Columbia University
Room 1005 SSW, MC 4690
1255 Amsterdam Avenue
New York, NY 10027
Phone: 212.851.2132
Fax: 212.851.2164
Program description.
The Ph.D. program in statistics prepares students for a career pursuing research in either academia or industry. The program provides rigorous classroom training in the theory, methodology, and application of statistics, and provides the opportunity to work with faculty on advanced research topics over a wide range of theory and application areas. To enter, students need a bachelor’s degree in mathematics, statistics, or a closely related discipline. Students graduating with a PhD in Statistics are expected to:
Course Work A Ph.D. student in our department must complete sixteen courses for the Ph.D. At most, four of these courses may be transferred from another institution. If the Ph.D. student is admitted to the program at the post-Master’s level, then eight courses are usually required.
Qualifying Examinations First, all Ph.D. students in the statistics track must take the following two-semester sequences: MA779 and MA780 (Probability Theory I and II), MA781 (Estimation Theory) and MA782 (Hypothesis Testing), and MA750 and MA751 (Advanced Statistical Methods I and II). Then, to qualify a student to begin work on a PhD dissertation, they must pass two of the following three exams at the PhD level: probability, mathematical statistics, and applied statistics. The probability and mathematical statistics exams are offered every September and the applied statistics exam is offered every April.
Note: Students concentrating in probability may choose to do so either through the statistics track or through the mathematics track. If a student wishes to do so through the mathematics track, the course and exam requirements are different. Details are available here .
Dissertation The dissertation is the major requirement for a Ph.D. student. After the student has completed all course work, the Director of Graduate Studies, in consultation with the student, selects a three-member dissertation committee. One member of this committee is designated by the Director of Graduate Studies as the Major Advisor for the student. Once completed, the dissertation must be defended in an oral examination conducted by at least five members of the Department.
The Dissertation and Final Oral Examination follows the GRS General Requirements for the Doctor of Philosophy Degree .
Satisfactory Progress Toward the Degree Upon entering the graduate program, each student should consult the Director of Graduate Studies (Prof. David Rohrlich) and the Associate Director of the Program in Statistics (Prof. Konstantinos Spiliopoulos). Initially, the Associate Director of the Program in Statistics will serve as the default advisor to the student. Eventually the student’s advisor will be determined in conjunction with their dissertation research. The Associate Director of the Program in Statistics, who will be able to guide the student through the course selection and possible directed study, should be consulted often, as should the Director of Graduate Studies. Indeed, the Department considers it important that each student progress in a timely manner toward the degree. Each M.A. student must have completed the examination by the end of their second year in the program, while a Ph.D. student must have completed the qualifying examination by the third year. Students entering the Ph.D. program with an M.A. degree must have completed the qualifying examination by October of the second year. Failure to meet these deadlines may jeopardize financial aid. Some flexibility in the deadlines is possible upon petition to the graduate committee in cases of inadequate preparation.
Students enrolled in the Graduate School of Arts & Sciences (GRS) are expected to adhere to a number of policies at the university, college, and departmental levels. View the policies on the Academic Bulletin and GRS website .
Residency Post-BA students must complete all of the requirements for a Ph.D. within seven years of enrolling in the program and post-MA students must complete all requirements within five years. This total time limit is set by the Graduate School. Students needing extra time must petition the Graduate School. Also, financial aid is not guaranteed after the student’s fifth year in the program.
As with all Ph.D. students in the Department of Mathematics and Statistics, the main source of financial aid for graduate students studying statistics is a Teaching Fellowship. These awards carry a stipend as well as tuition remission for six courses per year. Teaching Fellows are required to assist a faculty member who is teaching a course, usually a large lecture section of an introductory statistics course. Generally, the Teaching Fellow is responsible for conducting a number of discussion sections consisting of approximately twenty-five students each, as well as for holding office hours and assisting with grading. The Teaching Fellowship usually entails about twenty hours of work per week. For that reason, Teaching Fellows enroll in at most three courses per semester. A Teaching Fellow Seminar is conducted to help new Teaching Fellows develop as instructors and to promote the continuing development of experienced Teaching Fellows.
Other sources of financial aid include University Fellowships and Research Assistantships. The University Fellowships are one-year awards for outstanding students and are service-free. They carry stipends plus full tuition remission. Students do not need to apply for these fellowships. Research Assistantships are linked to research done with individual faculty, and are paid for through those faculty members’ grants. As a result, except on rare occasions, Research Assistantships typically are awarded to students in their second year and beyond, after student and faculty have had sufficient time to determine mutuality of their research interests.
Regular reviews of the performance of Teaching Fellows and Research Assistants in their duties as well as their course work are conducted by members of the Department’s Graduate Committee.
The department considers it essential that each student progress in a timely manner toward completion of the degree. The following are the deadlines for achieving the milestones described in the Degree Requirements and constitute the basis for evaluating satisfactory progress towards the Ph.D. These deadlines are not to be construed as expected times to complete the various milestones, but rather as upper bounds. In other words, a student in good standing expecting to complete the degree within the five years of guaranteed funding will meet these milestones by the much e arlier target dates indicated below. Failure to achieve these milestones in a timely manner may affectfinancial aid.
If you have any questions regarding our PhD program in Statistics, please reach out to us at [email protected]
Applied statistics is the implementation of statistical methods, techniques, and theories to real-world problems and situations in science, engineering, business, medicine, social sciences, and more. The discipline involves collecting, summarizing, analyzing, interpreting, and presenting data to make informed decisions, solve problems, and answer questions.
Applied statisticians also use advanced techniques, such as machine learning algorithms, to extract insights and patterns from large datasets. Experts in applied statistics work in several settings: research institutions, the government, business and finance, universities, healthcare systems, and more.
