phd survey and data science michigan

Education & Training

SRC offers a variety of educational opportunities, ranging from a doctoral and masters program in survey methodology to internship opportunities for graduate and undergraduate students. Please select a link below to find out further information on the program of interest to you.

SRC Summer Institute in Survey Research Techniques (SRCSI) (link opens in a new window)

The SRC Summer Institute seeks to provide rigorous and high quality graduate training in all phases of survey research. The program teaches state-of-the-art practice and theory in the design, implementation, and analysis of surveys.

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Program in Survey and Data Science (PSDS) (link opens in a new window)

The University of Michigan Program in Survey Methodology, established in 2001, seeks to train future generations of survey methodologists. The program offers doctorate and master of science degrees and a certificate through the University of Michigan.

Joint Program in Survey Methodology (JPSM) (link opens in a new window)

The Joint Program in Survey Methodology is a cooperative program between the University of Michigan, the University of Maryland and Westat. The program seeks to train future generations of survey methodologists. The program offers doctorate and master of science degrees.

Fellowships (link opens in a new window)

The Survey Research Center supports its scientists in research and training with fellowships.

• SRC Summer Institute in Survey Research Techniques (SRCSI)
• Program in Survey and Data Science (PSDS)
• Joint Program in Survey Methodology (JPSM)
• Fellowships

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Data Science Degree Programs

This is a partial list of degree programs at the University of Michigan, Ann Arbor, that focus on data science.

Graduate Programs

Masters in Data Science (Ann Arbor campus) Offered through the College of Engineering (EECS), the College of Literature Science and the Arts (Statistics), the School of Public Health (Biostatistics), the School of Information, and the Michigan Institute for Data Science, this program provides core data science training focused on computer and information sciences, statistical sciences, and applications.

Data and Business Analytics Concentration This program is designed for students looking to hone their analytical skills in an effort to provide data-driven business recommendations.  Students are required to take 12 credits of analytics related courses. 

Masters in Data Science (Dearborn campus) Offered through the College of Engineering & Computer Science, with four concentrations: computational intelligence, applications, business analytics, big data informatics. 

Masters in Applied Data Science Online This online master’s program will be offered through the School of Information and Coursera.

Masters in Health Data Science (Ann Arbor campus) Offered through the School of Public Health (Biostatistics), this program builds on the core of the MS in Biostatistics with emphasis on big data analytics and computing skills and applications in biomedical sciences and public health.

Undergraduate Programs

Undergraduate Degree in Data Science Offered jointly through the College of Literature, Science and the Arts and the College of Engineering, this program provides a foundation in those aspects of computer science, statistics, and mathematics that are relevant for analyzing and manipulating large complex datasets.

Read more about the BS in Data Science Read more about the BSE in Data Science

BS in Information: Information Analysis Path The courses allow students to identify and articulate questions that matter to stakeholders, gather essential data, find answers that are grounded in empirical evidence, and present answers convincingly. 

Read more about the BSI Program

Programs with Data Science Components

Master of Science in Survey and Data Science Survey methodology combines knowledge from sociology, psychology, statistics, and data science. Students take courses from each of these areas, and will specialize in Survey Methods, Survey Statistics, or Data Science. 

Graduate Certificate Program in Computational Discovery and Engineering  This program trains students to conduct computationally intensive research, and prepares them for interdisciplinary research and product development that employ high-performance computing.

ICPSR Summer Program in Quantitative Methods for Social Research This program offers over 80 courses on a broad range of methodologies and techniques that are relevant for research in the social, behavioral, and health sciences. 

Certificate in Survey Methodology  Offered to graduate students in departments other than the survey methodology program, this is a two-year, part-time program. 

Ph.D. Program in Scientific Computing The joint Ph.D. in Scientific Computing is intended for students who will make extensive use of large-scale computation, computational methods, or algorithms for advanced computer architectures in their studies.

