phd data science texas

Natural Sciences and Mathematics

Mathematical sciences.

Doctor of Philosophy in Data Science and Statistics

The program offers extensive coursework and intensive research experience in theory, methodology, and applications of statistics (see  degree requirements ). 

• Faculty members with broad and diverse research interests are available to supervise doctoral dissertations .
• Financial support in the form of assistantships, full tuition support, and scholarships and awards are provided. Additional scholarships are available for US citizens and permanent residents.
• Our students, both domestic and international, have a strong record of starting in full-time jobs right after graduation .
• Students have opportunities to participate in active  Statistics Seminar  series and the departmental  Colloquium series.
• To enhance career prospects, students can pursue  Graduate Certificate in Data Science , and possibly use the certificate courses to fulfill the PhD degree elective requirements.
• NSM Career Success Center  is available to support professional development and experiential learning of students.  
• GRE test score is not required for admission.

100% of our 22 PhD graduates since 2020, both domestic and international, secured full-time employment within a few months of receiving their degrees. 

Placement of 2022 & 2023 PhD Graduates

See a more complete list

Assistantships

Graduate Teaching Assistantships are offered to qualified PhD students on a competitive basis. These assistantships include a monthly stipend (currently set at $2,400) along with a full tuition waiver (covering 9 credit hours per term in the Fall and Spring semesters). The assistantship additionally covers the cost of health insurance purchased through the university and most fees. Graduate Research Assistantships for advanced PhD students are also available on some faculty members’ research grants. Typically, assistantship support is provided for five years and encompasses the Summer semester as well.

All admitted students are considered for assistantships; no separate application is necessary. 

Scholarships, Fellowships & Awards

PhD students are additionally supported through the following awards:

• NSM McDermott PhD Admission Fellowship  (for highly qualified new students, offered at the time of admission)
• Dean’s Fellowship  and  EEF Scholarship  (for highly qualified new students who are U.S. citizens and permanent residents, offered at the time of admission)
• Julia Williams Van Ness Merit Scholarship  and  Mei Lein Fellowship
• Outstanding Teaching Assistant of the Year Award
• Dean of Graduate Education Dissertation Research Award
• Best Dissertation Award ,  David Daniel Thesis Award , and  Outstanding Graduate Student Award

Conference Travel Support

NSM Conference Travel Award  and  Betty and Gifford Johnson Travel Award  are available to provide financial support to PhD students to present their research at professional conferences.

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Ready to start your application?

Before you apply, visit our  How to Apply  page to get familiar with the admission requirements and application process.

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Information Science Ph.D. With a Concentration in Data Science

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Why Earn an Information Science Ph.D. With a Concentration in Data Science?

The UNT Information Science Ph.D. program with a concentration in Data Science responds to the varied and changing needs of an information age, increasing recognition of the central role of information and information technologies in individual, social, economic, and cultural affairs.

The mission of UNT's Information Science (IS) Ph.D. program is to provide a center of excellence in graduate education and research. Its primary goals are to:

• Nurture critical and reflective thinking on the fundamental issues and elements of problems of utilization of information
• Foster an environment of substantive and productive mentoring and apprenticeship
• Prepare scholars passionate about the role of information in human affairs
• Foster cross-disciplinary thinking and research

Students are recruited to the program from a wide range of disciplines and encouraged to expand and refocus their expertise and skills in cutting-edge areas of information science that cross disciplinary boundaries. The multifaceted nature of information science warrants the focusing of resources, courses, and faculties from a broad range of academic units.

• Research and publication
• Pedagogical practices
• Critical thinking
• Leadership ability
• Data analysis

Information and Data Science Ph.D. Highlights

What can you do with an information science ph.d. with a concentration in data science, information and data science ph.d. courses you could take.

Learn More About UNT

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Linguistics Information Science Ph.D.

Health Informatics Information Science Ph.D.

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Academic catalog, ph.d. in data science.

The PhD program in Data Science will prepare and train individuals who can immediately obtain positions in industry using data to guide decision-making, it can be applied to an array of industries which includes enterprise management, marketing, medical treatment options, scientific research and development services, aerospace product and parts manufacturing, securities, commodity contracts, and financial investment activities. The program is open to individuals from many backgrounds.

Admissions Requirements

Applicants who hold a Bachelor of Science degree or equivalent from any regionally accredited university, or international institutions of comparable stature, will be considered for admission.

We will also consider the following kinds of applications for admission to the PhD program:

• Applicants who hold a degree in a related field in the mathematical and computational sciences (e.g., mathematics, computer sciences, statistics, data science, economics, business analytics, computational biology, computational chemistry, engineering, health sciences) or any technical field, as long as they have completed sufficient courses in mathematics, statistics and computing, as determined by the Doctoral Program Committee
• Applicants who have a Master's degree in these fields, as well as those who have a doctoral degree in another field.
• Applicants must have at least a 3.0 undergraduate major GPA (or equivalent, as determined by the Doctoral Committee; for example, some universities do not assign GPAs).
• Applicants must provide three letters of recommendation from previous professors, supervisors, or others qualified to evaluate the applicant.
• Applicants must submit a statement summarizing her/his research interests and future plans.
• Applicants must provide the Graduate Record Exam (General or Mathematical Sciences).
• Applicants from countries where English is not the first language are required to demonstrate English proficiency. Please consult the graduate school website for required scores. 
• Exceptional applicants who do not meet all of these requirements (with the exception of a required score on the TOEFL exam) may be accepted conditionally but must maintain a 3.5 GPA in the first 12 units of instruction. After they have satisfied this requirement, they will be admitted to regular status.
• Both full-time and part-time students will be accepted into the program.
• Students transferring from other graduate programs will be accepted if they meet all requirements listed above; their previous coursework will be credited upon evaluation by the Doctoral Program Committee.

