data science phd programs in california

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» Ph.D. in Computational and Data Sciences

Computational Science is the art of creating, developing, and validating models in order to gain a profound understanding of real-life complex problems. Data Science is the art of generating insight, knowledge, and predictions by applying modern methods to large datasets.  

In Chapman University’s Ph.D. in Computational and Data Sciences program, you will collaborate on innovative research as you work closely with   nationally and internationally renowned   faculty mentors who will help prepare you to thrive in a variety of professional settings, from academia to private industry, scientific research labs to government agencies. You will learn to design and implement mathematical models and refine quantitative analysis techniques to solve complex scientific problems. Develop your dissertation with focus on advancement of theory and applications of statistical, machine learning and AI in diverse data science related fields such as medicine and epidemiology, climate and Earth hazards, big data and high-performance computing, drug design, genetics, natural language processing, bioinformatics and biotechnology, economics, and sports analytics. 

Employment and Future Opportunities

In our tech-driven world, employers are increasingly recognizing the value of data science professionals.  According to U.S. News and World Report, the Bureau of Labor Statistics projects 35.8% employment growth for data scientists between 2021 and 2031. In this period, an estimated 40,500 jobs should open up.  

Graduates from the program have gone on to work in a variety of industries, such as:  

• Artificial Intelligence and Machine Learning 
• Higher Education Institutions 
• Healthcare 
• Entertainment Industry 
• Government Agencies 
• Large Tech Companies such as Amazon, Microsoft, Google, Yahoo 

Ph.D. Student Handbook

For the latest information on the current curriculum, please visit the Graduate Catalog .

Prerequisites

It is expected that students admitted to the CADS Ph.D. program will have completed substantial preparatory coursework as an undergraduate major or minor from a regionally accredited institution in one of the following disciplines, or the equivalent: Mathematics, Statistics, Computer Science, Data Science, Physics, Electrical Engineering, or Software Engineering.

Preparatory coursework must include the following courses, or the equivalent:

• Linear Algebra
• Multivariable Calculus
• Differential Equations
• Computer Programming: Data Structures preferred (R, Python, and SQL)
• Probability and Statistics (Distributions, Confidence Intervals, Hypothesis Testing, Linear Models)

Total Credits - 70

Admission Requirements

An undergraduate degree specifically in computational science is not required for admission. The program will consider applicants from a broad range of undergraduate and master’s level science disciplines (e.g. biology, chemistry, computer science, biochemistry and molecular biology, mathematics, physics). Admission will depend on the relationship between the student’s goals and the program’s objectives as well as the likelihood that the student will benefit from the program.

1 . Prerequisite Courses

2. Application Requirements

Admission to the program may be achieved by the completion of the following requirements:

Online application for admission (including $60 non-refundable application fee)

Official transcript from degree granting institution. If prerequisite courses have been taken at schools other than the degree granting institution, those transcripts must also be submitted. Applicants must have earned a minimum grade point average of 3.00.

Graduate Admission Test Scores (School Code: 4047); the Graduate Record Examination (GRE) general test scores must have been taken within the last five years. Applicants must achieve the following minimum scores:

• Verbal: 153
• Quantitative: 146
• Analytical Writing: 4.0

To request a GRE waiver, complete this form . Once you submit your waiver request, you will be notified by email in 1-2 weeks if the GRE has been waived for you. Each waiver is reviewed on a case-by-case basis.

Letters of recommendation - two letters of recommendation are required, including one from an academic source which describes your professional and academic abilities.

Statement of Intent - a 750 word essay; applicants are expected to address science topics they are interested in and how they envision applying computational science in those areas.

Resume - a resume or curriculum vitae is required

International student application requirements

Chapman's language of instruction is English. If you have not received a bachelor's degree (or higher) at an institute where English was the language of instruction, you must demonstrate English proficiency by submitting official scores from an English language exam. You can find additional information here .

Official Transcripts and Diploma

• Your application requires official transcripts in both the native language, and in English. If your university does not provide translations of your transcript, you will need to have your transcript translated, line-by-line and word-for-word exactly. You will need to submit both the official transcript and the official translation.
• If your university only provides one official transcript, you will need to submit a notarized copy. You will need to take your official transcript and have certified copies made, and translated into English if needed. These documents should be stamped by the legal notary who made the copy and/or translation. We do not accept uncertified copies directly from students. Please note that official documents will be required upon acceptance.
• While your diploma will not be required with your application, your enrollment into Chapman University will be dependent upon submission of your official diploma. Should you be admitted, your diploma will need to be submitted in both the native language, and in English. You will need to submit both the official diploma and the official translation. If your university only provides one official diploma, you may send a notarized copy, or bring the original documents into our office at the time classes begin. 

Supplemental Application

• The International Supplemental Application is the financial certification form that provides comprehensive information about your passport, I-20 requirements, and financial support for your studies. This form is required for F-1 student visa applicants.
• Should you be admitted into our program, you will be sent information on how to access the Supplemental Application.
• If you hold a U.S. passport, or are a permanent resident, you do not need to submit this document. You will apply as a domestic student.

Tuition Information

Financial assistance is available in the form of federal loans, department scholarships, teaching assistantships, and research assistantships.

More information can be found on the Financial Aid website or by contacting Graduate Financial Aid at [email protected]  or (714) 628-2730.

Admission – Please contact the Associate Director of International Initiatives, DSO, Monica Chen, MA, at [email protected]  regarding your application, to schedule a campus visit or for other non-program specific questions.

Application : How to Apply

International Students – View our international student admissions page for additional information regarding applying to Chapman.

Tuition - Contact Student Business Services at (714) 997-6617 for information regarding tuition, fees, billing & payments. Please note that program staff are prohibited from discussing financial information.

Federal Financial Aid - For more information, email [email protected]  or call (714) 628-2730.

Housing - For graduate student housing options, contact Housing and Residence Life at (714) 997-6603.

Q: What is required for admission to the program?

A: Please review our admissions requirements for more information. You may also contact our graduate admissions team at (714) 997-6711, or [email protected]  

Q: Am I required to take the TOEFL (or equivalent)?

A: Applicants who have completed their bachelor’s degree or higher at an institution where English was not the primary language of instruction must submit scores for an English Proficiency exam. Chapman University's institution code for the TOEFL is 4047. 

Q: Who should my letters of recommendation come from? May I submit additional letters?

A: Letters of recommendation should come from former faculty members or those you've worked with in industry who can attest to your academic and professional abilities. Two letters is recommended, but you can submit more if you wish.

Q: Can I send in transcripts to show coursework from non-degree granting institutions? 

A: Yes, all courses you have completed will be taken into account by the admission committee.

Q: Can I submit my application before I have all the necessary documents?

A: Yes, although some sections are required before submitting. Admissions will hold your application and notify us as your documents become available. You will not receive an admissions decision until all documents have been received.

Q: How many students are accepted each year?

A: The Ph.D. program accepts an average of 8 applicants each fall.

Q: Do you accept admissions on a rolling basis?

A: No, students are admitted once a year – for the following fall semester.

Q: What is the cost of the program?

A: The 22/23 cost of the Ph.D. program is $126,000 ($1,800 per credit regardless of residency). However, most students receive funding and TA opportunities.

Q: How long does the program take to complete?

A: Normative completion to the doctoral degree is 4-6 years, depending on the student’s level of preparation, research topic, and rate of publication.

Q: Am I allowed to attend part-time?

A: Yes, although part-time Ph.D. students are expected to provide their own funding.

Q: Is this program online?

A: No, this program is not online and does not offer any hybrid courses. 

Q: When are classes offered?

A: Most courses are offered in the afternoons and evenings.

Q: Can I transfer courses?

A: Up to 18 credits may be accepted as transfer credit. We accept both standard and online courses that meet all transfer requirements and are from regionally accredited schools.

Q: Is there financial support available?

A: Yes, highly qualified Ph.D. applicants will be offered financial packages upon admission.

Q: Do I find out about available assistantships?

A: Students who would like to be considered for assistantships should send their CV and evaluations from any previous teaching assignments to the Program Coordinator prior to the application deadline. Please specify level of knowledge in each of the following undergraduate areas: math, physics, statistics, and/or computer science.

Q: What scholarships are available?

A: Students are encouraged to apply for external scholarships sponsored by government agencies, corporations, and foundations. Some scholarship search options are found on the Financial Aid - Outside Scholarships page .

Q: What are the housing options?

A: On-campus housing is extremely limited and graduate students are encouraged to research alternative living arrangements off-campus by visiting our Introduction to Off-Campus Living page . After being accepted to the program, you can connect to the community through Facebook Off Campus Housing and Roommate Corner and Off-Campus Housing Listings .  International students should also check with International Student & Scholar Services .  

Additional Information for International Students:

Q: Are Chapman's Computational and Data Sciences degrees STEM (Science, Technology, Engineering, Mathematics) programs?

A: Yes, students in our program are eligible to apply for STEM benefits.  See the International Student & Scholar Services for more information.  You can also contact Lisa Luu-Luc, Specialist International Student & Scholar Services, at [email protected]  or (714) 744-2110, with any questions. Q:  What is OPT? 

A:   Optional Practical Training or OPT allows you to work for one year, following graduation, in a job related to your major or field of study.  See the International Student & Scholar Services for more information.  You can also contact Lisa Luu-Luc, Specialist International Student & Scholar Services, at [email protected]  or (714) 744-2110, with any questions. Q: What is CPT?

A: Curricular Practical Training or CPT allows you to participate in an off-campus paid internship that is related to your major or field of study.  See the International Student & Scholar Services for more information.  You can also contact Lisa Luu-Luc, Specialist International Student & Scholar Services, at [email protected]  or (714) 744-2110, with any questions.