The Data Science Student Experience

Watch U-M Data Science students give presentations to the 2020 NxtGen Summer Academy talking about the work and research they are doing in Data Science.

The Current State of Data Science

The Importance of Access in Data Science

Career Options/Professional Paths in Data Science

Community Data Science Project Examples

Experiential Learning

MIDAS organizes many events throughout the year specifically geared towards students’ technical skill development, job search and career preparation, and engagement with industry professionals in the data science field.

DATA CHALLENGES AND HACKATHONS

Participating as either teams or individuals, students use real world data sets from industry sponsors, community organizations, or University research projects to answer pre-defined research questions.  Often run as competitions, MIDAS aims to promote student work by using judges from industry that may (and many times do) offer outstanding participants internship and permanent job opportunities.

Previous Events:

• MIDAS Basketball Data Madness Challenge 2020 Final Four
• MIDAS Data Challenge
• Project X Data Challenge
• American Heart Association COVID-19 Data Challenge
• MyVoice Data Challenge
• Quicken Loans + MIDAS Data Challenge

INDUSTRY TALK-BACKS

Students learn about how data science is utilized in industry and how best to prepare for careers through conversations with real-world professionals.  Sessions are either centered around a theme (interviewing, company specific job openings, etc.) or feature panelists from various fields to give a broad overview of career opportunities in data science.

• A Data Scientist Plays Games    View Recording  
• Careers in Data Science Panel    View Recording
• Cracking the Coding Interview    View Recording    
• Quicken Loans Career Panel      View Recording  

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• Program in Survey and Data Science

Ann Arbor, MI

Program in Survey and Data Science / Program in Survey and Data Science is located in Ann Arbor, MI, in an urban setting.

Degrees & Awards

Degrees offered, degrees awarded, earning your degree, degree requirements, acceptance rate, application deadlines, entrance requirements, tuition & fees, financial support, student body, race/ethnicity, location & contact.

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• Rackham Graduate School

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Program in survey and data science: ms student fellowship opportunities, michigan program in survey and data science achievement fellowship.

MPSDS Achievement Fellowships are structured to assist eligible MS students with four semesters of tuition support, full time enrollment required.  The fellowship is open to newly admitted students who:

• Have a record of superior academic achievement (e.g., grade point average, honors, or other designation);
• Are U.S. citizens, permanent residents, or undocumented students with Deferred Action for Childhood Arrival (DACA);

 And meet one or more of the following criteria:

• come from educational, cultural or geographic backgrounds that are underrepresented in survey methodology in the United States or the University of Michigan;
• have demonstrated sustained commitment to diversity in the academic, professional, or civic realm through their work experience, volunteer engagement, or leadership of student or community organizations to reduce social, educational or economic disparities;
• have experienced financial hardship as a result of family economic circumstances;
• have participated in programs in the Rackham application open to students from disadvantaged backgrounds;
• are first generation in their families to graduate from a four-year college;
• are first-generation U.S. citizens in their families.

A stipulation of being awarded this funding is that the chosen student will be involved in community building within MPSDS.  Community building is all about bringing people together and creating a sense of inclusiveness, something MPSDS values.  To that end, the recipient will be expected to spend an average of 10 hours per month on community building events during the academic term.  The selected fellow will be supported financially to participate in professional development activities related to the field of survey and data science. Continued funding will be contingent on academic success, employment performance, and community engagement.

To be considered, qualified applicants must email a statement indicating how they meet the criteria and why they are a strong candidate for the Achievement Fellowship. Email your statement to  [email protected] .

Michigan Program in Survey and Data Science Traineeship

The Program in Survey and Data Science will consider all applications to the Program in Survey and Data Science for traineeship opportunities submitted for admitted students. Only MS applicants are eligible for this opportunity.  The traineeship is with the Survey Research Operations (SRO)* unit in the Survey Research Center of the University of Michigan's Institute for Social Research. 

The Full time study traineeship  ( FTS ) provides support for two years of study. 