With Michigan Tech's Online MS in Applied Statistics , you, too, can build the specialized skills to meet the growing demand for statistics and data professionals. MTU's distance-learning, research-based MS combines tested techniques with emerging technologies while maintaining the high standards of a Michigan Tech graduate education.
The online MSAS curriculum is based on direct industry feedback. Through in-depth coursework, you will learn how to merge theory and application, enabling you to graduate ready to address the data-driven challenges that public, private, and non-governmental institutions face.
You'll develop expertise in several programming languages and tools that applied statisticians regularly use on the job.
Our compact, 7-week courses are periodically offered throughout the year. Depending on what courses you take and how many you take, you may be able to complete your graduate degree more quickly. Please check the courses and registration page to see what courses are currently running.
Whatever course you take, you'll be taught by expert faculty from the Department of Mathematical Sciences who conduct research on the best mathematical and statistical approaches to diverse problems. You'll receive the same quality instruction as do students in MTU's on-site classes.
Need to brush up on your Linear Algebra ? No problem. MTU offers an affordable Linear Algebra Bridge Course for those who want to apply to (or are provisionally accepted into) the Online MSAS degree.
Introduction to probabilistic methods. Topics include probability laws, counting rules, discrete and continuous random variables, moment generating functions, expectation, joint distributions, and the Central Unit Theorem.
Topics include sampling distributions, theory of point and interval estimation, properties of estimators, and theory of hypothesis testing.
Covers simple, multiple, and polynomial regression; estimation, testing, and prediction; weighted least squares, matrix approach, dummy variables, multicollinearity, model diagnostics and variable selection. A statistical computing package is an integral part of the course.
Covers construction and analysis of completely randomized, randomized block, incomplete block, Latin squares, factorial, fractional factorial, nested and split-plot designs. Also examines fixed, random and mixed effects models and multiple comparisons and contrasts. The SAS statistical package is an integral part of the course.
Introduction to design, conduct, and analysis of statistical studies, with an introduction to statistical computing and preparation of statistical reports. Topics include design, descriptive, and graphical methods, probability models, parameter estimation and hypothesis testing.
Course will cover various topics in statistical data mining, including linear model selection and regularization, regression and smoothing splines, unsupervised learning, resampling methods, tree-based methods, and deep learning. This course will introduce modern statistical data mining techniques and their applications.
Introduction to computationally intensive statistical methods. Topics include resampling methods, Monte Carlo simulation methods, smoothing technique to estimate functions, and methods to explore data structure. This course will use the statistical software R.
Construction, evaluation, and application of generalized linear models to analyze different types of data. Topics include logistic and Poisson regression, multinomial logit models, random effects and mixed effect models, models for repeated measures and longitudinal data. Introduce theory on GLM fitting, hypothesis testing, and diagnostic models.
Statistical modeling and inference for analyzing experimental data that have been observed at different points in time. Topics include models for stationary and non stationary time series, model specification, parametric estimation, model diagnostics and forecasting, seasonal models and time series regression models.
Application, construction, and evaluation of statistical models used for prediction and classification. Topics include data pre-processing, over-fitting and model tuning, linear and nonlinear regression models and linear and nonlinear classification models.
"I chose Michigan Tech because I am a high school math teacher and I was looking for a program in statistics that was 100% online. I was aware of Michigan Tech's reputation and knew they were a solid school in mathematical fields. I also like that no GRE was required for admission." Mary Lonz, High School Math Teacher, MS in Applied Statistics
"The fact that the courses cover multiple applications is what stood out to me. It shows MTU has done adequate research to ensure students succeed post their degree. The professors provide feedback to our discussion posts in a timely manner. I feel they are there for you whenever you need them." Jay Huen Lee, Senior Analyst/Commercial Analytics, MS in Applied Statistics
With MTU's Online MS in Applied Statistics , you'll develop skills for a wide range of industries and sectors where data analysis and statistical methods are implemented to make decisions, solve problems, and improve processes. Some common areas where applied statisticians find employment include the following:
Enhance your career options by earning an online master's degree in applied statistics. Employers recognize advanced training—a master's degree may qualify you for leadership opportunities, project management responsibilities, or the chance to work on more complicated or sophisticated projects.
Take the next steps on your online educational journey at Michigan Tech. We are here to answer your questions and to help you succeed in your online program.
On this page:, at a glance: program details.
Degree Awarded: PHD Statistics
As a science, statistics focuses on data collection and data analysis by using theoretical, applied and computational tools. The PhD program in statistics reflects this breadth in tools and considerations while allowing students sufficient flexibility to tailor their program of study to reflect individual interests and goals. Research can be of a disciplinary or transdisciplinary nature.
Curriculum plan options.
Required Core (3 credit hours) STP 526 Theory of Statistical Linear Models (3)
Other Requirements (15 credit hours) IEE 572 Design Engineering Experiments (3) or STP 531 Applied Analysis of Variance (3) IEE 578 Regression Analysis (3) or STP 530 Applied Regression Analysis (3) STP 501 Theory of Statistics I: Distribution Theory 3 (3) STP 502 Theory of Statistics II: Inference (3) STP 527 Statistical Large Sample Theory (3)
Electives (42 credit hours)
Research (12 credit hours) STP 792 Research (12)
Culminating Experience (12 credit hours) STP 799 Dissertation (12)
Additional Curriculum Information Electives are chosen from statistics or related area courses approved by the student's supervisory committee.
Other requirements courses may be substituted with department approval.
Students must pass:
Students should see the department website for examination information.
Each student must write a dissertation and defend it orally in front of five dissertation committee members.
Applicants must fulfill the requirements of both the Graduate College and The College of Liberal Arts and Sciences.
Applicants are eligible to apply to the program if they have earned a bachelor's or master's degree in mathematics, statistics or a closely related area from a regionally accredited institution.