Undergraduate coursework should include at least one semester of calculus and statistics and at least one upper-division course in either mathematics, statistics or computing.  Undergraduate preparation in the core fields of mathematics, computing and statistics is highly recommended. Basic computer skills are essential, including word processing, database development and maintenance, and operational knowledge of statistical software or a basic computing software.

Candidacy/Dissertation

The student will be expected to form a doctoral committee before completing 24 credit hours, and present a research proposal to the committee members before completing 40 credit hours. The committee will meet at least once per term with the student, to evaluate progress. If the progress is deemed inadequate, the student will be given one year to remedy the situation. If progress continues to be inadequate, the student will be dismissed from the program. Inadequate progress shall be defined as not completing coursework on schedule, or not completing research objectives, as outlined by the advisor and the student’s committee.

Students will be required to pass a written qualifying exam in Data Science within 6 months of completing the core coursework. If a student fails the exam, he/she may repeat the exam once within a 6-month period. If a student fails the exam the second time, he/she will not be allowed to continue in the program. After consultation with the Doctoral Committee, such a student may be allowed to complete the requirements for a Master's Degree, using the coursework completed as part of the doctoral requirements. Once a student passes the exam she/he will be advanced to candidacy. An oral exam will be required at the end of the third year. This will be focused on the chosen research topic of the candidate and the questions will be provided by the doctoral committee members.

A doctoral dissertation, directed by the major professor and doctoral committee, will be required of all students. A member of the core faculty of the Department of Mathematical Sciences will advise the dissertation with the possibility of an associated faculty member outside of the Department of Mathematical Sciences co-advising. A candidate must successfully defend his/her dissertation orally, in a forum that is open to the public. Each student is expected to submit or publish at least one peer reviewed paper during his or her tenure at UTEP. Students will normally graduate within 4 or 5 years. Current Graduate School regulations mandate that once a student exceeds 90 units, he/she may not be eligible for a state supported assistantship.

Degree Plan

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Doctor of Philosophy in Artificial Intelligence and Data Science

The Doctor of Philosophy (Ph.D.) degree is a research-oriented degree requiring a minimum of 64 semester credit hours of approved courses and research beyond the Master of Science (M.S.) degree [96 credit hours beyond the Bachelor of Science (B.S.) degree]. The university places limitations on these credit hours in addition to the requirements of the Department of Civil Engineering.

A complete discussion of all university requirements is found in the current Texas A&M University Graduate Catalog .

NOTE: All documents requiring departmental signatures must be submitted to the Civil Engineering Graduate Office in DLEB 101 at least one day prior to the Office of Graduate Studies deadline.

Artificial Intelligence and Data Science Faculty Members

• Dr. Mark Burris
• Dr. Nasir Gharaibeh
• Dr. Stefan Hurlebaus
• Dr. Dominique Lord
• Dr. Xingmao “Samuel” Ma
• Dr. Ali Mostafavi
• Dr. Arash Noshadravan
• Dr. Stephanie Paal
• Dr. Luca Quadrifoglio
• Dr. Scott Socolofsky
• Dr. Yunlong Zhang

Admission Admission to the AI/DS track is conditional upon meeting the general admission requirements. Also, students may only be admitted to the AI/DS track if a faculty member affiliated with the track is willing to supervise (and provide funding support via GAT or GAR or Fellowship) for the student. If a current student is approved to change from one track to another, they must complete the Track Change Request Form and send it to the CVEN Graduate Advising Office so notification can be sent to their original area coordinator. Please read the CVEN department policy on changing tracks.

Departmental Requirements In addition to fulfilling the University requirements for the Doctor of Philosophy (Ph.D.) degree, a student enrolled in the Civil Engineering graduate program in the area of Artificial Intelligence and Data Science area must satisfy the following department requirements.

• A minimum of 32 credit hours of graduate-level coursework taken through Texas A&M University [a minimum of 24 credit hours if the student already has taken at least another 24 credit hours of graduate course work for the Master of Science (M.S.) or Master of Engineering (MEng) degree].
• Remaining coursework requirement can be met by 32 hours of CVEN 691.
• Qualifying Exam
• Degree Plan
• Written Preliminary Exam
• Research Proposal
• Oral Preliminary Exam
• Completion of Dissertation
• Final Defense

Dissertation Topic Students pursuing the AI/DS track would work on dissertation topics with a great extent of interdisciplinary elements spanning across civil engineering and computer science/AI. Such interdisciplinary research would require a student to develop depth of knowledge and skills across both domains.

Committee The committee of Ph.D. students in the AI/DS track can be composed of faculty from different departments with backgrounds and skills related to the subject matter of the dissertation research.

Students in the AI/DS track are strongly encouraged to form their dissertation committee prior to the qualification exam. If the dissertation committee is formed prior to the qualification exam, the exam questions will be developed by the committee in coordination with the AI/DS Track Coordinator. If the student's dissertation committee is not formed at the time of the qualification examination, the Track Coordinator and the student advisor will handle the development of the qualification examination.

Ph.D. in Statistics

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

Program Description

Degree type.

approximately 5 years

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

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

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

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

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

Dissertation research

Coursework Timeline

Year 1: focus on core learning.