Tuition coverage for fall and winter semesters for two years.

Hourly employment for four semesters, fall and winter semesters for two years, assuming an average of 20 hours per week.

The trainee will have an opportunity, but not be required, to fulfil their internship requirement by working full time for 10 weeks in the summer between the first and second years of course work in fulfillment of the MPSDS internship requirement.

The trainee will be expected to fulfill two years of permanent employment to begin upon completion of the MPSDS master’s degree program.

Decisions for this traineeship are based on application materials; no separate application is required.

* Survey Research Operations (SRO) is the main data collection unit within the Survey Research Center ( SRC ). SRO constitutes over half of SRC , and provides a wide range of survey design, data collection and data processing services. SRO staff members work closely with members of the Survey Methodology Program and incorporate cutting-edge methodology and technical systems into their projects. SRO conducts national surveys as well as small-scale, regional, and methodological surveys.

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Data Science is often viewed as the confluence of (1) Computer and Information Sciences (2) Statistical Sciences, and (3) Domain Expertise. These three pillars are not symmetric: the first two together represent the core methodologies and the techniques used in Data Science, while the third pillar is the application domain to which this methodology is applied. In this program, core data science training is focused on the first two pillars, along with practice in applying their skills to address problems in application domains.

We characterize the required Data Science skills in two categories: statistical skills, such as those taught by the Statistics and Biostatistics departments, and computational skills, such as those taught by the Computer Science and Engineering Division and the School of Information. The design of the program is to require every student to receive balanced training in both areas. To create an academic plan that achieves this balance, and to foster a greater sense of shared community, we do not intend to offer any sub-plans or tracks within the proposed degree program. Rather, we will expect graduates of this program to understand data representation and analysis at an advanced level. 

With the MS in Data Science all students will be able to:

• identify relevant datasets
• apply the appropriate statistical and computational tools to the dataset to answer questions posed by individuals, organizations or governmental agencies
• design and evaluate analytical procedures appropriate to the data
• implement these efficiently over large heterogeneous data sets in a multi-computer environment

Prerequisites

Our diverse community of graduate students comes from many different countries and many undergraduate majors, including statistics, mathematics, computer science, physics, engineering, information, and data science. While a Data Science undergraduate major is not required, it is expected that applicants will have at least the following background before they join:

• 2 semesters of college calculus
• 1 semester of linear or matrix algebra
• 1 introduction to computing course

MDS Program Curriculum

Downloadable pdf version of the Master’s of Data Science (MDS) program curriculum:  MDS Program Guide

You can review the University of Michigan’s course offerings regardless of term on the  LSA Graduate Course Catalog, and you can search for more details about each course on Atlas.

Students must take the following core courses (unless waived by the course review process, to be determined after matriculation):

MATH 403: Introduction to Discrete Mathematics (First Fall Semester)

EECS 402: Programming for Scientists and Engineers (First Fall Semester)

EECS 403: Graduate Foundations of Data Structures and Algorithms (Offered only in the Fall semester; Prerequisites: MATH 403 & EECS 402)

1 of the following

• BIOSTATS 601: Probability and Distribution Theory
• MATH/STATS 425: Introduction to Probability
• STATS 510: Probability and Distribution
• BIOSTATS 602: Biostatistical Inference
• STATS 426: Introduction to Theoretical Statistics
• STATS 511: Statistical Inference

Expertise in Data Management and Manipulation

• EECS 484: Database Management Systems
• CSE 584: Advanced Database Systems
• EECS 485: Web Systems (available to MDS students in Spring term only)
• EECS 486: Information Retrieval and Web Search
• CSE 549/SI 650: Information Retrieval
• SI 618: Data Manipulation Analysis
• STATS 507: Data Science Analytics using Python

Expertise in Data Science Techniques

1 of the following:

• BIOSTAT 650: Applied Statistics I: Linear Regression
• STATS 500: Statistical Learning I: Linear Regression
• STATS 513: Regression and Data Analysis
• DATASCI 415: Data Mining and Statistical Learning
• STATS 503: Statistical Learning II: Multivariate Analysis
• EECS 545: Machine Learning (CSE)
• EECS 553: Machine Learning (ECE)
• EECS 476: Data Mining
• CSE 576: Advanced Data Mining
• SI 670: Applied Machine Learning
• SI 671: Data Mining: Methods and Applications
• BIOSTAT 626: Machine Learning for Health Sciences

* Please refer to the MDS Capstone Guidelines for details.