Applicants must have a minimum cumulative GPA of 3.00 (scale is 4.00 = "A") in the last 60 hours of their first bachelor's degree program or a minimum cumulative GPA of 3.00 (scale is 4.00 = "A") in an applicable master's degree program.
All applicants must submit:
Additional Application Information An applicant whose native language is not English must provide proof of English proficiency regardless of their current residency.
Completion of the following courses (equivalents at ASU are given in parentheses) is required. Applicants who lack any of these prerequisite courses must complete them before being considered for admission.
Learn about our programs, apply to a program, visit our campus, application deadlines, learning outcomes.
Statistical analysis and data mining have been identified as two of the most desirable skills in today's job market. Data, and the analysis of data, is big business, and the Department of Labor projects that overall employment of mathematicians and statisticians will grow 33% between 2020 and 2030, much faster than the average for all occupations.
For graduates of the doctoral program in statistics, that means a broad variety of career opportunities in fields as diverse as business, finance, engineering, technology, education, marketing, government and other areas of the economy.
These are just a few of the top career opportunities available for a graduate with a doctoral degree in statistics:
If you have questions related to admission, please click here to request information and an admission specialist will reach out to you directly. For questions regarding faculty or courses, please use the contact information below.
General information, program offerings:.
Graduate program administrator:.
As the field of data science grows and opens new opportunities in many different disciplines, Princeton University’s CSML has kept apace by fostering cutting-edge research and engaging in deep collaboration with faculty members, centers and departments across campus and with outside practitioners in varied industries. Graduate students would be well positioned at Princeton due to the center’s strong interdisciplinary nature and the wide array of exciting research happening on campus.
Princeton graduate students can now earn a Graduate Certificate in Statistics and Machine Learning as a complement to their departmental graduate studies. This certificate is overseen by CSML. There are three requirements in completing the certificate: completing appropriate course work, engaging in research involving statistics or machine learning, and participating in the CSML graduate seminar. (See link on the left-hand menu.)
Data-driven research increasingly involves large-scale complex data that needs to be analyzed using innovative methods from the fields of statistics and machine learning. This approach, coupled with advancing data science’s underlying methods and algorithms, has become an essential component of modern scientific discovery. Princeton University is committed to playing a vital role in preparing students to lead in these areas, and the certificate should deepen and enhance students’ understanding and application of data science techniques.
Program offering: certificate, program description.
The Graduate Certificate Program in Statistics and Machine Learning is designed to formalize the training of students who contribute to or make use of statistics and machine learning as a significant part of their degree program. In addition, it serves to recognize the accomplishments of graduate students across the University who acquire additional training in statistics and machine learning, going beyond the requirements of their own degree programs. This certificate program is open to Princeton University students currently enrolled in a Ph.D. or master’s program at the University. Students must enroll by completing an online application form on the CSML website. The application will include a tentative plan and timeline for completing all the course requirements. Students are encouraged to sign up as soon as possible, and no later than one semester prior to graduation. Because Ph.D. students who have entered Dissertation Completion Enrollment (DCE) status are not eligible to enroll in courses, Ph.D. students must enroll in the CSML graduate certificate program in time to complete the course requirements while they are still in their regular degree program length.
For enrollment, please use this form: Graduate Certificate Enrollment Form
For questions, contact us at [email protected]
For students enrolled in a graduate degree program with a thesis or dissertation requirement, the certificate is comprised of three components: (a) completion of three appropriate graduate courses, (b) a relevant research contribution, and (c) a research seminar. We expect that the core courses can be taken as graduate electives, in partial fulfillment of the various course requirements in home departments, and that item (b) will naturally form as part of the student’s thesis or dissertation. For non-thesis master’s students, item (b) is replaced by an appropriate technical presentation. The certificate will appear on a student’s official transcript after all requirements for the certificate have been fulfilled and a graduate degree has been awarded. Students who earn the certificate will also be recognized on the CSML website.
Take for credit and receive an average GPA of B+ (3.3) or better in three courses from the approved list that has three categories: core machine learning, core statistics and probabilistic modeling, and electives. One course must be selected from each category. With the permission of the certificate director, the elective course can be selected from a core category provided it does not significantly overlap with the other course selected from that category. At least one of the three courses must be outside the student’s home department and at most one course can be below the 500 level.
The core curriculum is intended to provide training in the foundations of statistics and machine learning while ensuring that certificate students have some breadth across the core of statistics and machine learning. A list of approved core courses in the two areas is included below. In addition, a certificate student selects the third course from a listed set of elective courses that expands on the core courses. These electives delve more deeply into supporting material (e.g., optimization) or focus on applications in a specific domain.
Students may not count courses that are used to satisfy core requirements in their home department concentration toward this certificate, however, they may count up to two electives that were taken for their degree requirements.
Seminar series (SML 510)
The CSML graduate seminar, SML 510 serves as a venue for discussing current methods and results and the integration of different research approaches to data analysis. Attendance and participation in the CSML graduate seminar for at least one semester is required. It helps teach students how to communicate technical ideas to a broad audience and encourages the development of skills for interacting with other students, postdoctoral fellows, and faculty who are investigating data analysis problems. It also serves to build a supporting community of young scholars with shared interests.
Original Research For students completing a thesis or dissertation as part of their degree, the thesis or dissertation should include a significant component making contributions to statistics or machine learning, or rigorous use of such methods in an application domain. To ensure that an important component of a Ph.D. student's dissertation involves either rigorous data analysis and/or mathematical or computational modeling of data or machine learning problems, one of the dissertation readers or FPO committee members must be a participating graduate certificate faculty member. This reader or committee member is required to send either a letter or the reader's report to the program director to verify that the dissertation satisfies this requirement. Master's students who complete a thesis follow the same requirement.
For non-thesis master's degree students, the original research requirement can be satisfied by completing a relevant graded research project while enrolled in FIN 561 - Master's Project II, or through applied research performed in a professional setting as part of an approved internship. Such research performed in a professional setting must be submitted as a technical presentation and is reviewed for approval by the certificate director.