The first year consists of the core courses:

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

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

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

Year 2: Transitioning from Student to Researcher

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

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

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

Year 3: Advance to Candidacy

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

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

Year 4+: Dissertation Completion and Defense

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

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

General Information and Expectations for All Ph.D. students

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

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

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

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Data science is an emerging discipline that combines mathematics, computing and statistics to develop and apply methodologies required for data-driven industries. There is a high demand for data science professionals in many industries including technology, government, utilities and banking.

The department of Mathematical Sciences offers a PhD program in Data Science to prepare and train individuals who can immediately obtain positions in industry using data to guide decision-making. The program is interdisciplinary and open to individuals from many backgrounds.

Each student's program will be designed to meet individual interests and goals.

Application Deadlines

Deadlines for spring admission:.

• October 1 for International and domestic applicants.

Deadlines for Fall admission:

• February 1  for International and domestic applicants.

For both semesters, there may be special cases after the deadlines that will merit review for admission and funding.

Admission Requirements

Entry into the program requires Calculus I, II, and III (MATH 1411, MATH 1312, and MATH 2313), Matrix Algebra (MATH 3323),  Principles of Mathematics (MATH 3325),  Introduction to Analysis (MATH 3341), Probability (STAT 3330), and Statistics Inference (STAT 4380) or the equivalent of these courses.

In addition, it is recommended that the student possesses a good working knowledge of a high level computer language such as C++, JAVA, SAS, R (Splus), Python, or Matlab.

A complete application to the graduate program requires official transcripts, a statement of purpose, GRE scores, and 3 letters of recommendation. This application packet is reviewed for admission and funding for Teaching Assistantships by the program's faculty committee. A separate application for a Teaching or Research Assistantship is not needed.

A minimum TOEFL score of 213 (550 or higher on paper based TOEFL; minimum score of 6.5 on the IELTS; minimum score of 79 on the IBT - Internet Based - TOEFL) is required for international applicants whose first language is not English or who have not completed a university degree in the U.S. or other English-Speaking institution.

Applicants who do not have all the prerequisite coursework for full admission into the Data Science PhD program may be provisionally admitted and will be required to complete the leveling courses after entry into the program. 

Degree Requirements

The required number of credit hours depends on the candidate's previous course history and will be evaluated by the Director of the Ph.D. program. The general degree plan requirements, pending evaluation of previously taken courses, are:

All students in the program will be expected to complete and take qualifying exams for the following core required graduate courses:

• DS 6380 and DS 6381: Mathematical Foundations of Data Science I and II
• DS 6474: Introduction to Data Mining
• MATH 6330: Computational Methods of Linear Algebra
• STAT 6385: Statistics in Research

The remaining courses will usually be selected from the list below:

• CS 6322: Topics in Advanced Database Systems
• CS 6334: Parallel and Concurrent Programming
• CS 6350: Advanced Algorithms
• CS 6361: Machine Learning
• CS 6362: Data Mining
• DS 6339: Data Visualization
• DS 6494: Statistical Data Mining
• DS 6335: Introduction to Data Science Collaborations (SC Lab)
• DS 6336: Mathematical Applications in Data Science
• DS 6382: Statistical Theory for Big Data
• DS 6384: Linear Models for Data Science
• DS 6386: Stochastic Differential Equations and Appl.
• DS 6388: Multivariate Stat. Met. High Dimensional Data
• DS 6390: Data Science Research Collaborative
• DS 6392: Advanced Computational Data Science
• MATH 6311: Topics in Applied Mathematics
• MATH 6329: Numerical Analysis
• MATH 6335: Techniques in Optimization
• MATH 6343: Numerical Solutions to Partial Diff. Eq.
• MATH 6321: Measure and Probability Theory
• STAT 6329: Statistical Programming
• STAT 6331: Sampling Theory
• STAT 6335: Experimental Design
• STAT 6336: Categorical Data Analysis
• STAT 6354: Post Genomic Analysis
• STAT 6370: Special Topics
• STAT 6386: Stochastic Processes
• STAT 6388: Multivariate Data Analysis
• STAT 6390: Non-parametric Statistics
• STAT 6391: Time Series Analysis
• STAT 6392: Statistical Computing
• STAT 6393: Survival Analysis
• STAT 6397: Longitudinal Data Analysis
• STAT 6428: Introduction to Statistical Analysis

For course description search the UTEP's Course Catalog. 

Apply on the Graduate School website , specifying the semester you want to start the PhD degree in Data Science.

Accepted applicants are eligible and may be considered for Teaching or Research Assistantships positions (available for students attending school full-time). IF funded, applicants will receive an offer letter for either a Teaching Assistant (TA) or Research Assistant (RA) position. These positions provide an in-state tuition waiver if required.

For the most current information on degrees offered and their requirements, please visit the Mathematical Sciences section of UTEP's Graduate Catalog. 

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Ph.D. in Data Science

The ph.d. in data science at smu is distinctive because of its highly interdisciplinary nature..

Most existing Data Science Ph.D. programs are either housed in a single department, such as Statistics, Computer Science, Operations Management or Business Analytics; or they focus on a single disciplinary area of research, such as Business or Medicine.

The program’s core curriculum consists of courses in Computer Science, Operations Management, Statistics, and Data Science, and elective courses go beyond those disciplines to include Mathematics, Finance, Marketing, Education, Psychology, Chemistry, Game Design, Economics, and more. Student and faculty interest will continue to set directions for how the program evolves in the future.

Another distinctive feature are the research rotations that students engage in after having completed 4 semesters of coursework.

The goal of this program is to recognize that data science research can inform nearly every discipline at the university and beyond; and that the future of research and work in data science will not be limited to specific and restricted areas.