• M.S. Food Science
• MS Computational and Data Sciences (CADS)
• Ph.D. Computational and Data Sciences
• Doctor of Science in Mathematics, Philosophy and Physics
• Luigi Maierù’s International School in the History of Mathematics

Cyril Rakovski, Ph.D. Co-Program Director [email protected]

Adrian Vajiac, Ph.D. Co-Program Director [email protected]

Matthew Martinez, MFA Graduate Program Coordinator [email protected]

Melissa Liberman, MA Senior Graduate Admission Counselor [email protected] Graduate Financial Aid [email protected] (714) 628-2730

• Graduate Applications
• Financial Aid

Application Deadlines

Early Admission Deadline: December  1, 2023 Regular Deadline: January 15, 2024

Applications submitted after the deadline will be reviewed on a space-available basis.

Joshua Fisher

Associate Professor Joshua Fisher is a Climate Scientist focusing on terrestrial ecosystems, water, carbon, and nutrient cycling using a combination of remote sensing, supercomputer models, and field campaigns from the Amazon to the Arctic. 

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PhD Course Requirements

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PhD Program

PhD Program Overview

Welcome to the HDSI PhD Graduate Program! 

The goal of the doctoral program is to create leaders in the field of Data Science who will lay the foundation and expand the boundaries of knowledge in the field. The doctoral program aims to provide a research-oriented education to students, teaching them knowledge, skills and awareness required to perform data driven research, and enabling them to, using this shared background, carry out research that expands the boundaries of knowledge in Data Science. The doctoral program spans from foundational aspects, including computational methods, machine learning, mathematical models and statistical analysis, to applications in data science.

Group A courses are introductory level graduate courses in the foundational areas of data science. Group B are core graduate level courses with prerequisites from Group A courses. Group C are advanced, specialized and free-standing courses, often part of the required courses in the Data Science specialization of the Graduate Program in other departments. In all three groups, required courses are indicated as such; they can not be substituted by other courses without exception approval from the graduate program committee.

The doctoral program is structured as a total of 52 units in courses from these group A, B, and C as described in detail here. Out of the 52 units, 48 units (or 12 courses) must be taken for letter grade and at least 40 units must be using graduate-level courses.

The remaining 4 (= 52 – 48) units are for professional preparation , consisting of 1 unit of faculty research seminar, 2 units of TA/tutor training and 1 unit of survival skills course taken for a passing (satisfactory) grade. Finally, as mentioned earlier, out of the 12 regular courses, at least 10 must be graduate-level courses; at most two can be upper-level undergraduate courses. 36 units or 9 courses must be completed within six quarters from the start of the degree program.

Group A: Preparatory Knowledge and Skill Areas  There are five important knowledge and skills necessary for understanding (and advancing) core data science. It is, therefore, important that all our entering students either have background preparation or have courses available in the program to ensure a successful completion of the stipulated doctoral degree program. A student can receive credit towards the Ph.D. degree for a maximum of three courses from the list of courses below

Group B: Core Knowledge and Skill Areas

Doctoral students are required to take a minimum of 6 courses for letter-grade credit from Group B courses. Students can take more than 6 courses from this group to satisfy letter grade course requirements except (satisfactory completion of professional preparation) teaching, survival skills and research seminar courses. Students who satisfy all letter-grade course requirements are expected to enroll into individual research (DSC 298) in a section offered by the faculty advisor to meet residency requirements and maintain graduate student standing during the period of dissertation research.

Four core courses are required for all Ph.D. students, including those with a Bachelors in Data Science. The four required courses are:

Group C:  Professional Preparation and Elective Courses 

Group C courses aim to provide either practical experiences in chosen specialization areas, or advanced training for students preparing for doctoral programs. The courses include required professional preparation courses: 2 unit TA/tutor training (DSC 599), 1 unit of academic survival skills (DSC 295) and 1 unit faculty research seminar (DSC 293), all of which must be completed with a Satisfactory (S) grade using the S/U option.

Research rotations provide the opportunity for first-year PhD students to obtain research experience in data analysis under the guidance of HDSI affiliated faculty members. Through the rotations, students can identify a faculty member under whose supervision their dissertation research will be completed.

A research rotation is a guided research experience lasting one quarter (10 weeks) obtained by registering for DSC 294 (4 units) with an instructor. All PhD students will participate in

a minimum of 2 research rotations during their first year, and with a minimum of two different faculty members, and as much as three rotations including summer quarter. A student may rotate twice under the same faculty member as long as they rotate with at least two faculty members. The goal is to expose students to new methodological approaches or domain knowledge and help them find an advisor for their PhD research.

Please refer to the HDSI Faculty page for a list of HDSI faculty members and their research interests. Students should reach out to faculty members to learn more about potential rotation opportunities.

An important goal of HDSI is to foster interdisciplinary collaboration. Specifically, collaboration between method researchers and domain researchers. Domain researchers specialize in a particular scientific field and collect or generate large datasets. Domain experts can partner with HDSI faculty and jointly advise a PhD. student. The site matchpoint.com was designed to facilitate the creation of such collaborations.

Outcome of each rotation program: A written report, or curated datasets with jupyter notebooks. Students are expected to work 10 hours per week.

Research rotations must be completed by the end of the Fall Quarter of the second year with a signed commitment form from a faculty advisor. Those who fail to identify a research advisor shall be advised to leave the doctoral program with an optional assessment for completion of a terminal MS-DS degree.

Formal Coursework

Requirements representing breadth and depth requirements consisting of 48 units of courses structured in three groups: foundations, core and depth areas; as well as 4 units of professional preparation including 1-unit HDSI Faculty Research Seminar, 2-units of TA/Tutor training and 1-unit of Research Skills courses to be completed with a Satisfactory grade.

Preliminary Advisory Assessment

The goal of the preliminary assessment examination is to assess students’ preparation for pursuing a PhD in data science, in terms of core knowledge and readiness for conducting research. The preliminary assessment is an advisory examination.

There is an oral presentation that needs to be completed before the end of Fall quarter of the second year. The student will propose the topic of the presentation (e.g., the outcome of a research rotation or a literature survey). The Graduate Program Committee will set up a committee consisting of two members (students can suggest committee members but there is no guarantee that only those suggested faculty members will be chosen for the committee). The oral presentation from the student will be followed by a Q&A session by the committee members. The committee will assess both the oral presentation as well as the students academic performance so far (especially in the required core courses). Students who did not get a satisfactory evaluation will receive a recommendation from the Graduate Program Committee regarding ways to remedy the lacking preparation (e.g, suggestion of courses to be taken), or an opportunity to receive a terminal MS in Data Science degree provided the student can meet the degree requirements of the MS program.

In a technical area of choice by the student by a committee set by the Graduate Committee (GradCom). This examination is to be completed before the start of the second year. Preliminary examinations will normally be scheduled annually in the Spring quarter through Summer quarter of the first year. The goal of the preliminary assessment is to assess student preparation in background courses and identify any required courses consistent with the planned research area. In rare cases, the assessment outcome may include a requirement to retake the examination. The preliminary assessment must be successfully completed no later than completion of two years (or six quarter enrollment) in the Ph.D. program

Passing a Research Qualifying Examination (UQE)

A research qualifying examination (UQE) is conducted by the dissertation committee consisting of four or more members approved by the graduate division as per senate regulation 715(D). One senate faculty member must have a primary appointment in the department outside of HDSI. Faculty with 25% or less partial appointment in HDSI may be considered for meeting this requirement on an exceptional basis upon approval from the graduate division.

The goal of UQE is to assess the ability of the candidate to perform independent critical research as evidenced by a presentation and writing a technical report at the level of a peer-reviewed journal or conference publication. The examination is taken after the student and his or her adviser have identified a topic for the dissertation and an initial demonstration of feasible progress has been made. The candidate is expected to describe his or her accomplishments to date as well as future work. The research qualifying examination must be completed no later than fourth year or 12 quarters from the start of the degree program; the UQE is tantamount to the advancement to PhD candidacy exam.

A petition to the Graduate Committee is required for students who take UQE after the required 12 quarters deadline. Students who fail the research qualifying examination may file a petition to retake it; if the petition is approved, they will be allowed to retake it one (and only one) more time. Students who fail UQE may also petition to transition to a MS in Data Science track.

Dissertation Defense Examination and Thesis Requirements

Students must successfully complete a final dissertation defense oral presentation and examination to the Dissertation Committee consisting of four or more members approved by the graduate division as per senate regulation 715(D). The primary Thesis Adviser, who will chair the Dissertation Committee, must be a senate faculty member with an appointment of 0% or more at HDSI. One senate faculty member in the Dissertation Committee must have a primary appointment in a department outside of HDSI. Partially appointed faculty in HDSI (at 25% or less) are acceptable in meeting this outside-department requirement as long as their main (lead) department is not HDSI.

A dissertation in the scope of Data Science is required of every candidate for the PhD degree. HDSI PhD program thesis requirements must meet Regulation 715(D) requirements. The final form of the dissertation document must comply with published guidelines by the Graduate Division.

The dissertation topic will be selected by the student, under the advice and guidance of Thesis Adviser and the Dissertation Committee. The dissertation must contain an original contribution of quality that would be acceptable for publication in the academic literature that either extends the theory or methodology of data science, or uses data science methods to solve a scientific problem in applied disciplines.

The entire dissertation committee will conduct a final oral examination, which will deal primarily with questions arising out of the relationship of the dissertation to the field of Data Science. The final examination will be conducted in two parts. The first part consists of a presentation by the candidate followed by a brief period of questions pertaining to the presentation; this part of the examination is open to the public. The second part of the examination will immediately follow the first part; this is a closed session between the student and the committee and will consist of a period of questioning by the committee members.

Successful defense of the dissertation presentatio n in a final examination to the doctoral dissertation committee;  Approved dissertation that must explicitly address the reproducibility requirement . This requirement can be met by providing supplementary online material consisting of code, data repositories, any evidence of use by external parties and/or where necessary through validated proof of results.