The original research requirement can also be met by any student through a publishable research paper that is approved by the certificate director.
For a full list of faculty members and fellows please visit the department or program website.
Courses listed below are graduate-level courses that have been approved by the program’s faculty as well as the Curriculum Subcommittee of the Faculty Committee on the Graduate School as permanent course offerings. Permanent courses may be offered by the department or program on an ongoing basis, depending on curricular needs, scheduling requirements, and student interest. Not listed below are undergraduate courses and one-time-only graduate courses, which may be found for a specific term through the Registrar’s website. Also not listed are graduate-level independent reading and research courses, which may be approved by the Graduate School for individual students.
Phi 543 - machine learning: a practical introduction for humanists and social scientists (also sml 543), sml 505 - modern statistics (also ast 505), sml 510 - graduate research seminar, sml 515 - topics in statistics and machine learning (also soc 516).
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Introduction to statistics.
Stanford School of Humanities and Sciences
Stanford's "Introduction to Statistics" teaches you statistical thinking concepts that are essential for learning from data and communicating insights. By the end of the course, you will be able to perform exploratory data analysis, understand key principles of sampling, and select appropriate tests of significance for multiple contexts. You will gain the foundational skills that prepare you to pursue more advanced topics in statistical thinking and machine learning.
The Online Master of Statistics degree at NC State offers the same outstanding education as our in-person program in a fully online, asynchronous environment filled with a vibrant community of learners.
Our online program serves a wide audience. We have traditional students that enter our program directly after their undergraduate studies. However, a large proportion of our online program community have been working for 5+ years and are looking to retool or upscale their careers.
We also have learners with a wide range of backgrounds. Some have strong quantitative skills and want to further their understanding of statistics and dive into the growing field of data science. Still others are practicing data scientists that want a more fundamental understanding of the techniques and analyses they use. The flexibility of our program allows us to serve all of these audiences.
Visit our departmental website for more information about our online master of statistics program.
Graduates of our program develop a strong methodology for working with diverse types of data in multiple programming languages. Their skills at building and assessing predictive and inferential models are honed as well as their ability to communicate to diverse audiences.
We have students from all walks of life. Some come to us directly after their undergraduate coursework, but most are working professionals looking to further their careers or move to a new phase of their lives. Students have six years to complete the degree. Most take one course per semester, including the summer, and are able to finish in three to four years. Other students take a full-time load of three courses per semester and are able to finish in one year.
Our graduates are employed in many fields that use statistics at places like SAS Institute, First Citizens Bank, iProspect, the Environmental Protection Agency, North Carolina State University, and Blue Cross and Blue Shield.
Graduate Certificate
Advance your career with a master’s degree in applied statistics.
Predictive analysis is a crucial organizational resource in the modern, data-driven market. For this reason, the demand for skilled statisticians is rising. According to the U.S. Bureau of Labor Statistics, there is a projected 35% job growth for statisticians through 2029.
Excel in this lucrative industry with a 100% online Master’s degree in Applied Statistics from Purdue. Purdue’s market-driven curriculum teaches students professional skills in probability, statistical theory, statistical methods, experimental design, and data management - preparing them for high-demand positions in statistics and data analysis.
The Department of Statistics at Purdue University is one of the largest and most computationally modernized statistics programs in the United States. Our faculty conduct cutting-edge research in both theoretical and applied statistics, and our students use the latest computational tools to solve problems relevant to today’s world. Our graduates are equipped with the training and experience they need to obtain exciting jobs in industry, government, academic and non-profit institutions.
Our online master’s program makes a world-class statistics education accessible to working professionals. The 33 credit-hour Master of Science in Applied Statistics program can be completed in 24-30 months of consecutive, part-time enrollment. Students will learn advanced statistical skills that will make them more competitive in the job market while being able to maintain full-time careers, family obligations, and other commitments.
The online courses are taught by the same distinguished faculty that teach on-campus at Purdue. Online students will also take an innovative statistical consulting and collaboration course – a capstone-like experience that gives students the opportunity to work on real-world problems alongside experienced statistics faculty.
The Graduate Certificate in Applied Statistics is a 12 credit-hour program for those looking to improve their analytical abilities while gaining a greater knowledge of statistics. If you are accepted into the Master of Applied Statistics program, the credits you earn toward this certificate may be applied to the master's program.
Contact information.
Email: [email protected]
Phone: 765-496-0990
Purdue Department of Statistics, 150 N. University St, West Lafayette, IN 47907
Phone: (765) 494-6030, Fax: (765) 494-0558
© 2024 Purdue University | An equal access/equal opportunity university | Copyright Complaints | DOE Degree Scorecards
Trouble with this page? Accessibility issues ? Please contact the College of Science .
Dietrich college of humanities and social sciences, statistics/machine learning joint ph.d. degree, this program differs from the standard statistics ph.d. program in its emphasis on machine learning and computer science. students in this track will be involved in courses and research from both the departments of statistics and machine learning..
During the first year, students will normally be situated in and supported by the Department of Statistics. During later years, students will be located in the Department of their primary advisor.
Students will be granted the joint degree if they meet TWO sets of program requirements corresponding to the TWO departments, namely the ML Ph.D. Requirements and the Statistics Ph.D. Requirements, as we present next.
Students in this program are subject to all of the core Ph.D. requirements , except that the Data Analysis Exam is not required. (The Data Analysis Exam is required, however, in order to receive the M.S. in Statistics .)
Students in this program are required to complete the Advanced Data Analysis (ADA) project to the same standards as regular Statistics Ph.D. students. Namely, they are required to work on a substantive, real data project with a domain expert as outside advisor. A faculty member from the Department of Statistics must play an oversight role in the project, if not as the primary advisor. This project will satisfy the ML Data Analysis Project (DAP), speaking skills, and writing requirements, provided that an ML faculty member is an advisor.