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• Programs : PhD in Information Studies

PhD in Information Studies

alt="Doctoral PhD students collaborating in the Information eXeprience Lab"

Where Grand Challenges Meet Interdisciplinary Solutions

We live in the Information Age, offering unprecedented opportunities but also unmatched threats and challenges. Information systems and technologies are fundamentally shaping the behaviors of individuals, organizations, and society. To understand the dynamics of our world, and to help shape a future that reflects social values, research at the School of Information crosses disciplinary divides, bridges the arts and the sciences, and applies human insights to technological advances.

Our flexible curriculum and immersive mentorship with  world-class faculty who are experts in their fields prepare students to become high-quality, high-impact researchers, scholars, and teachers. Throughout the doctoral program, students will learn to reason and evaluate ideas and data across disciplines, see beyond current approaches to problems, and cross disciplinary boundaries in search of answers to the grand challenges facing today's modern information society.

Join Our Community of Innovative Scholars

At the School of Information, you will work with faculty who are among the best in the world in their areas of expertise. You will be immersed in an environment that offers the facilities and resources of one of the premier research universities in the US, and live in a city that is both a rapidly growing center for IT research and development and one of the country's most vibrant cities in which to live and work.

Admissions & Funding

Learn about admissions procedures as well as funding available for doctoral students.

Program Overview

Our curriculum offers flexibility to tailor your coursework and explore research interests.

Meet Our Faculty

Over 20 full-time, world-class faculty come from diverse, interdisciplinary backgrounds.

Research at the iSchool

We're tackling the greatest challenges facing today's modern information society — how to understand the extraordinary complexity of information, to discover principles and processes for managing its massive scale, and exploring ways to leverage it to enhance our lives.

PhD Degree Requirements

• Committees & Annual Review
• Coursework & Plan of Study
• Qualifying Procedure
• Proposal & Dissertation Defense

What Do We Look For?

The study of information extends beyond any existing field. Our PhD students come from a wide range of disciplinary backgrounds, and we welcome applications from interested candidates regardless of the field of study in which you completed your prior degree(s). A master's degree is not required to apply. 

We seek the best and brightest people who thrive on challenges:

People dedicated to creating new forms of information systems that can augment human and organizational capabilities.

People committed to exploring the human and technological principles and processes that underlie information complexity.

People energized by a vision of a diverse society where access to relevant information is not a luxury, but a requirement.

People appreciative of the importance and challenge of preserving and disseminating information on the human cultural record.

If you want to join us in helping to forge a better information society:  Apply to the PhD Program

Our Vision & Values

The School of Information is committed to making a positive difference in people’s lives through excellence in research, teaching, and public engagement. Our core values underpin our efforts to shape the field of information for human and social benefit.

About The University of Texas at Austin

The University of Texas at Austin is one of the largest public universities in the US. Beyond our top-ranked international graduate program, UT Austin is one of the world's premier research universities and is located in one of the sunniest and most vibrant cities in which to live and work: Austin, TX.

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35 Best colleges for Data Science in Texas

Updated: February 29, 2024

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Below is a list of best universities in Texas ranked based on their research performance in Data Science. A graph of 418K citations received by 14.3K academic papers made by 35 universities in Texas was used to calculate publications' ratings, which then were adjusted for release dates and added to final scores.

We don't distinguish between undergraduate and graduate programs nor do we adjust for current majors offered. You can find information about granted degrees on a university page but always double-check with the university website.

1. University of Texas at Austin

For Data Science

2. Texas A&M University - College Station

3. Baylor College of Medicine

4. University of Houston

5. Rice University

6. University of North Texas

7. University of Texas at San Antonio

8. Texas Tech University

9. University of Texas Southwestern Medical Center

10. University of Texas at Dallas

11. University of Texas at Arlington

12. University of Texas MD Anderson Cancer Center

13. University of Texas Health Science Center at Houston

14. University of Texas Health Science Center at San Antonio

15. University of Texas at El Paso

16. Texas State University

17. Southern Methodist University

18. University of Texas Medical Branch

19. Baylor University

20. Trinity University

21. Sam Houston State University

22. Prairie View A&M University

23. Texas A&M University - Corpus Christi

24. Texas Christian University

25. University of Houston - Clear Lake

26. Texas A&M International University

27. Texas Tech University Health Sciences Center

28. University of Dallas

29. University of North Texas Health Science Center

30. University of Houston - Downtown

31. University of Texas at Tyler

32. University of Texas Rio Grande Valley

33. Texas A&M University - Commerce

34. Texas Woman's University

35. Lamar University

Closest to Texas states to learn Data Science

Computer Science subfields in Texas

Department of Information Science

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Information science ph.d. program, doctor of philosophy degree.

The Doctor of Philosophy, Ph.D.  is a research degree. It is awarded in recognition of original scholarship and the generation of new knowledge by immersion in a topic, analysis, synthesis and creativity. When a Ph.D. is awarded, the degree carries and bestows certain rights and responsibilities that relate in large measures to serving society by exploring, shedding light upon, and resolving fundamental problems.

The Doctor of Philosophy degree is said to be fundamentally interdisciplinary. All those who pursue the degree, in one sense or another, seek to clarify some portion of our best possible image of the world. Each of those who pursue the Ph.D. seek to provide the most robust understanding and appropriate tools for enabling each member of society to live well, to make the best life decisions—to become most fully human. Doctoral pursuits follow many paths, use different toolsets, invoke different mindsets, and continue testing assumptions by different means. Over the centuries, many of these paths have clustered into discrete departments or schools. An interdisciplinary program attempts to return to an era of broader assumptions, linking paths and cross-fertilizing research. Such an approach provides resources across boundaries.