Special Requirements: Generalization, Reproducibility and Responsibility

A candidate for doctoral degree in data science is expected to demonstrate evidence of generalization skills as well as evidence of reproducibility in research results. Evidence of generalization skills may be in the form of — but not limited to — generalization of results arrived at across domains, or across applications within a domain, generalization of applicability of method(s) proposed, or generalization of thesis conclusions rooted in formal or mathematical proof or quantitative reasoning supported by robust statistical measures. Reproducibility requirement may be satisfied by additional supplementary material consisting of code and data repository. The dissertation will also be reviewed for responsible use of data.

Annual Review

Annual review of the progress in the doctoral program by the graduate committee of HDSI faculty council.

Teaching Requirements

Including completion of teacher training course (DSC 599) and minimum of one quarter of teaching experience at half-time (50%) appointment as a Teaching Assistant over the course of the degree program;

Time Limits

Assuming a student has no deficiencies and is full-time enrolled in the program, our normative length of time pre-candidacy is 3 years and 2 years in candidacy. Extension of total time from matriculation to degree beyond six years will require petition and approval from the graduate division. HDSI has instituted several mechanisms and incentives to ensure expeditious time-to-degree. These include a full-time graduate students advisor in HDSI Graduate Affairs , preliminary assessment examination and advisory in the first year, and annual 13 review of each graduate student in the Ph. D. program led by the assigned faculty academic advisor of the student, graduate scholarships funded by HDSI foundation accounts to cultivate a culture of excellence in research and dedicated staff for computing and data curation services to ensure a smooth and easy access to necessary experimental platforms.

Obtaining an MS in Data Science

PhD students may obtain an MS Degree in Data Science along the way or a terminal MS degree, provided they complete the requirements for the MS degree.

Data Sciences + Operations PhD Program

Operations Management studies how firms organize their resources and recurring activities in order to be competitive in cost, price, responsiveness and quality. The field stresses quantitative techniques ranging from applied probability to optimization and game theory.

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• ADMISSIONS EVENTS

Operations Management

Operations Management studies how firms organize their resources and recurring activities in order to be competitive in cost, price, responsiveness and quality. The field stresses quantitative techniques ranging from applied probability to optimization and game theory. Successful applicants will have strong technical and mathematical training, and we particularly encourage applicants with undergraduate degrees in mathematics, economics and/or engineering to apply at the USC Application Portal. A prior background in business is not required.

Research areas: Our faculty are interested in a wide variety of research questions; check their personal webpages for specific examples. Some indicative topics include:

• Revenue management and dynamic pricing
• Data-driven analytics
• Healthcare operations
• Market and auction design
• Sustainability
• Supply-chain and service management

Operations Management and operations research prize interdisciplinary thinking and approaches. Hence, high-quality research often leverages a range of methodologies and tools, including:

• Optimization (linear, nonlinear, combinatorial, data-driven)
• Game Theory
• Queuing Theory
• Stochastic Processes / Applied Probability
• Econometrics
• Machine Learning

High quality research can involve developing new theoretical tools in these areas, application driven case studies, or both.

Our statistics faculty are interested in both pushing the boundaries of theoretical knowledge in the field as well as applied areas. There are many interesting research projects at the intersection of statistics, computer science, econometrics, finance, and marketing. Selected topics include:

• Statistical machine learning
• High­dimensional data analysis
• Computational and statistical limits of "Big data" problems
• Structural learning (such as clustering, sparse recovery and network analysis)
• Multiple hypotheses testing, uncertainty assessment and inference problems
• Finance and econometrics
• Applications in genetics, neuroscience and health care

Cutting­edge research in these areas leverages and builds upon many mathematical tools including:

• Functional data analysis
• Graphical models and iterative algorithms
• Optimization

Collegial, Supportive and Expansive

The DSO Ph.D. program is research-focused, collegial, supportive and highly interactive. Ph.D. students are viewed as “junior colleagues” and are encouraged to become involved in academic research with faculty early in their doctoral studies through the following programs and initiatives:

• Low student-to-faculty ratio coupled with the faculty's "open-door" policy leading to frequent and meaningful collaborations. Unlike many other PhD programs, successful students often collaborate with several different faculty members, both in operations and statistics, throughout their PhD. Successful students typically co-author several papers with faculty members, which are often published in top peer-reviewed journals in the field.
• Presentations by invited faculty from around the world as part of a regular seminar provide an opportunity for students to experience the breadth of the field and meet renowned scholars. Click here for more information.
• Internal brown-bag seminars and colloquia with other research-oriented universities in Southern California

Operations Management Research

Research Environment

The structure of the Ph.D. program permits great flexibility, allowing custom-tailored courses of study emphasizing all areas of specialization within the department. In addition to our own faculty, students typically draw on the distinguished faculty and outstanding resources available throughout the University, most notably in economics, mathematics, industrial and systems engineering, computer science, and the Information Sciences Institute. The Center for Global Supply Chain Management(GSCM) and the Institute for Communication Technology Management (CTM) are also key resources to this program.

Recognized Research Authorities

The Operations Management faculty have served as senior editors and associate editors of premier management journals, including:

• Management Science
• Manufacturing and Service Operations Management
• Naval Research and Logistics
• Operations Research
• Production and Operations Management

Faculty Honors

• Fellows, Production and Operations Management Society
• National Science Foundation Grants

Learn More:   Explore the Data Sciences and Operations department to learn more about current research and faculty activities.

Statistic Research

Research by the statistics faculty emphasizes developing new methodology that grows out of business and public policy applications. In methodology, the statistics faculty's research covers a wide segment of the field of statistics including Bayes and empirical Bayes methods, data mining, categorical data, hidden state Markov models, classification rules, clustering methods, econometric and psychometric models, and general linear models. Applications include finance, marketing, real estate, health policy, defense system testing, computer science and discrimination in employment and in sentencing.

The Statistics faculty members are among the leading authorities of Business Statistics worldwide. One senior Statistics faculty member plays a leading role in the profession's advice to the federal government on statistical methodology. In addition, Statistics faculty members have served as senior editors and associate editors of premier management journals, including:

• Annals of Statistics
• Journal of the American Statistical Association
• The Econometrics Journal
• Journal of Multivariate Analysis
• Statistica Sinica
• Fellow of the American Statistical Association; Noether Young Scholar Award; National Science Foundation Grants; National Science Foundation CAREER Award

Learn More:   Explore the Statistics group to learn more about current research and faculty activities.

Faculty Coordinator:   Vishal Gupta , Associate Professor of DSO

OPERATIONS MANAGEMENT REQUIREMENTS

Sample Course Schedule

• DSO 671: Inventory Models and Supply Chain Management
• GSBA 602: Selected Issues in Economic Theory
• ISE 631: Linear Programming
• Elective (1)
• ISE 538: Elements of Stochastic Processes (or MATH 505B: Applied Probability)
• DSO 677: Dynamic Programming and Markov Decision Processes
• ISE 630: Foundations of Optimization
• GSBA 604: Regression and Generalized Linear Models for Business
• BUCO 634: Communication for Doctoral Students: Conference Papers and Presentations
• Summer Research Paper

Research Development Beginning in the first semester, students will apprentice with a faculty mentor to learn research fundamentals. Typically students are assigned to a new mentor each semester in the first year. During the summer following the first year in the program, students are expected to research and complete a Summer Research Paper.

Screening Examination In addition to the papers and examinations assigned in their first-year courses, a screening process will occur in May at the end of the first year. This process will include a review of each student's grades, an analysis of competence in written communications and a written exam based on the material covered during the first year. The results of the examination help the faculty determine students’ developmental needs and fitness to continue in the program.

• DSO 670: Current Research in Operations management
• 3 electives
• DSO 674: Queuing and Stochastic Networks
• ISE 632: Metwork Flows and Combinatorial Optimization
• 2 electives
• BUCO 633: Communication for Doctoral Students: Foundations of Academic Writing
• BUCO 637: Communication for Doctoral Students: Succeeding as a Teacher
• Independent Dissertation Preparation (2)

Research Development At this stage in the program, the relationships Ph.D. candidates have developed through the research apprentices often lead to collaborative research projects. Ph.D. candidates continue to apprentice faculty with increasing responsibility. They also begin work on their dissertations and are expected to have an approved dissertation proposal before the start of the fourth year.

Teaching Development Ph.D. candidates begin developing teaching skills by serving as a teaching assistant for one or two classes and engaging in teaching skills workshops.

Qualifying Exam The examination qualifying a student for candidacy for the Ph.D. degree is partly written and partly oral, designed, at least in part, to test the student’s fitness to undertake independent research. After successful completion of the qualifying exams, the student is admitted to candidacy.

Research Development Research is the primary activity in the fourth year and beyond. Faculty view Ph.D. candidates as “junior colleagues” and partner to write and present papers.

Teaching Development Ph.D. candidates co-teach one course with a faculty member.

Dissertation Defense & Submission The dissertation is the culminating work of a student’s independent research. In the fourth or fifth year of the program, students complete, defend and submit the dissertation. This is the last step to earn the Ph.D. degree.

STATISTICS REQUIREMENTS

• MATH 541B Introduction to Mathematical Statistics
• ISE 630 Foundations of Optimization
• MATH 507A Theory of Probability or MATH 505a Applied Probability
• MATH 505b Applied Probability or Math 506 Stochastic Processes
• DSO 607 High-Dimensional Statistics and Big Data Problems
• GSBA 604 Regression and Generalized Linear Models for Business
• MATH 541A Introduction to Mathematical Statistics 1 elective

Research Development Beginning in the first semester, students will apprentice with a faculty mentor to learn research fundamentals. Typically students are assigned to a new mentor each semester in the first year. In the summer following the first year in the program, students research and complete a Summer Research Paper.

• MATH 541B Introduction to Mathematical Statistics or MATH 547 Methods of Statistical Inference (if already taken 541B)
• 3 electives (including a graduate level class in a selected business area)
• DSO 599 Special Topics Class

Research Development At this stage in the program, the relationships Ph.D. candidates have developed through the research apprentices often lead to collaborative research projects. Ph.D. candidates continue to apprentice faculty with increasing responsibility. They also begin work on their dissertations.