The student must be advised by a faculty from the home department along with a Core Faculty member from the joint department as a co-mentor. Joint Statistics-MLD faculty cannot serve both roles. Both faculty must be identified at the time of admission to the joint program.
The thesis proposal and defense must be announced to the MLD community.
For questions, please send email to: [email protected] (link sends e-mail)
How to apply.
Students interested in this joint Ph.D. degree should apply to the Ph.D. program that best aligns with their research interests (Ph.D. in Statistics or Ph.D. in Machine Learning).
Machine Learning Ph.D. Online Application
Statistics Ph.D. Online Application
Wharton’s PhD program in Statistics provides the foundational education that allows students to engage both cutting-edge theory and applied problems. These include problems from a wide variety of fields within Wharton, such as finance, marketing, and public policy, as well as fields across the rest of the University such as biostatistics within the Medical School and computer science within the Engineering School.
Major areas of departmental research include: analysis of observational studies; Bayesian inference, bioinformatics; decision theory; game theory; high dimensional inference; information theory; machine learning; model selection; nonparametric function estimation; and time series analysis.
Students typically have a strong undergraduate background in mathematics. Knowledge of linear algebra and advanced calculus is required, and experience with real analysis is helpful. Although some exposure to undergraduate probability and statistics is expected, skills in mathematics and computer science are more important. Graduates of the department typically take positions in academia, government, financial services, and bio-pharmaceutical industries.
Apply online here .
The Wharton School, University of Pennsylvania Academic Research Building 265 South 37th Street, 3rd & 4th Floors Philadelphia, PA 19104-1686
Phone: (215) 898-8222
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Year of entry: 2024
The standard academic entry requirement for this PhD is an upper second-class (2:1) honours degree in a discipline directly relevant to the PhD (or international equivalent) OR any upper-second class (2:1) honours degree and a Master’s degree at merit in a discipline directly relevant to the PhD (or international equivalent).
Other combinations of qualifications and research or work experience may also be considered. Please contact the admissions team to check.
Full entry requirements
Apply online
In your application you’ll need to include:
Full-time | Part-time | Full-time distance learning | Part-time distance learning | |
---|---|---|---|---|
PhD | Y | Y | N | N |
The Department of Mathematics has an outstanding research reputation and a thriving community of PhD students.
Opportunities for PhD research are available in a range of Probability and Statistics topics and Mathemathics research areas . For more information, please see advice on choosing a project or find out more about specific projects . Please contact the relevant individual members of staff for information about a specific project, or get in touch with the Postgraduate Admissions Tutor .
For entry in the academic year beginning September 2024, the tuition fees are as follows:
Further information for EU students can be found on our dedicated EU page.
The programme fee will vary depending on the cost of running the project. Fees quoted are fully inclusive and, therefore, you will not be required to pay any additional bench fees or administration costs.
All fees for entry will be subject to yearly review and incremental rises per annum are also likely over the duration of the course for Home students (fees are typically fixed for International students, for the course duration at the year of entry). For general fees information please visit the postgraduate fees page .
Always contact the Admissions team if you are unsure which fees apply to your project.
There are a range of scholarships, studentships and awards at university, faculty and department level to support both UK and overseas postgraduate researchers.
To be considered for many of our scholarships, you’ll need to be nominated by your proposed supervisor. Therefore, we’d highly recommend you discuss potential sources of funding with your supervisor first, so they can advise on your suitability and make sure you meet nomination deadlines.
For more information about our scholarships, visit our funding page or use our funding database to search for scholarships, studentships and awards you may be eligible for.
Our internationally-renowned expertise across the School of Natural Sciences informs research led teaching with strong collaboration across disciplines, unlocking new and exciting fields and translating science into reality. Our multidisciplinary learning and research activities advance the boundaries of science for the wider benefit of society, inspiring students to promote positive change through educating future leaders in the true fundamentals of science. Find out more about Science and Engineering at Manchester .
Use the links below to view lists of programmes in related subject areas.
The University of Manchester is regulated by the Office for Students (OfS). The OfS aims to help students succeed in Higher Education by ensuring they receive excellent information and guidance, get high quality education that prepares them for the future and by protecting their interests. More information can be found at the OfS website .
You can find regulations and policies relating to student life at The University of Manchester, including our Degree Regulations and Complaints Procedure, on our regulations website .
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Our PhD by Distance programme allows you to benefit from our world-class support and the expertise of a Reading-based supervisor, while conducting your research in a location that suits your circumstances.
The programme is available to candidates who need to study for most of their registration period at another site, whether in the UK, or worldwide. Acceptance for PhD by Distance is subject to the approval of the supervisors and the relevant school or department.
There are several reasons why you may wish to base yourself away from the University of Reading while undertaking your PhD:
On the PhD by Distance programme, you will benefit from:
The doctoral and researcher college provides a suite of generic researcher training open to all doctoral researchers at reading; this complements subject-specific training available via schools. the specific content of the programme changes from year to year but broadly includes the following: .
By Distance study entails reduced access to training opportunities and on-campus experience. By Distance candidates are encouraged to discuss and mutually agree overall training plans with their supervisor and seek supplementary training external to Reading (e.g. via an employer or nearby institution) as appropriate.
A University-wide induction event is held by the Doctoral and Researcher College near the start of each term for all new doctoral researchers. PhD by Distance students studying at Reading in the first term are expected to attend. Where it is not possible for PhD by Distance students to attend in person (i.e. they are not on campus), a recording of the induction will be made available on the virtual learning environment.
Students registered By Distance will benefit from being physically present on campus for periods of time, for instance to attend induction and selected training, to meet their supervisor and peers, and to attend at key points such as Confirmation of Registration. It is strongly recommended that the first month of their programme be spent on campus.