Each discipline has its foundational notions of what constitutes doctoral studies. Likewise, each institution sets administrative guidelines and constraints for doctoral studies. The goal is to ensure that society is provided with the most capable people and that each person pursuing doctoral studies has every opportunity and resource to flourish.

The University of North Texas Information Science Ph.D. Program, responds to the varied and changing needs of an information age, increasing recognition of the central role of information and information technologies in individual, social, economic, and cultural affairs. Graduates of the program are prepared to contribute to the advancement and evolution of the information society in a variety of roles and settings as administrators, researchers, and educators

UNT IS Ph.D. Program offers

• excellent research faculty across UNT serving as instructors and advisors;
• a variety of course delivery formats, including online and blended;
• a residential experience with a high level of faculty-student interaction;
• a flexible degree plan tailored to individual interests;
• a culturally and ethnically diverse community of faculty and students;
• competitive scholarship, grant, fellowship, and assistantship opportunities;
• extensive research library resources on campus and online. 

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Doctor of Philosophy in Computer Science

Program description.

The graduate programs in computer science offer intensive preparation in design, programming, theory and applications. Training is provided for both academically oriented students and students with professional goals in the many business, industrial and governmental occupations requiring advanced knowledge of computing theory and technology.

Courses and research opportunities are offered in a variety of subfields of computer science, including operating systems, computer architecture, computer graphics, pattern recognition, automata theory, combinatorics, artificial intelligence, machine learning, database design, computer networks, programming languages, software systems, analysis of algorithms, computational complexity, parallel processing, VLSI, virtual reality, internet of things, embedded and real-time systems, computational geometry, computer vision, design automation, cyber security, information assurance and data science.

The University maintains a large network of computer facilities including specialized computers for research within the program. In addition to computer science faculty, many other individuals at the University are involved in computer-related work in the physical and social sciences and in various areas of business and management. Computer science students with an interest in these important application areas may have opportunities to consult and work with talented faculty from a wide range of disciplines.

Career Opportunities

Graduates of the program seek academic positions at universities, as well as positions as researchers, senior software engineers, data scientists. Graduates often become industry experts in fields like cyber security, artificial intelligence, machine learning or natural language processing.

Marketable Skills

Review the marketable skills for this academic program.

Application Requirements

Test score required:  Yes

Deadlines:  University  deadlines  apply.

OTHER APPLICATION REQUIREMENTS

Admission Option One

• Master’s degree in computer science or its equivalent
• GPA of at least 3.5
• GRE revised scores of at least 308, 153, 155 and 4 for the combined, verbal, quantitative, and analytical writing components, respectively, are advisable.

Admission Option Two

• A BS degree in related area that includes two semesters of calculus and linear algebra
• GPA of at least 3.5 in the last 60 semester credit hours
• GRE revised scores of at least 315, 156, 159 and 4 for the combined, verbal, quantitative, and analytical writing components, respectively, are advisable.

Applicants are admitted on a competitive basis.

Contact Information

Shyam Karrah  Email: [email protected]

Dr. Ovidiu Daescu Interim Head Department of Computer Science Email: [email protected] Office: ECSS 3.904

Erik Jonsson School of Engineering and Computer Science The University of Texas at Dallas, ECW41 800 W. Campbell Road Richardson, TX 75080-3021 [email protected]

engineering.utdallas.edu

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Applied Statistics PhD

Doctoral Program

The PhD in Applied Statistics addresses the growing demand in a wide range of fields for individuals with doctoral training in statistical theory and methodology who can apply statistical methods to solve business problems.

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Why Pursue a PhD in Applied Statistics

With constant technological advancement, the need for individuals who can design experiments and analyze large, complex data sets with the latest tools and technology continues to grow.

The demand for statisticians is high, especially for individuals trained to analyze big data in the areas of biomedical development, fraud detection, cyber security and defense-related issues. Job opportunities exist in a variety of industries including education, energy, finance, government, healthcare, insurance and manufacturing .  

Research Opportunities

Research is carried out while students are taking formal coursework and during the summers. As research assistants, students are involved with faculty in joint research activities and pursue their own research objectives under faculty supervision. These activities should lead to authoring or co-authoring papers presented at academic meetings and possibly submitted for publication by the time the student is ready for dissertation research. (To compete successfully in the job market, students should give high priority to presenting papers at meetings and publications while in the program.)

Careers in Higher Education and Research

The primary focus of a doctoral program is to prepare qualified candidates for careers in higher education, teaching, and research. Data predicts a strong demand for business school faculty for the next 15 years. Becoming a university faculty member is a gratifying experience that offers collaboration with students and other faculty, as well as fair compensation.

Outside of academia, Statisticians are in high demand in the growing biomedical field to develop methods for evaluating the efficacy and safety of new medications, surgeries, and other treatments. Additionally, Statisticians are conducting cutting-edge Bioinformatics research to assess topics such as gene therapy, genomics research, aging, and many other newly developed issues.

Register for an Info Session

Interested in learning more about UTSA’s Carlos Alvarez College of Business Applied Statistics PhD program? Register to attend an upcoming Information Session where you’ll have the opportunity to review application procedures, learn admissions requirements and ask questions.

• Admission Requirements

Application Deadlines

Funding opportunities, career options, admission & application requirements.

Applications are submitted through the UTSA Graduate Application . Please upload all required documents (listed below) on your UTSA Graduate Application. It is the applicant’s responsibility to ensure completion and submission of the application, a nonrefundable application fee, and all required supporting documents are on file with UTSA by the appropriate application deadline.