Qualifying Exam Students are required to complete the qualifying exam by November 1 of their third year. The examination qualifying a student for candidacy for the Ph.D. degree is partly written and partly oral, designed, at least in part, to test the student’s fitness to undertake independent research. After successful completion of the qualifying exams, the student is admitted to candidacy.

The department maintains a highly supportive and collegial atmosphere that fosters close cooperation and coordination among the faculty to ensure the integration of the several specific disciplines represented by individual members of the group. This collaborative imperative guarantees the continued relevance and timeliness of the teaching and research programs.

Student Background: Our students come from all of over the world. They have strong academic backgrounds and bring with them a variety of experiences prior to joining the program.

Rashmi Ranjan Bhuyan

• PhD Candidate in Data Sciences and Operations

Rashmi is a fourth year Ph.D. student in the statistics group at USC Marshall School of Business. He is broadly interested in machine learning and it's application in social science and business.

Sebnem Manolya Demir

• PhD Student in Data Sciences and Operations

Katerina Giannoutsou

Katerina Giannoutsou is a PhD student in the Data Sciences and Operations department in the Marshall School of Business. She previously obtained her bachelor’s degree in Mathematics and Economics at New York University and worked as a Research Assistant at the Operations Research Center of MIT where she focused on healthcare operations. Her current research focuses in mechanism design and the dynamics of two-sided platforms.

Sang-Wook Lee

Second year Ph.D. student in Data Sciences and Operations at USC Marshall School of Business

APPLYING TO THE PhD PROGRAM

Dates + deadlines.

December 15, 2023: Application Deadline - Accounting, Data Sciences & Operations, and Management & Organization* 

January 15, 2024: Application Deadline - Finance & Business Economics and Marketing 

The link to the PhD Program application is available on the Admissions page and the next opportunity to apply is for Fall 2024 admission. Late applications may or may not be considered at the discretion of the admissions committee. 

Admissions decisions are made from mid-February to mid-April. You will be notified by email when a decision has been made.

ADMISSIONS CONTACT

Ph.D. Program USC Marshall School of Business 3670 Trousdale Parkway, BRI 306 Los Angeles, California 90089-0809 EMAIL

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Graduate Education

Berkeley offers a variety of opportunities for graduate students, including master's programs and PhD programs with interdisciplinary designated emphases in data science.

Computational Biology

Ph.D. in Computational Biology

The main objective of the Computational Biology Ph.D. is to train the next generation of scientists who are passionate about exploring the interface of computation and biology and committed to functioning at a high level in both computational and biological fields.

Designated Emphasis in Computational and Genomic Biology

The Designated Emphasis is a specialization offered adjunct to affiliated doctoral degrees for students with research interests in computational biology and genomics. DE students receive a solid foundation in the different facets of computational/genomic research and the ensuing competitive edge for the most desirable jobs in academia and industry, which increasingly require interdisciplinary training.

Computational Precision Health

Ph.D. in Computational Precision Health

Students in the Ph.D. in Computational Precision Health develop skills and expertise in both the computational sciences (machine learning and AI, natural language processing, statistical inference and modeling, data standards, parallel computing and data at scale, etc.) and health sciences (clinical decision sciences and cognitive informatics, clinical delivery, clinical research, implementation science, health information policy, etc.).

Designated Emphasis in Computational Precision Health

Current UCSF and UC Berkeley PhD students from affiliated programs can incorporate CPH courses and advising into their Ph.D. Designated Emphasis students will receive a solid grounding in the fundamentals of computational precision health, with training in the application of computation to the practice of medicine and public health. 

Electrical Engineering and Computer Sciences

Master of Engineering (M.Eng.)

The Master of Engineering program offers innovative graduate courses on scientific and technical topics, organized by technical concentrations that match student interest.

Master of Science (M.S.) and PhD in Electrical Engineering or Computer Science  

The Master of Science (M.S.) and Doctor of Philosophy (Ph.D.) programs emphasize research preparation and experience. 

Joint Bachelors/Masters (5th Year M.S.)

This program is available only to Berkeley EECS and CS L&S Undergraduates. It is a five year combined Bachelor/Master's program geared toward outstanding and highly motivated students who desire a program of study that offers greater breadth than is practical in the B.S. or B.A. programs alone.

Master of Arts (M.A.) in Statistics

Professional master's program candidates are engaged in a full-time program for one year (with a possibility of a third semester depending on circumstances). The program is designed to prepare students for careers in industries that require statistical skills.

Ph.D. in Statistics

The Statistics Ph.D. program welcomes students from a broad range of theoretical, applied, and interdisciplinary backgrounds, and provides rigorous preparation for a future career in statistics, probability, or data science. Students in the Ph.D. program take core courses on the theory and application of probability and statistics during their first year.

Designated Emphasis in Computational and Data Science and Engineering

The College of Computing, Data Science, and Society (CDSS) sponsors a Designated Emphasis in Computational and Data Science and Engineering, a program committed to the development of new curricula and expanded programs aimed at the development and use of numerical and computational tools to further research across multiple disciplines.

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The School of Information is UC Berkeley’s newest professional school. Located in the center of campus, the I School is a graduate research and education community committed to expanding access to information and to improving its usability, reliability, and credibility while preserving security and privacy.

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The School of Information offers four degrees:

The Master of Information Management and Systems (MIMS) program educates information professionals to provide leadership for an information-driven world.

The Master of Information and Data Science (MIDS) is an online degree preparing data science professionals to solve real-world problems. The 5th Year MIDS program is a streamlined path to a MIDS degree for Cal undergraduates.

The Master of Information and Cybersecurity (MICS) is an online degree preparing cybersecurity leaders for complex cybersecurity challenges.

Our Ph.D. in Information Science is a research program for next-generation scholars of the information age.

• Spring 2024 Course Schedule
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The School of Information's courses bridge the disciplines of information and computer science, design, social sciences, management, law, and policy. We welcome interest in our graduate-level Information classes from current UC Berkeley graduate and undergraduate students and community members.  More information about signing up for classes.

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Research by faculty members and doctoral students keeps the I School on the vanguard of contemporary information needs and solutions.

The I School is also home to several active centers and labs, including the Center for Long-Term Cybersecurity (CLTC) , the Center for Technology, Society & Policy , and the BioSENSE Lab .

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I School graduate students and alumni have expertise in data science, user experience design & research, product management, engineering, information policy, cybersecurity, and more — learn more about hiring I School students and alumni .

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Ph.D. in Information Science

Ph.d. community.

Ph.D. students are knowledge architects and respected contributors to our information society, with a vision of expanding access to quality information, an appreciation for diverse perspectives, and the spirit of collaboration.

You Belong at Berkeley

The I School is a welcoming community of students, faculty, and staff from a wide variety of backgrounds, nations, cultures, and experiences.

The doctoral program is a research-oriented program in which the student chooses specific fields of specialization, prepares sufficiently in the literature and the research of those fields to pass a qualifying examination, and completes original research culminating in the written dissertation. The degree of Doctor of Philosophy is conferred in recognition of a candidate's grasp of a broad field of learning and distinguished accomplishment in that field through contribution of an original piece of research revealing high critical ability and powers of imagination and synthesis.

The Ideal Place for Breakthrough Thinking

School of Information offers an ideal environment for information scholars , on the campus of a preeminent, forward-thinking research institution .

Dedicated to cross-disciplinary research, breakthrough thinking, and creative collaboration, the I School actively shapes the information frontier and has a track record of scholarly ideas, solutions, and policy counsel that make information more accessible, manageable, and useful.

Rigorous academics instill the theoretical and research capabilities required to advance diverse interests — from information design, architecture, and assurance, to human-computer interaction and the social, economic, and public policy implications of information. Ph.D. students work closely with faculty recognized as information pioneers.

Interdisciplinary thinking and partnership are central to the I School approach, so doctoral research often engages exceptional UC Berkeley schools and departments beyond the I School, from journalism, business, and law to computing, engineering, humanities, and social sciences.

On average, I School students complete the Ph.D. degree in 6 years.

• Semester 1–4 : Breadth, major, & minor coursework
• Semester 4–5 : Prelim research paper & exam
• Semester 6–8 : Qualifying exam
• Semester 10–12 : Complete & present dissertation

Detailed degree requirements & timeline

Areas of Study

Major and minor areas include:

• Human-Computer Interaction
• Information Economics and Policy
• Information Law and Policy
• Information Organization and Retrieval
• Information Systems Design
• Social Aspects of Information
• Information and Communication Technologies and Development

Your Career

I School Ph.D. graduates go on to careers in academia, industry, or the public sector.

Recent Ph.D. graduates hold tenure-track faculty positions at the world’s leading universities, as well as leading research positions in industry, academia, and public-interest organizations.

More about Ph.D. career outcomes

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“I think we can do a better job of using the Internet to tap into how people are doing, how they’re feeling, and what matters to them — online democracy, in a way, but minus the hype.”

—Galen Panger Ph.D. 2017

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Berkeley Berkeley Academic Guide: Academic Guide 2023-24

Applied data science.

About the Program

The Graduate Certificate in Applied Data Science, offered by the UC Berkeley School of Information, introduces the tools, methods, and conceptual approaches used to support modern data analysis and decision-making in professional and applied research settings. It exposes students to the challenges of working with data (e.g., asking a good question, inference and causality, decision-making) as well as to the new tools and techniques for data analytics (machine learning, data mining, and more).

The certificate is particularly designed to meet the needs of the graduate students in Berkeley’s professional schools — both professional master’s students and doctoral students — as well as graduate students in the social sciences and the arts & humanities.

The need for expertise in data analytics continues to grow in all organizations and disciplines. Graduate students in every field are now working with data from new sources: websites, electronic medical records, transaction records, sensor networks, smart phones, and digitized records and documents. The analytical tools and methods traditionally used to derive insights from structured and well-curated data sets (census, surveys, and administrative data) are not sufficient for this new, unstructured and often user-generated data.