You must meet the following criteria:
If you are intending to study in the Henley Business School, then please check with the relevant Department within the Business School about whether PhD by Distance is available before you apply.
A PhD by Distance is not suitable for all candidates. In offering a PhD By Distance programme specific consideration is given to the suitability of the research project, and whether it can be completed successfully without regular access to campus. Further consideration is given to areas of supervision, support, transferable and subject-specific skills training, research environment , progression milestones and the examination process in order to ensure off campus research students receive a comparable (although, not similar) experience to on campus students registered on ‘standard’ PhD programmes.
Fees for PhD by Distance programmes can be found on the fees webpage
Before starting your application, you are strongly advised to navigate to the PhD webpages of your chosen school or department to read the specific guidance on how to apply, as the requirements can vary. Once you have read the guidance, you will need to make a formal application through the University's online application system , highlighting that you wish to study for a PhD by Distance (full or part-time). If you have questions about PhD by Distance in a specific school or department, then please contact the relevant School/Department PGR Administrator in the School PGR Support Team .
Immigration considerations for international students
PhD by Distance students must ensure that when visiting the University that they obtain the correct visa. The correct type of visa depends upon the period of time which a student intends to spend physically at the University.
PhD by Distance students can visit the University for up to six months within each academic year with a maximum of eighteen months in total for the duration of their programme. Students must discuss their intentions with the Doctoral Research Office (DRO) well in advance of any planned visit to the University. The DRO is well-placed to provide information on a range of immigration-related matters related to Postgraduate Research Programmes [email protected]
Examination
The normal expectation is that the viva of a PhD by Distance student will take place in Reading. Where this is not feasible, online examinations will be arranged.
Further information
Further information on PhD by Distance study can be found in the University's guidance on PhD by Distance .
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Updated: Aug 23, 2024, 10:31am
The past decade has transformed online learning. In 2012, 75% of college students took all their classes in person, with 13.1% taking some online classes and 12.4% taking all of their classes online.6 Those numbers flipped dramatically during the Covid-19 pandemic, with just 26.6% of college students in 2020 reporting that they took no online classes.
What do remote learning statistics look like in the wake of the pandemic shutdowns? Instead of reverting to the pre-pandemic status quo, college students increasingly choose to take classes online. In 2022, more than half of college students enrolled in at least one online class. That adds up to around 10 million college students taking classes online.
Online learners are a diverse group made up of undergraduate and grad students; men and women; and learners of all races. The best online colleges meet high academic standards and hold institutional accreditation . And learning online can help degree seekers save money. Whether you’re considering an online degree or you’re curious about online higher education, these are the key statistics to understand online learning.
Forbes Advisor’s education editors are committed to producing unbiased rankings and informative articles covering online colleges, tech bootcamps and career paths. Our ranking methodologies use data from the National Center for Education Statistics , education providers, and reputable educational and professional organizations. An advisory board of educators and other subject matter experts reviews and verifies our content to bring you trustworthy, up-to-date information. Advertisers do not influence our rankings or editorial content.
A growing number of college students take distance education courses. Digging into the data on online learning reveals a changing picture of higher education.
More than 10 million college students took at least one distance education class in the fall of 2022. Slightly less than half of those students took all of their courses online. Most of these online learners are undergraduates (8.4 million) attending four-year, public institutions (7.4 million).
A significant majority of college students took classes online in fall 2020 due to the impact of the Covid-19 pandemic. While only 36.3% of college students took online classes in the fall of 2019, that percentage more than doubled the following year.
The percentage of students taking online courses dropped between fall 2020 and fall 2022 as Covid-19 campus closures ended and in-person learning resumed. However, substantially more students chose online courses in 2022 compared to pre-pandemic levels in 2019. The shifts in distance learning enrollment indicate that while many students initially enrolled in online courses because of the pandemic, distance learning numbers remained high long after lockdowns ended.
More than 1 in 4 college students chose a fully online format for their courses in Fall 2022. Around 63% of those fully online learners attended in-state colleges, while 33% attended schools located in other states. In-state college students at public universities often benefit from subsidized tuition rates.
Undergraduate and graduate students enroll in online courses at similar rates––54.4% and 53.5%, respectively.
Undergrads are more likely to choose a hybrid option, however. While 23.9% take exclusively online courses, 30.5% take both online and on-campus courses. At the graduate level, 38.7% enroll fully online, while 14.8% take classes in a hybrid format.
Students at private, for-profit colleges are much more likely to take their classes in a fully online format than students attending private, nonprofit colleges and public colleges. While public colleges enroll the largest share of online learners, with 3.1 million of the 4.9 million fully online students, they enroll the smallest percentage of exclusively online students.
Some surprising states top the list of the states with the highest online college enrollment rates. States such as Alaska and West Virginia likely appear because of the challenges of accessing higher education in more remote areas. Alaska, for example, is home to only eight degree-granting postsecondary institutions. 7
The appearance of Utah, New Hampshire and Arizona point to another factor: The three largest online colleges by enrollment are located in those states, and most students at these colleges are enrolled exclusively online. 8
Only a small fraction of colleges primarily enroll distance learners. Part-time students are more likely than their full-time peers to choose primarily online colleges.
Very few colleges meet the definition of a “primarily online college,” according to the NCES. The term only applies to colleges where 90% or more of students enroll online. Instead, many traditional colleges offer both online and on-campus courses.
While more than half of college students now take online classes, only a small fraction attend primarily online colleges. As of Fall 2022, just 6% of all students attended one of these schools.
Over 1 million students attend primarily online colleges. Part-time students are more likely to enroll at one of these institutions than full-time students.
At the undergraduate level, 7% of part-time students attend primarily online schools, compared with 4.4% of full-time students. Similarly, 10.7% of part-time graduate students attend primarily online colleges, compared with 8.1% of full-time graduate students.