Applicants are encouraged to have their admission file completed as early as possible. All applications, required documents and letters of recommendation, if applicable, must be submitted by 5:00 PM U.S. Central Time on the day of the deadline. Deadlines are subject to change.

PhD’s are generally funded with our financial package which consists of an assistantship in the form of a research or teaching assistantship with paid tuition and fees for up to four (4) years.

For more information about graduate funding, click below.

UTSA prepares you for future careers that are in demand. The possible careers below is data pulled by a third-party tool called Emsi, which pulls information from sources like the U.S. Bureau of Labor Statistics, U.S. Census Bureau, online job postings, other government databases and more to give you regional and national career outlook related to this academic program.

School of Data Science

This program is part of the School of Data Science, also known as San Pedro I, and is housed in a cutting-edge facility dedicated to advancing the field of data science and fostering innovation in data-driven research and education. San Pedro I is a state-of-the-art facility specifically designed for hands-on learning and research under renowned faculty with expertise in data science, machine learning, artificial intelligence, and more. The School of Data Science is committed to providing a world-class education that equips students with the knowledge, skills, and career opportunities needed to thrive in the rapidly evolving data landscape.

Earning a Master's Degree

While in a doctoral program, a student may earn a master’s degree provided the following conditions are satisfied:

• A student must be admitted to candidacy.
• A student is eligible to receive a master’s degree upon completion of University-wide requirements and any additional degree requirements specific to the program.
• The Doctoral Studies Committee, Department Chair, and the Graduate Associate Dean of the College must recommend students for the degree.
• The student must apply for graduation by the published deadline the semester prior to awarding the doctoral degree.
• All required coursework in the doctoral program at the time of admission to candidacy must have been taken within the previous six years.
• If the master’s degree requires a thesis, the degree cannot be awarded on the basis of the doctoral qualifying examination.
• Students will not be approved for an additional master’s degree in the same field in which an individual has previously received a master’s degree.

Course Offerings & Schedule

The PhD in Applied Statistics is offered at UTSA’s Downtown Campus only and will admit full-time as well as part-time students.

Most courses are offered during the day and full-time students must enroll for nine hours in the fall semester, nine hours in the spring semester and three hours in the summer semester. We do not recommend working full-time if you plan to pursue the full-time program.

This program is does not offer a hybrid or fully online modality. All PhD programs in the college are in-residence and admitted students are expected to complete the program in-person.

PhD full-time students normally serve as either a teaching assistant or research assistant throughout the program. These experiences are an important part of the training and overall doctoral experience.

Frequently Asked Questions

Admission process, what are the key factors on which admissions are based, and who decides.

Admission is based on

• Undergraduate transcripts (and graduate, if applicable)
• Standardized test scores
• Recommendations from former professors or employers who can speak to your ability to do doctoral-level work at UTSA

The admission committee is looking for evidence that you understand the specific nature of the program that you are applying for, that you can articulate your scholarly intentions that fit with the research interests of current faculty and that you are academically prepared to succeed in the program.

The most important part of your application is your statement of purpose. Although outstanding grades and test scores are important, you should construct a clear, persuasive, well-written statement of purpose in order to be competitive.

I am completing an undergraduate degree. Am I eligible to apply?

Yes; however, you must take additional leveling courses and complete any graduate coursework where your academic background is insufficient. The catalog states that the PhD requirement is “66 hours beyond the master’s degree.” Therefore, the time required to complete a PhD will most likely be much longer for a candidate without a master’s degree than for a candidate with a master’s degree.

When are admission decisions made?

Admission decisions are typically made in March; however, exceptionally qualified candidates are considered earlier.

Can I submit GMAT/GRE test scores after the application deadline?

No. All application documents must be received by the application deadline and incomplete applications will not be considered. You will be required to upload unofficial copies within the Graduate Admissions Application.

Can I wait to submit the foreign credential evaluation (ECE transcript) until after I am accepted to UTSA?

No. Foreign credential evaluations must be received by the application deadline for your application to be processed. Processing time may take up to three weeks, and students should plan accordingly with the admission deadlines of the programs for which they are applying.

Do you accept WES transcript evaluations?

All NACES accredited evaluators are accepted.

Program Expectations

What should i expect as a doctoral student.

Your role and the expectations will change as you progress in the program. Initially, your role will be as a student with the expectation that you attend and participate in doctoral seminars with other students. Expect to read a great deal and write papers.

To prepare to become a university professor, you will work closely with faculty members to learn how to teach. You will start as a teaching assistant and work toward teaching classes independently.

Conducting research is another area of focus where you will work closely with faculty on research projects. Under the direction of a faculty committee, you will conduct original research that will be the basis for your dissertation.

How long does this program take to complete?

Most students will need four years. Plan for at least two years to complete the coursework. Add another year to pass the comprehensive exams, develop a dissertation topic and defend your dissertation proposal. Dedicate your final year(s) to dissertation research.

Are PhD students required to teach?

Teaching is crucial to your academic career and job prospects. Every PhD student should gain teaching experience before graduating. Initially, students may work as research assistants for faculty members and may also assist in teaching various courses. For students who receive stipends, they will most likely teach an undergraduate course at the Carlos Alvarez College of Business during their program.

What are the research requirements of the PhD program?

The PhD program requires students to research while they complete formal coursework and during the summers. As research assistants, students work with faculty members in joint research activities and pursue their research objectives under the supervision of faculty members. The goal is to create papers to present at academic meetings and submit to research publications by the time the student is ready to begin their dissertation research. To be competitive in the academic job market, students should prioritize producing papers and publications while in the program.