The Graduate Certificate in Applied Data Science provides hands-on practice working with unstructured and user-generated data to identify new ways to inform decision-making. The curriculum educates professionals and scholars to be intelligent consumers of data science techniques in a variety of domains, with a foundation of skills for applying these techniques in their own domains.

Visit Program Website

Any UC Berkeley graduate student in good standing may apply. To apply, students should submit the following materials on the School of Information website :

• a letter of intent,
• a proposed study plan,
• a description of their Python programming and statistics competencies,
• their curriculum vitae or resume,
• their Berkeley course transcript.

Applications are accepted twice a year, in the middle of the fall and spring semesters.

Students may apply at any time during their UC Berkeley graduate career, either before or after taking courses that would count toward the certificate.

Certificate Requirements

Prerequisites.

Applicants must:

• Be registered and enrolled in a graduate degree program at UC Berkeley
• Be in good academic standing
• Meet course and subject matter prerequisites for courses taken in the certificate program, typically including Python programming and basic statistics knowledge.

The certificate requires three 3-unit courses, taken from the following approved lists:

An introductory data science class

A course in analytical methods and techniques of data science

An additional elective: either a domain-specific data science course or a second methods course.

Courses should be taken for a letter grade and must be completed with a grade of B or higher.  At least one of these courses must be an INFO course offered by the School of Information.

1. INtroductory data science course

 2. analytical methods and techniques of data science.

Students must take at least one course from this list:

3. Electives

Students must take one domain-specific data science course from the following list or a second methods course from the list in Section 2 above:

Contact Information

School of information, applied data science certificate.

[email protected]

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When you print this page, you are actually printing everything within the tabs on the page you are on: this may include all the Related Courses and Faculty, in addition to the Requirements or Overview. If you just want to print information on specific tabs, you're better off downloading a PDF of the page, opening it, and then selecting the pages you really want to print.

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Applying to Our M.S. or Ph.D. Program

Thank you for your interest in our graduate programs in Statistics. To apply for admission to our Master of Applied Statistics program please visit this link . To apply for admission to our M.S. or Ph.D. program please visit this link . To apply for admission to our Articulated Masters program please also visit this link .

Since applicants can ONLY apply to one program at UCLA each quarter, if you are applying to UCLA’s Biostatistics Program in the Fall, you cannot apply for UCLA’s Statistics Program in that same quarter.

Since you can only apply to one of our degree programs at a time, you cannot submit an application to both our M.S. and Ph.D. Programs for the same quarter.

Requirements & Qualifications for Consideration

• Ph.D. and M.S. students are admitted only in the Fall. Deadlines to apply for entry into the next Fall term are December 10, for all Ph.D. applicants and February 1, for all M.S. applicants.
• GRE – A GRE score is highly recommended when applying to the Ph.D. and M.S. programs of the Department of Statistics and Data Science.
• Three (3) letters of recommendation. Letters should emphasize the applicant’s level of preparation for graduate study in Statistics.
• Submission of the Online Graduate Application.

Statement of Purpose & Personal Statement A bachelor’s degree in statistics is not required for the M.S. or Ph.D. programs, but applicants should have taken at least 12 quarter courses (or eight semester courses) in substantial upper division quantitative work, including, but not limited to, courses in statistics, mathematics, computer science, and electrical engineering.

M.S. applicants must have a cumulative grade-point average of at least 3.2 in their upper division courses.

Ph.D. applicants must have a grade point average of at least 3.5 to apply to the doctoral program. Applicants who have already obtained a master’s degree must have maintained an average of better than 3.5 in graduate study.

Foreign applicants must upload their original transcripts and English translations (if applicable), TOEFL or IELTS scores & Confidential Financial Statement to the online application. If there is no identifying section, please upload these documents to the Supplementary Materials section. Please do not mail/send any documents to the office until further notice.

The Application Process Applications must be completed online. International Admission Requirements All M.S. and M.A.S. International applicants must upload a Confidential Financial Statement with their application. The document is found at this link . Please complete and upload it to the Supplemental or Supporting Documents section of the application.

Non-US Citizens whose first language is not English are required to take either the TOEFL or IELTS exam. The exam should be taken in November, but no later than January. If you have received a degree from a University in the United States, you need not take the TOEFL.

Non-US Citizens (including Permanent Residents) who would like to serve as a Teaching Assistant are recommended to take the Test of Oral Proficiency. Before our Department can offer a Teaching Assistantship to any student, they must have passed this exam.

Financial Support Almost all of our doctorate statistics graduate students receive financial support in the form of Teaching Assistants, Research Assistantships, and University and Extramural Fellowships. The support is usually for 4 years and may include registration fees and non-resident tuition. United States citizens and permanent residents who receive non-resident tuition for their first year are expected to establish California residency by their second year.

All applicants are strongly encouraged to apply for fellowships from outside sources (NSF, Javits, etc.) and UCLA special fellowship (listed with the application) that they qualify for.

Due to our limited funds, Master’s students are not guaranteed funding or assistantships. There is a chance that they can get some, but it is not guaranteed.

Application Checklist

Please upload the following items to the UCLA Graduate Division application by December 10th for PhD applicants and by February 1st for MS applicants.

• Online Graduate Application
• Letter of Recommendation (3 Required)
• Statement of Purpose/span>
• Official GRE scores (Highly Recommended) – Use UCLA’s Code: 4837 and Statistics Department code: 0705
• Official TOEFL or ILETS scores (International Applicant Only) (UCLA’s Code: 4837, Department code: 59)
• Confidential Financial Statement* (M.S. International Applicants Only)

In the FIRST paragraph of your statement of purpose, please indicate your specific interests within the realm of statistics and which ladder faculty member would most likely be your research advisor if you are admitted to UCLA Statistics.

43 Best colleges for Data Science in California, US

Updated: February 29, 2024

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Below is a list of best universities in California ranked based on their research performance in Data Science. A graph of 1.69M citations received by 32.4K academic papers made by 43 universities in California 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. Stanford University

For Data Science

2. University of California - Berkeley

3. University of California-San Diego

4. University of California - Los Angeles

5. University of Southern California

6. University of California - San Francisco

7. University of California - Santa Barbara

8. University of California - Irvine

9. University of California - Davis

10. California Institute of Technology

11. University of California - Riverside

12. University of California - Santa Cruz

13. San Jose State University

14. San Diego State University

15. Naval Postgraduate School

16. University of California - Merced

17. Claremont Graduate University

18. Palo Alto University

19. Santa Clara University

20. California Polytechnic State University - San Luis Obispo

21. University of San Francisco

22. California State University - Fullerton

23. San Francisco State University

24. Humboldt State University

25. California State University - Sacramento

26. Sanford Burnham Prebys Medical Discovery Institute

27. california state polytechnic university - pomona.

28. California State University - Fresno

29. Chapman University

30. California State University - Long Beach

31. Loma Linda University

32. California State University - Northridge

33. California State University - Los Angeles

34. Pomona College

35. Loyola Marymount University

36. California State University - East Bay

37. California State University - San Bernardino

38. University of San Diego

39. California State University - Bakersfield

40. California State University - Chico

41. University of the Pacific

42. Harvey Mudd College

43. California State University - Monterey Bay

Closest to California states to learn Data Science

Computer science subfields in california.

DiscoverDataScience.org

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

Created by aasif.faizal

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

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

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

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

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

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

So why pursue a data science PhD?

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

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

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

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

Topics include:

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

Preparing for a Doctorate Program

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

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

Data Science PhD Programs, Historically

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

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

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

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

Can You Get a PhD in Data Science Online?

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

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

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

Overview of PhD Coursework

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

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

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

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

What’s the core curriculum like?

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

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

Dissertation

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

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

Join professional associations

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

Leverage your social network

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

Kaggle competitions

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

Internships

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

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

Get certified

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

Conferences

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

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

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

What most schools will require when applying:

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

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

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

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

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

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

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

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

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

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

Complete List of Data Science PhD Programs

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

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

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

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

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

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

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

View Course Offerings

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

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

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

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

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

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

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

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

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

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

Coding well in C++ is preferred.

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

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

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

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

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

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

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

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

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

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

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

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

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

View Course Offerings – Clemson

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Applicants must have a master’s already.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Students in this program work in research groups across campus.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Worcester Polytechnic Institute – Worcester, Massachusetts PhD in Data Science

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

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

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

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

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

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

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

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

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

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Data Science

Data Science @ UC Riverside

Data science is the study of the collection, management, and analysis of data to extract knowledge. It is a multidisciplinary field with core components from Computer Science and Statistics and applications in a variety of empirical disciplines. The Data Science Center at UCR was established to inspire, enable and support cross-disciplinary research and education in Data Science. Located in the Multidisciplinary Research Building (MRB)  the Center includes faculty from various departments and colleges across UCR.

UCR offers a  Data Science Undergraduate Major in the Department of Computer Science and Engineering and in the Department of Statistics . The curriculum draws from both the Computer Science and Statistics disciplines and includes data-focused breadth courses in other disciplines including Biology, Business, Economics, Electrical Engineering, and Earth Sciences.

Students from other disciplines can get a  Minor in Data Science .

UCR also offers a Master of Science in Computational Data Science . This in-person MS program is a STEM degree offered through a collaboration between the  Department of Computer Science and Engineering  and the Department of Electrical and Computer Engineering . Its focus is on the computational foundations of Data Science, including Data Mining, Artificial Intelligence, Deep Learning, Natural Language Processing, Big Data, Database Management etc.

Other related programs are the online Master of Science in Engineering with focus in Data Science , offered by the Bourns College of Engineering  and the Master of Science in Business Analytics  jointly offered by the Department of Statistics  and the School of Business .

Nowadays, research groups in areas like biology, engineering, physics, material science, astronomy, business, economics, sociology, and political science (to name a few), generate gigabytes to exabytes of data. Data science researchers at UCR work to make sense of these data banks in novel ways by applying domain knowledge and finding statistically and computationally efficient solutions.