While more than 10 million students take at least some distance education courses, 8.5 million do not take any online courses.
Online learning data indicates that private, nonprofit institutions enroll the highest percentage of fully in-person students, with 56.2% of learners not taking any online courses. By contrast, 45% of public school students and 33.3% of private, for-profit learners do not participate in online learning. 6
While the Covid-19 pandemic had a major impact on remote learning, the last decade has brought many other significant changes as well.
In 2012, 74.5% of college students took no online courses. That number declined to 63.7% in 2019. At the same time, the percentage of students exclusively enrolled in online courses grew. In 2012, 12.4% of college students enrolled fully online, while 13.1% took at least one online class. By 2019, the share of fully and partially online learners grew to 17.2% and 19.1%, respectively.
The pandemic upended remote learning, with the share of students not taking online classes plummeting to 26.6%. While that number rebounded in the following years, reaching 41.4% in 2021 and 46.7% in 2022, most college students are still not enrolled exclusively online.
As the Covid-19 pandemic closed schools, many switched to a remote learning model. From 2019 to 2022, the share of fully online learners more than doubled. However, when schools closed their campuses, most students did not have a choice in how they attended classes, and a significant number of degree seekers dropped out of college during this period.
Nearly three in 10 college students enrolled in a hybrid format in fall 2020. Combined, distance learners made up around 75% of all college students at that time, with only 25% taking exclusively in-person classes.
The percent of students taking online classes dropped significantly between fall 2020 and fall 2021 as many campuses reopened following the early pandemic lockdowns. During the same period, total college enrollment declined 2%, with public colleges losing more students than private colleges.
After the sharp decline from 2020 to 2021, distance learning enrollment dipped by nearly 10% from 2021 to 2022. During that period, total college enrollment dropped by around 0.5%.
Overall, the e-learning market brought in an estimated $214.26 billion in 2021. By 2030, the e-learning industry will top $1 trillion in revenue. Still, higher education makes up only part of the total e-learning market.
Online degrees make up a significant share of all degrees, with nearly 280,000 graduates earning their degrees at primarily online colleges in 2022. 3 This is about 6.8% of all degrees earned in the U.S that year. However, these figures undercount the total number of online degrees, because they do not account for online degrees awarded by colleges that also offer in-person degrees.
A bachelor’s degree is the most common degree to earn online, representing 47.4% of all online degrees awarded by primarily online colleges in 2021–22. More than 94,000 graduate students also earned master’s degrees online, making up 33.7% of all online degrees conferred. Associate and doctoral degrees make up smaller fractions of online degrees.
Primarily online colleges awarded less than 7% of all degrees awarded in 2022; they confer master’s degrees at a comparatively higher rate. However, many traditional colleges also award online degrees, indicating that online degrees make up a larger share of total degrees than the 6.8% awarded by primarily online colleges.
Women make up the majority of all college students and an even larger share of graduates from primarily online colleges. At the bachelor’s level, women made up 58.5% of the class of 2022 and 63.6% of primarily online bachelor’s graduates that year.
At the bachelor’s level, primarily online colleges report relatively low graduation rates compared to other types of colleges. While just over 20% of undergraduate students enrolled at primarily online colleges earn their bachelor’s degrees within six years, the graduation rate for similar students at other institutions was 64.6%.
However, online students at two-year schools graduate at higher rates. Primarily online, two-year institutions saw a 45.6% graduation rate as of 2019, compared to 34.1% at all two-year institutions.
Overall, around 6% of college students attend primarily online colleges. However, Pacific Islander, Black and Native American students enroll in these institutions at higher rates.
A relatively large fraction of Pacific Islanders enrolls at primarily online schools. As for students from other racial and ethnic groups:
While white students attend online and traditional colleges at approximately the same rate, Black students make up a larger share of online students. In contrast, Hispanic and Asian learners make up more of the traditional college student body.
Tuition rates differ between online and in-person colleges. In addition, the institution type and residency status also play a major role in the cost of college. While online learning can cost less than in-person learning, that is not always the case. The following tuition rates compare fully online colleges against the median for all colleges.
Public online colleges charge a higher median tuition rate for in-state learners than all public colleges. That means degree seekers may save money by choosing an in-state, public college over a fully online school. Keep in mind that many public colleges offer both in-person and online learning.
Out-of-state students attending public colleges pay significantly higher tuition rates than their in-state counterparts. However, choosing an online learning format can save out-of-state learners money. Overall, out-of-state learners pay lower tuition rates at online public colleges compared to the median out-of-state tuition cost at all public colleges.
Online learners save the most when choosing a private, nonprofit college. While the median cost of tuition and fees at primarily online colleges in this category is around $10,000, the overall cost of tuition and fees reaches nearly $28,000, indicating that degree seekers interested in private, nonprofit institutions can cut costs with an online format.
What is online learning.
Online learning refers to a distance education model in which learners complete course requirements in a virtual environment. Online students may attend live lectures, connect on discussion boards or watch recorded content.
The most popular online learning platforms include Coursera, Canvas, Blackboard and Moodle. Colleges often use Learning Management Systems to deliver online courses.
You can learn online by enrolling in online courses offered by colleges or learning organizations. For example, many accredited colleges offer online classes and degrees.
E-learning can be effective for many students because it offers a flexible, accessible environment to learn. Online learning can also be more cost-effective for students.
Genevieve Carlton holds a Ph.D. in history from Northwestern University and earned tenure at the University of Louisville. Drawing on over 15 years of experience in higher education, Genevieve provides practical, research-based advice on college degrees, career training and other higher education topics.
With five years of experience as a writer and editor in the higher education and career development space, Ilana has a passion for creating accessible, relevant content that demystifies the higher-ed landscape for traditional and nontraditional learners alike. Prior to joining Forbes Advisor's education team, Ilana wrote and edited for websites such as BestColleges.com and AffordableCollegesOnline.org.
