergraduate degree.  Two RUs and thirty points of advanced standing are granted for completing the masters degree. See the university requirements for the MPhil .

Last updated on June 5, 2024.

Find open faculty positions here .

Computer Science at Columbia University

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In the News

Press mentions, dean boyce's statement on amicus brief filed by president bollinger.

President Bollinger announced that Columbia University along with many other academic institutions (sixteen, including all Ivy League universities) filed an amicus brief in the U.S. District Court for the Eastern District of New York challenging the Executive Order regarding immigrants from seven designated countries and refugees. Among other things, the brief asserts that “safety and security concerns can be addressed in a manner that is consistent with the values America has always stood for, including the free flow of ideas and people across borders and the welcoming of immigrants to our universities.”

This recent action provides a moment for us to collectively reflect on our community within Columbia Engineering and the importance of our commitment to maintaining an open and welcoming community for all students, faculty, researchers and administrative staff. As a School of Engineering and Applied Science, we are fortunate to attract students and faculty from diverse backgrounds, from across the country, and from around the world. It is a great benefit to be able to gather engineers and scientists of so many different perspectives and talents – all with a commitment to learning, a focus on pushing the frontiers of knowledge and discovery, and with a passion for translating our work to impact humanity.

I am proud of our community, and wish to take this opportunity to reinforce our collective commitment to maintaining an open and collegial environment. We are fortunate to have the privilege to learn from one another, and to study, work, and live together in such a dynamic and vibrant place as Columbia.

Mary C. Boyce Dean of Engineering Morris A. and Alma Schapiro Professor

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Data Science

Nursing researchers in the School generate and use large and complex data sets to evaluate, predict, and protect patient and population health across the lifespan. Collecting and analyzing large data sets offers a unique opportunity for researchers to understand the behavioral, biological, and omic underpinnings of health with the goal of preventing and managing illness. Our faculty have expertise in and are conducting numerous research projects using major national data sets such as the Outcome and Assessment Information Set (OASIS) and other big data from the Centers for Medicare and Medicaid Services (CMS), as well as the National Health and Nutrition Examination Survey (NHANES) and the Minimum Data Set for long term care (MDS).

In addition, funded projects have led to the development of large, rich databases such as those from four NewYork-Presbyterian affiliated Hospitals containing information on hundreds of thousands of patient discharges and the Washington Heights/Inwood Informatics Infrastructure for Community-Centered Comparative Effectiveness Research (WICER) data which continue to be used in ongoing research and secondary data analyses. 

Some examples of areas of data-based research in the School of Nursing include natural language processing technologies to better recognize when nurses first begin noticing subtle indicators of patient decline and big data analytic research which integrates patient-level data with person-level self-reported information, providing a link through which to prospectively conduct comparative effectiveness research studies.

Researchers

Suzanne b. bakken, phd, ms, bsn, faan, facmi, fiahsi.

• Professor of Biomedical Informatics
• Alumni Professor of the School of Nursing

Research Approaches: Informatics, Interdisciplinary, Precision Health Research Interests: Latino, Symptom Science, Self-Management, Informatics, Information Visualization, Precision Health

Veronica Barcelona, PhD, MSN, PHNA-BC, RN

• Assistant Professor of Nursing

Research Approaches: Clinical, Informatics

Research Interests: pregnancy, childbirth, racism, discrimination, epigenomics, DNA methylation, electronic health records, stigmatizing language

Sarah Collins Rossetti, PhD, BSN, RN, FAAN, FAMIA, FACMI

• Associate Professor of Biomedical Informatics and Nursing

Haomiao Jia, PhD, MS

• Professor of Biostatistics (in Nursing) at CUMC

Research Approaches: Comparative Effectiveness, Economic Analysis, Health Services and Policy Research Interests: Elderly, Suicide, Disability, Mental Health, Social Determinants of Health, Cost-Utility Analysis, Quality-Adjusted Survival Time, Survival Analysis

Meghan Reading Turchioe, PhD, MPH, RN

Research Approaches: Dissemination and Implementation, Informatics, Interdisciplinary

Research Interests: Data Visualization, Natural Language Processing

Maxim Topaz, PhD, MA, RN, FAAN

• Elizabeth Standish Gill Associate Professor of Nursing

Research Approaches: Clinical, Informatics, Precision Health Research Interests: Information Technology, Home Care, Patient Prioritization

Yihong Zhao, PhD, MPhil, BS

• Professor of Data Sciences (in Nursing) at CUMC

Research Approaches: Interdisciplinary

Research Interests: Substance Misuse, Mental Health Disorders, Brain Image Data Analyses, Genetic Studies, Machine Learning Approaches

Maryam Zolnoori, PhD

• Assistant Professor of Health Sciences Research (in Nursing)

Research Approaches: Informatics, Interdisciplinary, Precision Health

Research Interests: Speech Recognition, Speech Analysis, Large Language Models, Machine Learning, Natural Language Processing, Decision Support Systems

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.