Data Science Seminar Highlights

The Data Science Center holds weekly public seminars in the Multidisciplinary Research Building (MRB) Seminar Room on Fridays from 12:10-1:00PM. If you are interested in getting the seminar announcements, please email [email protected] (use subject: Data Science Seminars).

Dealing with Latent Pre-exposure to Information Treatments

Machine learning guided modeling of ligand-protein binding energy landscape: applications in small molecule and protein-based drug design., some thoughts on data science - population health collaborations..

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USC Data Science Program

Be in demand. be a tech innovator. be the future..

Empower yourself with the skills to navigate the data-driven world

Shape the future and prepare yourself for success with data-driven decisions, at USC Data Science

Combine computing, statistics, mathematics, psychology, and engineering.

"The need for data science skills continues to increase in industry, government, and academia. Our USC data science degrees prepare students for the unprecedented challenges of data-rich inquiry. Our students pursue very successful careers in research, industry, government, and non-profits in a wide range of sectors and areas where data is abundant already or could be made so. We hope you will consider joining us!" -Yolanda Gil, Director, Data Science Program Department of Computer Science

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Published on February 23rd, 2017

Last updated on April 19th, 2024

DATA SCIENCE

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What we offer related to Data Science!

CSUEB Statistics students face lower competition for course availability.

We offer courses directly related to Data Science at the undergraduate and graduate levels. Topics include:   R   for Data Science, Data Visualization, Statistical Learning, Predictive Modeling, Training and Test Data, Statistical Natural Language Processing, Applied Deep Leaning, Time Series, Bayesian Statistics

Most important requirement for the   BS Statistics Concentration in Data Science or Minor in Data Science   is   interest   – the program is applied and hands on.  Study Data Science in the CSU East Bay College of Science.  Smaller undergraduate classes ~35 students per class.  We describe our major as: “Touch data” “Compute-more” “Calcu-less”

Most important application requirement for the   MS Statistics Option in Data Science   is   interest   – please apply for Fall Semester, but if you miss the deadline you can start Fall through OpenU and apply for Spring Semester.  Study Data Science in the CSU East Bay College of Science.  Our MS program is designed for continuing students who have completed any undergraduate degree and some prerequisite courses and for working adults.  Complete the degree in two years, 2 afternoons or 2 evenings per week.  Reasonable   price   – no $80,000 price tag, no CSU Business College $270 per UNIT fee, save approximately $8,640.  Program can be completed in 3 semesters by taking class four days a week, see the Graduate Advisor for details upon admission.  Afternoon and Evening classes.  Smaller graduate classes ~25 students per class.  We describe our program as:  “Touch data” "Work with small data to medium data to big data"  "100s of rows to 10's of millions to a billion+ rows of data"

Diversity of technical background valued, you can start from scratch or have a PhD or MD, retired professionals welcome!

DATA SCIENCE WORKGROUP F2

On Friday's at 2pm the Data Science Workgroup meets. In the Fall Semester the STAT 694 can be taken for credit. Each semester students work on a research project applying Data Science Methods to data of interest.

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Data Science and Analytics Programs in California

California is home to some of the best data science and analytics programs in the nation. The Golden State has a thriving tech culture, rife with opportunities for students at every level. The latest Bureau of Labor Statistics data shows that 18.47 percent of all US data scientists work in California.

And with an average pay of $103,500 for most data science jobs, the demand for data science courses in California shouldn’t be surprising. With hundreds of certifications, boot camps, on-campus and off-campus master’s degrees, and PhD programs, data science offers students a unique way to combine their software, statistics, and math interests to create innovative solutions. 

If you plan to get into data science or analytics in California, you’ll need to know what courses will help you get there.

Read on about California’s top data science and analytics programs.

Data Master’s Programs in California

California offers online and on-campus master’s degree programs in business analytics, data analytics, and data science. If you are looking to change career paths or want to advance your undergraduate degree, consider enrolling in one of the following master’s degree programs offered by universities and colleges in California: 

Masters of Business Analytics

Master of Business Analytics focuses on applying modern data science, machine learning, and optimization to solve real-world business problems. 

Graduates often find well-paying jobs solving business challenges in technology companies and startups in California, where analytics plays a huge role in business success.

Some of the master of business analytics programs available in California include:

Online Master of Science in Business Analytics- Touro University

• Class type: 100 percent online
• Course duration: one year
• You can earn the degree in 12 months by taking two courses per term for six consecutive terms.

Master’s in Business Analytics – Hult International Business School 

• Class type: Campus
• Course duration: 11 months

Master of Science in Business Analytics – UC Irvine – Merage School of Business

• Class Type: On-campus
• Course duration: 11-13 months

Master of Science in Business Analytics (MSBA) – Golden Gate University

• Course type: Online and on-campus 
• Course duration: 4 – 5 semesters 

Master’s of Data Analytics 

Master of data analytics empowers graduates with the skills of using big data for business decision-making and planning. This course builds students’ proficiency in data mining tools and data analysis methodologies.

Graduates can take roles such as big data architect, computer and information system manager, and logistician.

Some of the masters of data analytics programs available in California include:

Master of Analytics – University of California, Berkeley

• Course type: In-person 
• Course duration: three semesters 
• This course also includes a 10-week internship 

MS in Data Analytics – Alliant International University

• Course type: Campus-based program
• Course duration: 14 months 

MS in Healthcare Analytics – Alliant International University

• Course type: Full-time campus-based
• Course duration: one year 

Master of Science in Big Data Analytics – San Diego State University

• Course type: Full-time on-campus
• Course duration: two years

Master’s of Data Science 

Masters in data science earns you a broad skill set that you can apply to a vast array of tech-related careers such as computer programming, data architecture, and data engineering. 

You learn essential concepts such as applied statistics, database systems and preparations, and practical machine learning. You also learn programming languages such as R, Python, and SQL.

Some of the masters in data science programs available in California include:

MS in Data Science -San Francisco University

Master of Science in Engineering in Data Science – University of California, Riverside

• Course type: Online course 
• Course duration: one year, one month. 

Applied Data Science – University of Southern California

• Course type: On-campus and online

Data Science Master – the University of California, Irvine (UCI)

• Course type: On-campus
• Course duration: 15 months

Online Data Science Programs in California

If you wanted to study at a university just a few years ago, you could only do so by attending physical classes. Evening classes were the only way that professionals could juggle work and study. But not anymore. 

Today, if you want to get a master’s degree in data science, you can do so online. 

Like on-campus programs, online data science programs prepare students for work as data analysts and data scientists. Students will learn to use programming, statistics, modeling, and machine learning to solve real-world problems.

If you are ready to learn data science from the comfort of your computer at home, below are online masters of data science programs to consider: 

Master of Data Science, UC San Diego

• Course level: Beginner
• Course duration: 2 years
• Price: Approx. $15,820/year

The MDS program at UC San Diego is a joint degree between the School of Engineering and Computer Science and the Halıcıoğlu Data Science Institute (HDSI). The program draws from the expertise of these two departments to equip students with data-handling and analysis skills through a rich course curriculum that includes: 

• Three foundation courses: Python, probability and statistics, and machine learning 
• Three core courses: Data management, big data analytics with Spark, and data mining 
• Capstone project

Students can also choose between courses such as natural language processing (NLP), and unsupervised learning, among others. 

The program culminates in a research project during the third year, giving students experience in data science research. This program is highly ranked and has many employees from large companies as alumni. 

The coursework is rigorous, but the program is designed to be flexible so that students can fit their schedules around work and other responsibilities. 

Master of Science in Data Science, National University

• Price: $17,238

This program is designed for adult learners and covers 14 months. However, the National University offers a flexible timeline, spread over 4-week courses. You can thus start sooner or finish faster or have the flexibility to juggle between family, work, and studies.

This program offers career-focused specializations in the following fields:

• Artificial intelligence and optimization
• Business analytics
• Database analytics
• Health analytics

The prerequisite to enrolling in NU’s master of science in data science program is previous industry or study experience in statistics, math, engineering, computer programming, or science. You also need a minimum GPA of 2.50.

Master of Science in Data Science, Northcentral University

• Course duration: 30 credit hours | 23 months normal duration
• Price: $30,420

The Master of Science in Data Science at Northcentral University is a very flexible online program covering a comprehensive curriculum. It supports adult and non-traditional learners, with accessibility and educational flexibility being the private university’s biggest aims.

To enroll in the MS in Data Science program, you must hold an undergraduate degree. However, no test scores, prerequisite courses, or minimum GPA are required.

Students can start the program every week on Monday. The required courses to complete the program include:

• Principles of data science
• Quantitative research design
• Databases and business intelligence
• Big data integration
• Data mining
• Quantitative methods for data analytics and business intelligence
• Inferential statistics and predictive analysis
• Critical analysis, interpreting, and reporting data
• Data visualization
• Strategic management of data, information, and knowledge for data science

The admissions into this program are very flexible, and the course favors students with less academic preparation.

Online Master’s in Data Science, UC Berkeley

• Price: Approx. $2,712 per unit (fee is not inclusive of campus fees)

The Master of Data Science (MDS) at the University of California, San Diego is a top-ranked online master’s degree program in data science. With an emphasis on hands-on training, students learn to apply best practices and techniques from data science in real-world settings. 

Students can expect to learn how to set up data pipelines, work with big data, and think like data scientists. The curriculum also includes courses in statistics and probability theory. 

There are two tracks within the MDS: 

• Machine learning 
• Data mining/Business intelligence 

The courses within these tracks vary widely. For example, ML tracks include Bayesian models, machine learning algorithms, and statistical modeling courses. On the other hand, BI tracks focus on data mining tools such as R and SQL. 

Some electives allow students to choose topics such as Python for data science, designing data scraping pipelines with Scrapy, and visualizing data with D3.

This course equips students in the following areas: 

• Data cleansing
• Research design
• Data engineering
• Data mining and exploring
• Machine learning
• Information ethics and privacy
• Statistical analysis

Master of Science in Applied Data Science, University of Southern California  

Students with bachelor’s degrees in computer science, engineering, social science, and other scientific disciplines can get the training to pursue a data science career via this program.