IMAGES
COMMENTS
Northcentral University. Located in Minneapolis, Minnesota, North Central University has a robust online Ph.D. in data science program with a specialization in Statistics. You will learn advanced statistical analysis and explore research methodologies. The estimated time of completing the program is 40 months.
Graduate students at nonprofit private universities paid an average of $20,408 per year in 2022-23, according to the National Center for Education Statistics. Over the course of a typical three ...
With a history of over 250 years of academic excellence and 87 Nobel Prize Winners, graduate students in the Statistics Department can expect to acquire a degree of high academic standing. The Ph.D. in Statistics at Columbia is designed to train students in theoretical statistics, applied statistics, and probability.
Statistics and Data Science. Wharton's PhD program in Statistics and Data Science provides the foundational education that allows students to engage both cutting-edge theory and applied problems. These include theoretical research in mathematical statistics as well as interdisciplinary research in the social sciences, biology and computer ...
With the Statistics Graduate Program, you will gain an advanced skill set that can be applied to nearly all branches of science and technology. Statistical methods are used to analyze experiment results, test significance, and display results accordingly. Statistics can also be vital to making informed business decisions, particularly in times of uncertainty, by helping forecast seasonal ...
There are few, if any, available online statistics programs at the Ph.D. level. You can, however, take some prerequisite courses online. Some of the graduate coursework may be available online, but most Ph.D. programs require on-campus attendance. One reason for this is that many programs require doctoral candidates to teach or perform research.
PhD Program Overview. The PhD program prepares students for research careers in probability and statistics in academia and industry. Students admitted to the PhD program earn the MA and MPhil along the way. The first year of the program is spent on foundational courses in theoretical statistics, applied statistics, and probability.
The Statistics and Data Science Master's degree programs are intended as a terminal degree and do not lead to the doctoral program in Statistics. Recommended preparatory courses include advanced undergraduate level courses in linear algebra, statistics/probability and proficiency in programming. This is not an online program.
The Ph.D. program in statistics prepares students for a career pursuing research in either academia or industry. The program provides rigorous classroom training in the theory, methodology, and application of statistics, and provides the opportunity to work with faculty on advanced research topics over a wide range of theory and application areas.
Online Master of Science in Applied Statistics (MS) The median annual salary for mathematicians and statisticians is $104,860 according to the US Bureau of Labor Statistics (2023). And the job outlook for 2022-2032 is 30% growth, which is much faster than average (US BLS, 2023).
Program Contact Information. If you have questions related to admission, please click here to request information and an admission specialist will reach out to you directly. For questions regarding faculty or courses, please use the contact information below. [email protected]. 480/965-3951. A unit of.
The Graduate Certificate Program in Statistics and Machine Learning is designed to formalize the training of students who contribute to or make use of statistics and machine learning as a significant part of their degree program.In addition, it serves to recognize the accomplishments of graduate students across the University who acquire additional training in statistics and machine learning ...
Click "ENROLL NOW" to visit Coursera and get more information on course details and enrollment. Stanford's "Introduction to Statistics" teaches you statistical thinking concepts that are essential for learning from data and communicating insights. By the end of the course, you will be able to perform exploratory data analysis, understand ...
The Department of Statistics offers the 12-credit online Graduate Certificate in Statistics Education. The program is designed to provide convenient online access to the statistical training that is oftentimes required for career advancement. The application process is very simple, and a certificate will be earned by successful completion of ...
Graduate Certificate. Statistics Education . Graduate Certificate. Applied Statistics and Data Management . Online and Distance Education Home. Campus Box 7113 Raleigh, NC 27695-7555. 919.515.9030 1.866.GO.STATE (467.8283) Accreditation
Our online master's program makes a world-class statistics education accessible to working professionals. The 33 credit-hour Master of Science in Applied Statistics program can be completed in 24-30 months of consecutive, part-time enrollment. Students will learn advanced statistical skills that will make them more competitive in the job ...
For questions, please send email to: [email protected] (link sends e-mail) ML Joint Program Requirements ... Machine Learning Ph.D. Online Application. Statistics Ph.D. Online Application. Statistics & Data Science Dietrich College of Humanities and Social Sciences Carnegie Mellon University 5000 Forbes Avenue Pittsburgh, ...
PhD Program. Wharton's PhD program in Statistics provides the foundational education that allows students to engage both cutting-edge theory and applied problems. These include problems from a wide variety of fields within Wharton, such as finance, marketing, and public policy, as well as fields across the rest of the University such as ...
All regions. London. North West. Scotland. Yorkshire and the Humber. Study from anywhere, anytime with Online Postgraduate Statistics programs. Search courses and apply now.
Fees. For entry in the academic year beginning September 2024, the tuition fees are as follows: PhD (full-time) UK students (per annum): Band A £4,786; Band B £7,000; Band C £10,000; Band D £14,500; Band E £24,500. International, including EU, students (per annum): Band A £28,000; Band B £30,000; Band C £35,500; Band D £43,000; Band E ...
On the PhD by Distance programme, you will benefit from: the opportunity to study on either a part-time (4-6 years duration) or full-time basis (3-4 years duration) supervision from one or more leading University of Reading academics, working at the forefront of their field. access to a range of high-quality training, delivered on campus or ...
Choose from more than 60 online classes offered through Coursera, a leading provider of massive open online courses, or MOOCs. Courses are free and open to anyone who is interested—you don't have to be enrolled at Johns Hopkins. Topics include bioinformatics, data analysis, the science of patient safety, and caring for people with Alzheimer ...
In 2012, 12.4% of college students enrolled fully online, while 13.1% took at least one online class. By 2019, the share of fully and partially online learners grew to 17.2% and 19.1% ...