Civic Values Stronger Among Girls, Columbia University Study Finds

Girls are more likely than boys to tolerate extreme viewpoints, volunteer in their communities and experience feelings of patriotism, according to a columbia university study of democratic values among eighth graders in new york city schools. the findings emerged in a study of the factors that help young people develop democratic values, such as political tolerance and community involvement. eighth graders in public, private secular and private religious schools participated in the study, carried out by graduate students at columbia�s school of international and public affairs under the direction of patrick wolf, assistant professor of political science. female students were 7.8 percent more likely to volunteer in their communities while students with some private education were 15.2 percent more likely to volunteer than students educated entirely in public schools. schools that expose students to community leaders through assemblies or workshops produced more tolerant students, the researchers found. similarly, students who discuss politics with friends and family are more tolerant than those who do not. students who attend schools that encourage participation in school meetings were more tolerant and willing to volunteer more often. students allowed to participate in school curriculum development were more likely to volunteer in the community. "clearly, schools can make a big difference in promoting democratic values," professor wolf said. "our results indicate that in order to produce a higher level of civic values in students, the students themselves must become involved and participate in the learning process." professor wolf said girls held stronger positions on every measure of democratic values, particularly in tolerance for viewpoints on the fringes of american social and political life, such as atheism, religious fundamentalism or neo-nazism. "while this finding is interesting in its own right, we find it even more intriguing because previous studies indicate that by the time students reach college age, men tend to be more tolerant than women," professor wolf said. the columbia researchers found that public school students are more patriotic than their private and parochial school counterparts but that students at private secular schools are more tolerant of extremist viewpoints. students at religious schools were more likely to volunteer than students in other schools. "the findings illustrate that public, private and parochial schools each have unique strengths," said professor wolf. "our data show that students with a combination of both private and public schooling exhibit the highest levels of democratic values." wolf said that while certain schools may promote one indicator of democratic values, such as patriotism, they may detract from another, such as tolerance. school policies that encouraged student and community participation in school affairs resulted in higher levels of democratic values among students, the study found. the research was completed in wolf�s workshop in applied policy analysis, taken by students in the master�s of public administration program. the graduate students administered surveys to 590 students in three public schools in brooklyn, the bronx and staten island, 136 students in six private secular schools, all in manhattan, and 197 students in eight private religious schools in manhattan, staten island and brooklyn. among the students surveyed, 51.4 percent were female and 48.6 percent were male. white students made up 51.3 percent of the participants while 19.5 percent were hispanic; 10 percent, black; 11.9 percent, mixed race; 6.9 percent, asian; and 0.4 percent, american indian. over half of the students, 55.8 percent, identified themselves as roman catholic, while 16.7 percent were christian (non-catholic); 12.1 percent, jewish; 8.5 percent, hindu, muslim, buddhist or mormon. nearly 7 percent did not have a religious affiliation. twenty-four percent reside in manhattan; 29.4 percent in staten island; 17.9 percent in brooklyn and 28.8 percent in the bronx..

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School of Data Science to Launch Ph.D. Program, Formally Joins College of Computing and Informatics

UNC Charlotte’s School of Data Science will soon expand its academic offerings with the establishment of a Doctor of Philosophy in Data Science. Approved by the UNC System Board of Governors in May 2024, the degree program will enroll its first cohort of students in fall 2025, pending approval of the Southern Association of Colleges and Schools Commission on Colleges. 

Established in response to the skyrocketing growth of the data science industry in North Carolina and globally, the new doctorate will offer two pathways for students, providing training for both future industry practitioners and university educators. This transdisciplinary program will emphasize the core technical skills of machine learning, artificial intelligence and statistics along with the social implications and ethics of data use. The program builds on the Master of Science and Bachelor of Science programs offered by the school. Its establishment is the latest example of the University’s commitment to data science, coming over 10 years after the founding of UNC Charlotte’s Data Science Initiative.

 “UNC Charlotte is always working to add and evolve academic programs with an eye toward the future. The creation of the new data science doctoral program is the latest example of our ongoing efforts to align our curriculum to the demands of industry and will allow our thriving School of Data Science to further build on its track record of interdisciplinary innovation,” said Jennifer Troyer, provost and vice chancellor for academic affairs.

The new Ph.D. will become UNC Charlotte’s 25th doctoral degree program.

A new college home and transition in leadership The doctoral program will be enhanced by the School of Data Science formally joining the University’s  College of Computing and Informatics . 

This new structure will support and amplify the school’s ongoing mission to foster interdisciplinary research and partnership across the University, all while providing SDS with the institutional structure necessary for continued faculty expansion, student growth and research excellence as the school continues to bolster its position as an innovative data science institution working to push the field forward. The shift also will allow SDS and CCI to more effectively support the North Carolina General Assembly’s $41.2 million investment toward “Engineering North Carolina’s Future,” in service of the initiative’s call for investing in data science, cybersecurity and engineering efforts across the state.

As part of this transition, the school’s current Director of Research Dongsong Zhang was named its new interim executive director, effective May 15. Zhang is the Belk Endowed Chair in Business Analytics in the Belk College of Business. Founding Executive Director Doug Hague will work closely with Zhang throughout the upcoming academic year during the transition, as Hague begins a new role as executive director of corporate engagement for UNC Charlotte. In his new position, Hague will work hand-in-hand with University leadership, the Division of University Advancement and external partners to build relationships that strengthen UNC Charlotte’s connection with the business community and create additional opportunities for collaboration.

“With the newly approved data science doctoral program and the evolution of the School of Data Science’s relationship with the College of Computing and Informatics, SDS continues to strengthen its position as a leading data science program,” said Bojan Cukic, dean of the College of Computing and Informatics. “I am excited to continue to work alongside Dongsong Zhang in the months ahead as he and his team work to chart the school’s future. We are also extremely grateful for Doug Hague’s bold, thoughtful leadership of SDS over the years. He has played an instrumental role in the school’s incredible success, and we look forward to continued partnership with him in his new role as he works to foster innovation and industry collaboration to the benefit of our University.”

The UNC Charlotte Data Science Initiative was established in 2013. That initiative ultimately grew into the School of Data Science, founded in 2020. The Carolinas’ first School of Data Science, SDS is committed to excellence in education, research and community engagement to shape and lead the future of data science education, research and practice. Since its inception and to this day, the School of Data Science is an interdisciplinary partnership among UNC Charlotte’s College of Computing and Informatics, the Belk College of Business, the College of Humanities & Earth and Social Sciences, the College of Science, the College of Health and Human Services and the William States Lee College of Engineering.

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