As a PhD student, who will advise me?

Your program admission will identify an initial PhD advisor. However, as your interests and research agenda develop toward preparing a dissertation proposal, a different faculty member may emerge as the appropriate advisor for your dissertation research. Your initial advisor will help you assemble a program committee of faculty, who will advise you regarding your dissertation.

Can you waive my application fee?

You may request an application waiver if

• You are a McNair Scholar
• Active-duty military or a veteran of the US Armed Forces
• If you are an applicant who has attended a PhD Project Conference

Please complete the  Request to Waive Doctoral Application Fee  if you meet one or more of the above criteria.

Approved applicants will receive a single-use coupon code to enter into the payment field of the online application.

Can you waive the GMAT/GRE test score requirement?

We do not offer waivers for standardized test scores.

Is there a waiver for the TOEFL/IELTS exam requirement?

TOEFL scores may be waived for international students from countries where English is the official language or for non-citizens of the United States who have earned a regionally accredited bachelor’s degree or higher in the United States (or other countries where English is the official language) as indicated in the Graduate Catalog ( https://catalog.utsa.edu/policies/admission/graduate/internationalgraduatestudents/ ).

Graduate Advisor of Record

Min Wang, PhD

210-458-5381

Division of Data Science

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Welcome to the Division of Data Science (DDS), your central hub for groundbreaking research and intellectual pursuits in the field of data science. DDS is driven by its primary mission: to propel education and research initiatives across diverse disciplines within the College of Science (COS) through innovative, data-driven approaches.

Drawing strength from its interdisciplinary foundation, DDS actively fosters cross-disciplinary collaborations among faculty and students in the COS and throughout other colleges at UTA. Our commitment extends beyond fundamental research; at DDS, we are dedicated to nurturing collaborative cross-disciplinary studies, with a specific emphasis on cutting-edge AI-driven approaches across various fields.

Join us in exploring the boundless possibilities of data science and discover how DDS is shaping the future of research and education at UTA.

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Find the Best Data Science and Analytics Programs in Texas

Alex Gurevich is the CEO of FinalStepMarketing, a full-service marketing and business consulting firm.

Texas has grown to become a major data science hub in the US. According to the latest BLS data , Texas currently hosts 3.4 percent of the U.S. data science workforce.

That places the number of data science positions in the state, at the time of the research, at 5,040, with an average salary of $108,090.

With more people getting into data science as a career, more institutions have opened their doors to students willing to pursue various programs in the discipline. These include bootcamps , certifications , master’s and Ph.D. programs.

Ad techguide.org is an advertising-supported site. Clicking in this box will show you programs related to your search from schools that compensate us. This compensation does not influence our school rankings, resource guides, or other information published on this site. Got it! Featured Analytics Programs

Let’s look into the various data science and analytics programs you can enroll in Texas to kickstart your data career.

Data Science Master’s Programs in Texas

Texas has a broad selection of data science master’s programs for individuals interested in furthering their studies in the discipline. Several institutions, from San Antonio to Austin, and Denton to Houston, offer graduate programs for long-time industry professionals and recent undergraduates from related fields.

Texas institutions offer two ways of completing your Master’s in Data Science program. You can do the course on-campus or online.

Online Master’s in Data Science programs put the student on course for a career in information technology. It is an excellent option for many millennials who’ve been flocking the Lone Star State since 2019.

According to statistics, there are over 17,600 technology firms in Texas, with SmartAsset reporting that over 120,000 millennials moved to Texas recently.

Other institutions, such as Texas State University , offer a Master of Science in Data Analytics & Information Systems program. The course’s designed to equip graduates with proficient data analytics and information systems skills by teaching them how to use quantitative skills and information systems to analyze data.

Graduates of data science master’s programs in Texas often progress to working on an organization’s data and transforming it into actionable information using skills in information systems and data analytics.

How to Choose the Best Master’s in Data Science Program in Texas

Various institutions offer varying curriculums, class structures, and scholarships for Master’s in Data Science programs. When searching for the right program, the goal is to find one that aligns with your professional goals.

You might also want to find sponsored Master’s in Data Science programs with a GRE/GMAT waiver offered to eligible students.

One institution that offers sponsored programs is Southern Methodist University . The institution offers an online Master of Science in Data Science course. You can complete the course in about 20 months.

Online Data Science Programs in Texas

Texas has several institutions offering online data science programs. Each institution has a unique curriculum, set of resources, and access to faculty and mentors. Thus, you should consider all these offerings before enrolling in a Texan online data science program.

Online Texas data science schools also offer various flexibility options for distance learning. Choosing an institution that aligns best with your work and personal commitments is advisable.

Schools also offer varying opportunities for internships, employment, and research opportunities. Other institutions may also offer career counseling as part of the package.

Generally, you’ll want to check the following concerning your online data science program in Texas:

• How long the program has been around? – The longer an online data science program has been around, the more likely it has better offerings. However, you’ll be hard-pressed to find an online course quite old.
• Demos of the online student experience – Institutions offer different online learning experiences. Some use face-to-face real-time interactions with classmates to facilitate learning, while others use discussions and post interactions through asynchronous learning. All offerings are excellent. Your choice here is largely dependent on personal preference.
• The datasets/resources you’ll have access to – Several high-quality online data science programs in Texas have research centers with public/private partnerships, entrepreneur programs, sponsored competitions, incubators, and labs.
• Whether you will be working with companies on real-world problems – Top schools offering online data science programs in Texas have agreements or partnerships with companies aimed at helping students thrive in the industry. These companies include Coca-Cola, Toyota, and Amazon.