Upon course completion, students will have learned: 

• Data handling techniques
• Data science programming with Python 
• Data mining 
• Working with big data 
• How to handle data within databases 

The program offers a completely immersive experience in data science and prepares students to embrace their future roles as data scientists or analysts. 

Example Data Science Courses in California

Data science and analytics bootcamps in california.

Are you interested in learning about data science but don’t have the time or budget for an advanced degree? A bootcamp will provide the technical training you need to land your first data science job.

Tech bootcamps are short-term courses designed to teach technical skills and knowledge in a specific domain. Data science boot camps offer career training and can help you get a job as a data scientist or data engineer after graduation. Bootcamps are typically three months long, shorter than most other programs.

While boot camps are not for everyone, they can be a good fit for some people. If you’re looking for hands-on experience and some programming experience, a boot camp might be right for you.

We have compiled a list of popular data science boot camps below:

Data Science PhD Programs in California

Many people understand what data science is, but they aren’t always sure where or how it’s used. 

To better understand the field, you should look at where data PhDs work and what research they conduct. Data science PhDs are researchers who use data to address real-world problems.

There are two main reasons why data PhDs are essential: 

• The amount of data being generated is increasing dramatically. Hence there is a growing demand for skilled data scientists to help analyze and make sense of the data. 
• Data is becoming increasingly important in all research fields, including health care, business, social sciences, and law enforcement. 

That said, there are many different types of PhDs in data science: 

• Some focus on specific areas such as machine learning and deep learning. 
• Others are focused on more general topics like data mining or data visualization. 
• There are also interdisciplinary areas where PhDs cut across fields like engineering, mathematics, and statistics.

Most PhDs work in academia, government research facilities, or industry. Data PhDs tend to be found in disciplines that use data, such as science and engineering. Other PhDs work full-time or part-time with other researchers or by themselves, or as consultants.

Regardless of their position, they will likely spend most of their time working with computers and software. Some also conduct field research as part of their work duties. The pace of their work usually depends on the task at hand and how quickly they need results.

Examples of data science Ph.D. programs in California include USC Data Science and Operations and PhD in Computational and Data Sciences from Chapman University .

Five In-Demand Analytics Jobs in California

Suppose you have a solid quantitative background and a passion for data. In that case, you have the potential to be an analytics expert at a startup, medium-sized business, or even a large corporation.

Currently, there are many available positions for analytics professionals in California. This could be the right place to look for a challenging and rewarding career. Here are some of the most in-demand jobs in California:

1.       Data Analyst

• Education level: Bachelor’s degree or master’s. 
• Average salary/year: $90,307 (about 5 percent above national average)
• Work environment: From freelance, remote to full-time in the office
• Data analysts are responsible for analyzing data to produce actionable insights that help businesses make informed decisions about their operations. They perform complex mathematical and statistical calculations to extract meaning from large numerical data sets. They also develop models to predict future outcomes based on previous events and trends.

2.       Analytics Consultant

Education level: Bachelor’s degree or master’s in a related field. 

• Average salary/ year: $107,892
• Work environment: remote or part-time/full-time in the office
• An analytics consultant plays a vital role in helping businesses understand their customers’ needs, analyze their data, and use it to improve their products and services. They work with clients to design custom solutions to solve individual problems or optimize existing ones. They also advise organizations on building better customer relationships through data collection, analysis, and information sharing.

3.       Data Visualization Expert 

• Education level: Bachelor’s degree or master’s in data science, statistics, or related field
• Average salary/ year: $111,345
• Becoming a data visualization expert will require artistic talent and a solid understanding of data analysis. The goal is to create graphics and charts to help your employer make better decisions. This could be anything from infographics used on your company website or presentations for investors or clients.
• The best way to become a data visualization expert is by taking classes at your local college or university. The University of California – Berkeley (UCB) offers courses for free online that teach you how to use various data visualization tools like Tableau.

4.       Data Visualization Assistant:

• Education level: Bachelor’s degree. Masters preferred
• Average salary/ year: Hourly pay ($25.96to $77.64)
• If you don’t have prior work experience, you should consider becoming a data visualization assistant. This position requires little experience, but it does require strong analytical skills and an eye for design. 
• As a data visualization assistant, your primary responsibilities will involve creating high-quality visualizations using software such as Excel or Photoshop. If you can do this well, you might get promoted to a full-time data analyst position at the same company.

5.       Data Visualization Designer:

• Average salary/ year: $87,000
• Work environment: Freelance, remote, or in-office
• If you already have experience creating visualizations or a degree in computer science, you might consider becoming a designer. This position requires strong analytical skills and strong communication skills, especially when presenting your findings to clients or investors. As a data visualization designer, your primary responsibilities will involve creating high-quality visualizations using various software such as Tableau. You should also communicate well with other team members about what kind of data needs to be collected and analyzed so that the company can make the best decisions possible.

Frequently Asked Questions

California is renowned for its thriving tech culture and hosts some of the top data science and analytics programs in the U.S. This makes it an ideal place for students interested in these fields.

California offers a range of programs including online and on-campus master’s degrees, PhD programs, and bootcamps in business analytics, data analytics, and data science.

Data science bootcamps in California are short-term, intensive courses designed to provide technical training for those looking to enter the data science field quickly. They are ideal for individuals without the time or resources for an advanced degree​

Data science PhD graduates often engage in research to solve real-world problems and find opportunities in various sectors where data analysis is crucial.

There are many available positions for analytics professionals in California, spanning startups, medium-sized businesses, and large corporations.

Related Resources

• What is Data Analytics?
• Data Science Certification
• Data Analytics Bootcamp: A Complete Guide
• Find the Best Tech Scholarships
• Data Science and Data Scientist Jobs

MASTER OF DATA SCIENCE

Empowering innovation.

Data science has emerged as a crucial force in the technological revolution, bridging the gap between a data-poor past and a future of intelligent systems. 

The Master of Data Science (MDS) program is designed to cultivate expertise in contemporary data science methodologies. By integrating the foundational principles of Statistics and Computer Science, our program equips and empowers students with the necessary skills to thrive in the age of artificial intelligence disruption. 

Candidates may select from our full-time (15-month) or part-time (24-month) pathway, both instructed by esteemed faculty members and researchers possessing extensive experience in data science. The curriculum emphasizes hands-on training in applied probability, mathematical statistics, statistical modeling, computing, machine learning, data management, visualization, and artificial intelligence.

STEM Designated

Eligible for 36 Months of Optional Practical Training

Best Master's in Data Science

Ranked by FORTUNE

85% Placement Rate

ICS Professional Master’s programs boast 85% placement rate within 3 Months of Graduation

The UCI MDS program distinguishes itself with a robust foundation in data science, incorporating a balanced curriculum that accentuates both computer science and statistics. Furthermore, our program encompasses two capstone project courses, providing students with invaluable experience in addressing real-world challenges through data science techniques.

As intelligent systems consume vast amounts of data, data science will be instrumental to the innovation of powerful and versatile AI systems. Graduates of the program will be equipped to adapt to the advancements of an AI-driven future.

We are taking the helm of generating a robust Data Science program to innovate and be leaders in the industry. Our balanced approach makes us a unique catalyst where students can learn and make an impact on society. Bin Nan, Chancellor's Professor and Faculty Director Master of Data Science Department of Statistics

The Donald Bren School of ICS Ecosystem

The Donald Bren School of Information and Computer Sciences at UC Irvine, is one of the leading computing schools in the nation and is strategically centered in the nucleus of Southern California’s TechCoast, which encompasses over 40% of Orange County’s high-tech and biomedical enterprises and one-third of Fortune 500 company offices.

By capitalizing on our internationally renowned faculty and the vigorous industry clusters in life sciences, information technology, aerospace, advanced manufacturing, and digital arts and media, the Master of Data Science program is meticulously crafted to stimulate innovation and propel the data science discipline forward.

Discover Academic and Research Opportunities

Reinforce local businesses and industries, access to resources and capital, nurture a culture of innovation, enhance your network and professional development.

Embark on your degree in a dynamic destination city that provides the resources, capital, and innovative culture requisite for elevating your academic and professional trajectory in data science.

The Master of Data Science program provides flexible learning paths, with both full-time and part-time options, tailored to meet your unique academic and professional demands. Regardless of the pathway chosen, all students enjoy equal access to our rich, comprehensive curriculum and the collective wisdom of our distinguished faculty.

To attain the coveted Master of Data Science (MDS) degree, students are required to successfully navigate through an academic journey comprising 52 units. This includes ten core courses, which form the backbone of data science principles; three elective courses, offering an opportunity to delve into areas of personal interest within the field; and finally, a capstone project that allows students to apply their knowledge to real-world problems, synthesizing and showcasing their learning.

Upon the culmination of this rigorous yet rewarding program, graduates will emerge equipped with a robust blend of technical expertise and professional acumen. This unique combination primes you for a successful career trajectory, enabling you to seamlessly transition into the dynamic world of data science and make your mark.

Take the Next Steps

Learn more about the journey ahead.

Data science seems like the Swiss Army knife of majors in its applicability across fields—I cannot think of an industry that doesn’t make use of data in some way, Adelynn Paik, MDS Student '22

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Department of Statistics - Donald Bren School of Information & Computer Sciences

Uci in top 10 on fortune’s list of best master’s in data science programs.

August 24, 2022

UCI launched its Master of Data Science ( MDS ) professional graduate program in the fall of 2021 to meet the growing demand for data scientists. This year, Fortune launched its first-ever ranking of the top in-person master’s degree programs in data science, noting that “data science is one of the fastest growing fields today, and one of the best ways to break into the field is to pursue a master’s degree in the field.”

Fortune ranked UCI’s MDS program , which is offered through the Donald Bren School of Information and Computer Sciences ( ICS ), seventh on its list of 28 schools. UCI is the only UC school, and one of only a few universities from the West Coast, to make it on the 2022 list, which ranked data science programs based on selectivity and demand .