Examples of institutions offering online data science programs in Texas include:

Southern Methodist University

• Program: Online Master of Science in Data Science
• GRE Requirements: No GRE required
• Duration: 20 months
• Cost: $57,084
• Southern Methodist University, located in Dallas, Texas, offers an online Masters of Science in Data Science course where students can specialize in Business Analytics or Machine Learning.

Texas A&M University

• Duration: 20 months/5 semesters
• Cost: $25,878 – $42,275
• Texas A&M University offers an online Masters of Science in Data Science program through the College of Science and Mays Business School. The course teaches statistics, database management, and programming tools used for marketing, business management, and financial accounting.

Example Data Science Courses in Texas

Several Texas institutions offer great data science courses, especially Master’s programs. These include:

Data Science and Analytics Bootcamps in Texas

Data science and data analytics bootcamps in Texas offer a great opportunity for individuals interested in data science. However, they can’t afford a master’s degree to still pursue a career in data science.

These bootcamps are relatively short and intense. This is designed to help the students get ready faster for work in the data science field.

Some bootcamps in Texas are solely dedicated to data science or data analytics. Bootcamps tailored solely to data science teach how knowledge is extracted from raw data. Data analytics bootcamps, on the other hand, teach how insights are arrived at from the data.

Other bootcamps teach both data science and data analytics.

You can attend bootcamps in-person, online, or hybrid. The hybrid approach combines online and campus learning. Many Texas data science and analytics bootcamps offer flexible schedules to fit your work and personal commitments.

Some bootcamps offer evening or weekend classes that appeal to students working full-time.

While most bootcamps are targeted at beginners, Texas has several data science and analytics bootcamps targeting advanced learners. These may be individuals with experience in object-oriented programming, statistics, math, or databases.

Ensure you’ve gauged a boot camp’s curriculum to determine the job opportunities it will unlock for you.

Here are examples of data science and analytics bootcamps you can enroll in Texas.

Data Science PhD Programs in Texas

Getting a Ph.D. as a data scientist is the best way of securing a successful career and huge earnings. Data science doctorates get a better view of the data science academic world than undergraduate students. At this level, you get exposed to more research and innovation within the industry.

However, whether you want to do a data science Ph.D. program depends entirely on you. Despite being the top accolade for a data scientist, data science is still a very new discipline that’s constantly changing with research and innovation.

Thus, experience may triumph academic accolades in several cases.

The actual research for a Ph.D. may quickly get outdated, given the fast-paced nature of the industry. However, the exposure to research may be advantageous if you want to dive into the research facet of data science.

Thus, consider getting a data science Ph.D. if research is your stronghold.

One institution that offers Ph.D. programs in data science is the University of Texas at Dallas . They offer the course under the Ph.D. in Computer Science program. In the program, students can take the course and research opportunities in data science.

Five In-Demand Analytics Jobs in Texas

You can apply for numerous analytics jobs after going through a data science bootcamp, Master’s degree, or Ph.D. data science program in a Texas institution. These include:

Data Scientist – Sports Analytics

You can land a junior or senior data scientist sports analytics role in companies located in Texas having qualified from a certified institution. This role requires data scientists who are also passionate about sports to help in sports analytics.

The salary for this role often lands on the Texas average, ranging from $110,000 to $124,000 annually, with the option of working remotely.

Machine Learning Engineer

You can land a role as a machine learning engineer in a reputable Texan company after completing a data science program in Texas. This role covers several industries, from sports to technology.

Machine learning engineers develop, validate, and automate quantitative models while applying machine learning, statistics, simulation, and optimization. The role pays between $120,000 to $135,000 annually and often takes junior and senior data scientists.

Risk Analyst

Data scientists in Texas can work as risk analysts for big companies like TikTok. A risk analyst identifies risks in an organization’s system and finds a way to navigate them with minimal impact. Data analysts come in handy by offering the tools and processes to assess an organization’s data risk and suggest ways the risks can be mitigated.

This role also pays within the Texas average, ranging from $80,00 to $90,000 annually.

Data Visualization and Analytics

There are several full-time data visualization and analytics roles for data scientists in Texas. The role requires the data scientist to use their skills to develop and provide rich data insights to drive decisions in an organization. Most companies need this input through their full-cycle product development.

This role pays between $103,000 to $113,000 annually. 

Frequently Asked Questions

Texas offers a variety of programs including bachelor’s degrees, master’s degrees, certificates, and bootcamps in data science and analytics. These programs are available at numerous universities and colleges throughout the state, as well as through online platforms.

Notable universities in Texas offering reputable data science and analytics programs include the University of Texas at Austin, Texas A&M University, Rice University, and Southern Methodist University, among others.

Yes, many Texas universities and colleges offer online programs in data science and analytics, providing flexibility for students who cannot attend on-campus classes.

Graduates can pursue careers in various sectors including technology, healthcare, finance, energy, and government. Roles might include data scientist, data analyst, business intelligence analyst, and machine learning engineer, among others.

Financial aid, scholarships, and grants are often available for eligible students. Prospective students should check with the specific institution for available financial support options.

Consider factors such as your career goals, the program’s curriculum, faculty expertise, the institution’s reputation, alumni network, location, and the balance between theoretical and practical learning.

Related Resources

• Find the Best Computer Science Programs in Texas
• Engineering Programs in Texas
• Find the Best Design Programs in Texas
• Find the Best Data Science Courses
• Data Science and Data Scientist Jobs

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