“This achievement speaks volumes to the dedication and work we have committed for our program,” says MDS Program Director Bryan Muñoz . “The quality of our faculty and students that have made this possible in our first year is unmatched.” The inaugural cohort of students, including Adelynn Paik , is set to graduate later this year, and the 2022-23 cohort, including Michael Strand , is just getting started.

“When I was accepted into the 2023 MDS program, it was one of the proudest moments of my life,” says Strand. “I had weighed different data science programs across the country, but no other program offered the same perfect blend of faculty talents, prestige, cutting-edge academics, career-oriented program structure, and a prime location.”

Paik, who serves as an MDS program ambassador, similarly thought UCI would be the perfect school for furthering her interests in data science. “At UCI’s MDS program,” she has said , “I’m gaining useful skills while giving myself the chance to learn about the job landscape through offerings such as career counseling and the capstone project.”

In addition to offering real-world experience, the MDS program ensures students gain a strong foundation in both statistical and computing methods. It leverages pioneering faculty and researchers from the Department of Statistics and Department of Computer Science — conveniently housed together in the School of ICS — to provide hands-on training in applied probability and mathematical statistics, statistical modeling and computing, machine learning, data management and visualization, and artificial intelligence.

“We are incredibly proud of being ranked by Fortune . It is a great achievement and a testament to what we are trying to achieve with our program — to be the best in class and to educate students about the importance of data science,” says Bin Nan , Chancellor’s Professor in statistics and MDS faculty director. “We thank our faculty for the effort they put forth in educating our students and empowering innovation through data science.”

— Shani Murray

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ESDRI Seed Grants Enable Early-Stage Research on Indigenous Land Conservation, Carbon Stabilization, and Invasive Species

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Each year the  Environmental Science and Design Research Institute (ESDRI) hosts a competitive request for proposals which are reviewed by an interdisciplinary panel, awarding seed grants with funding up to $12,000 for multidisciplinary research related to ESDRI’s wide-ranging  areas of focus . These seed grants provide funds for preliminary or early-stage research, facilitating the building blocks to apply for extramural funding.

The application cycle for seed grants is typically early spring. To apply, at least one person from the research team must be an  ESDRI affiliated faculty member . If you are interested in becoming an ESDRI affiliated faculty member, please email  [email protected] .

This year the institute awarded three seed grants and they are thrilled to support this important and timely research!

"Two-eyed seeing in Earth observations: co-creating data tools and capacity for Earth observations data analysis in support of California’s land transfer policy"

Elaine (Lan Yin) Hsiao, PhD (Assistant Professor, School of Peace and Conflict Studies) and  He Yin, PhD (Assistant Professor, Department of Geography) were awarded an ESDRI seed grant to conduct a pilot study focused on supporting the transfer of land back to California Tribal Nations using two-eyed seeing that combines both remote sensing and Indigenous knowledge. This includes identifying lands that are at risk, from stressors such as degradation or wildfire, that would benefit from Indigenous conservation. “This work has real world implications because it is taking place in a state where funds are provided for Tribes to buy back land,” says Hsiao, “Within this project we can identify lands that might be optimal for land transfer while at the same time strengthening the argument that these lands should be given back.” 

This work centers around workshops with Tribal members in which they will first meet and begin the project design process, then the core research team will bring initial research back to Tribal members, and finally the whole team will work together to pull everything they have learned into a larger project proposal for external funding. The first workshop, taking place this summer, will include “listening sessions to understand the needs of the stakeholders,” tells He, “which are much needed to guide our remote sensing work.” He is excited to begin his first remote sensing project in the environmental justice sphere, adding “What is even more exciting is that I will co-design the research with the stakeholder, rather than just working alone.”

Joining Hsiao and He on this project are undergraduate students Rae Baba (Junior, Environmental Studies with Environment, Peace, and Justice minor) and Andrew Shenal (Sophomore, Environmental Studies with Geography and GIS minors). Both are participants in the  Summer Undergraduate Research Experience (SURE) program and are supported by ESDRI and the  Anti-Racism and Equity Institute (AREI), respectively.

The research team states that the seed grant makes this co-design process possible. By creating the workshops in California, it allows them to physically meet with partners and design together, from the basic plans of the project to how data is managed. Hsiao says, “This level of collaboration requires a lot of time together to work through questions and ideas, and it is really not possible to build this trust and side-by-side cooperation otherwise.”

Find out more about this Indigenous land conservation research project 

"Unlocking secrets below: Investigating the spatial heterogeneity of carbon stabilization in Arctic lake sediments through a visual lens"

Chelsea Smith, PhD (Postdoctoral Scholar, Department of Earth Sciences),  Allyson Tessin, PhD (Assistant Professor, Department of Earth Sciences), and  Shannon Hines  (Manager, Design Innovation Hub) will be deploying a camera system to study lake sediments in an Arctic lake in Alaska with their seed grant. Lakes hold an important role in carbon sequestration, but with climate change, that carbon isn’t necessarily going to remain stored in lake sediments. “Warming causes more carbon to enter lakes from the surrounding landscape as permafrost thaws, then in turn microbes can eat that carbon, releasing carbon dioxide,” tells Smith, “However, metals, such as iron and manganese, may stabilize the carbon, making it inaccessible to microbes allowing it to eventually become buried in the sediments over time.” The group's preliminary research shows that some parts of their research lake, Lake Toolik, are high in iron, while other parts are high in manganese. This interesting feature of the lake will allow them to look at the role of each of these metals separately and see if they are doing similar or different things.

Being awarded an ESDRI seed grant allowed an increase in interdisciplinarity for this research. Smith and Tessin brought on Hines as well as Nicholas Cindrich (BE ‘24, Mechatronics Engineering Technology), to help with the planning and design of the 3D printed camera and light attachments that will fasten to their sediment coring device. Sediment cores are a traditional way to sample sediment in lakes, but adding the camera and light allows for videos to be taken of the process to use in outreach and education material. Additionally, the team noticed some odd striations in one sampling site in Toolik Lake (right) and this new tool will allow them to examine that abnormality more closely. A prototype of this camera system will be tested out in the Central Basin of Lake Erie in June with plans to take it to Alaska in August.

Smith is very passionate about outreach and has plans to disseminate this information as well as generally teach students about this type of research in collaboration with the Toolik Field Station, the Alaska Native Science and Engineering Program, and as a Scientist in Residency at the Sitka Sound Science Center. Through these programs she’ll be with students from grade school through high school, participating in workshops, group projects, panel discussions, radio and podcast interviews, and classroom visits.

Find out more about this carbon stabilization research project

"Understanding the Early-Stage Invasion Dynamics of Box Tree Moths in Midwestern USA: Integrating Genomics and Landscape Approaches"

Sangeet Lamichhaney, PhD (Assistant Professor, Department of Biological Sciences),  Sarah Eichler, PhD  (Assistant Professor, Department of Biological Sciences), and  He Yin, PhD (Assistant Professor, Department of Geography) were awarded a seed grant to study the early-stage invasion of the Box Tree Moth (Cydalima perspectalis) in Ohio, Michigan, and New York. This work will be key to understanding the invasion dynamics of this moth, which decimates boxwood trees (Buxus sps.), but also has the potential to answer bigger questions in the field of invasion biology. “Most invasive systems we study are already established systems. We don’t normally get to study the biological processes associated with early stages of invasion, so we have a very interesting case with the Box Tree Moths which were first identified in the USA in 2021,” says Lamichhaney. “We will begin trapping Box Tree Moth adults and larvae in May and use genomics tools to characterize genetic diversity and population structure of local invasive populations, identify genetic markers associated with their successful introduction and explore where these populations originated from.”

During this time Eichler will lead the collection of “on the ground” data, and states “we will do a rapid assessment of the plant community near each trap as well as assess the relative development intensity of the area.” The team will also obtain plant tissue and soil samples to test the attractiveness of pests based on plant and soil nutrition. Alongside, He will be at the field sites to learn about the vegetation and landscape. “Such information is vital for guiding remote sensing work,” says He.

One of the key pieces to this work is the combination of genomic and landscape approaches. In addition to this “on the ground” data collection, Eichler and Yin will be analyzing landscape characteristics such as topography, land use patterns, habitat composition from satellite imagery to see if plant community may be a factor in where the moths become established. He tells us “While you cannot detect individual moths from satellite images, we can see the damage of the moth to plants, which can be used to trace moths.” These elements will be combined to answer the questions about the invasion dynamics of this pest. Lamichhaney, Eichler and He expect to accomplish a detailed assessment of surrounding landscape features in the invasion areas and identify the spatial relationships between genetic variations in local invasive populations and landscape features.

The team includes a handful of students with key skills from genomics to remote sensing and GIS: Aarati Basnet (PhD Student, Ecology and Evolutionary Biology), Carter Henry (Junior, Zoology), Aciano Leipply-Caban (Sophomore, Botany with Climate Change and GIS minors), and Gus Holman (incoming PhD Student, Ecology and Evolutionary Biology). 

The research team states that this new collaboration and the transdisciplinarity of the project was made possible, in part, by ESDRI. They are currently collaborating with the United States Department of Agriculture (USDA) and the Ohio Department of Agriculture (ODA), with plans to produce public awareness campaign materials based on the results of this study. Given the popularity of the Box Tree as an ornamental plant, it is crucial to involve the public in understanding the invasion in our region and methods for its control.    

Find out more about this invasive species research project

Kent State University is proud to be ranked as an  R1 Carnegie Classification .  Aside from the Environmental Science and Design Research Institute,  explore the other institutes and initiatives that are dedicated to cutting edge research and development .

To learn more about the Environmental Science and Design Research Institute’s Seed Grant Program as well view past awarded projects, please visit the Seed Grant Program page

• Environmental Science and Design Research Institute
• Department of Biological Sciences
• Department of Earth Sciences
• Department of Geography
• School of Peace and Conflict Studies
• Anti-Racism and Equity Institute

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