• STATS 504: Practice and Communication in Applied Statistics
• STATS 750: Directed Reading
• CSE 599: Directed Study
• SI 691: Independent Study
• SI 699-xx5 Big Data Analytics
• BIOSTAT 610: Reading in Biostatistics
• BIOSTAT 698: Modern Statistical Methods in Epidemiologic Studies
• BIOSTAT 699: Analysis of Biostatistical Investigations

Select 1 course of at least 3 credits from each group. Electives must include at least 2 advanced graduate courses (500 level or above in LSA, UMSI, and CoE, or 600 level or above in SPH). CSE 598 Special Topics will have specific sections approved on a semesterly basis according to their category.

Principles of Data Science

BIOSTAT 601 (Probability and Distribution Theory) | BIOSTAT 602 (Biostatistical Inference) |    BIOSTAT 617 (Sample Design) | BIOSTAT 626 (Machine Learning Methods) | BIOSTAT 680 (Stochastic Processes) | BIOSTAT 682 (Bayesian Analysis) | ECE 501 (Probability and Random Processes) |        ECE 502 (Stochastic Processes) | EECS 545 (Machine Learning (CSE)) | ECE 551 (Matrix Methods for Signal Processing, Data Analysis, and Machine Learning) | EECS 553 (Machine Learning (ECE)) |     ECE 559 (Optimization Methods for SIPML) | ECE 564 (Estimation, Filtering, and Detection) | SI 670 (Applied Machine Learning) | DATASCI 451 (Introduction to Bayesian Data Analysis) | STATS 470 (Introduction to Design of Experiments) | STATS 510 (Probability and Distribution Theory) | STATS 511 (Statistical Inference) | STATS 551 (Bayesian Modeling and Computation) 

Data Analysis

BIOSTAT 651 (Generalized Linear Models) | BIOSTAT 653 (Longitudinal Analysis) | BIOSTAT 666 (Statistical Models and Numerical Methods in Human Genetics) | BIOSTAT 675 (Survival Time Analysis) | BIOSTAT 685/STATS 560 (Non-Parametric Statistics) | BIOSTAT 695 (Categorical Data) | BIOSTAT 696 (Spatial Statistics) | ECE 556 (Image Processing) | STATS 414 (Topics in Applied Data Analysis) | STATS 501 (Applied Statistics II) | STATS 503 (Statistical Learning II: Multivariate Analysis) | STATS 509 (Statistics for Financial Data) | STATS 531 (Analysis of Time Series) | STATS 600 (Linear Models) | STATS 601 (Analysis of Multivariate and Categorical Data) | STATS 605 (Advanced Topics in Modeling and Data Analysis) | STATS 700 (Topics in Applied Statistics) 

Computation

BIOSTAT 615 (Statistical Computing) | BIOSTATS 625 (Computing with Big Data) | EECS 481 (Software Engineering) | EECS 485 (Web Systems) | EECS 486 (Information Retrieval and Web Search) |      EECS 504 (Computer Vision) | EECS 542 (Advanced Topics in Computer Vision) | CSE 548/SI 649 (Information Realization) | CSE 549/SI 650 (Information Retrieval) | CSE 572 (Randomness and Computation) |  CSE 586 (Design and Analysis of Algorithms) | CSE 587 (Parallel Computing) |       CSE 592 (Artificial Intelligence) | CSE 595/SI 561 (Natural Language Processing) | SI 608 (Networks) | SI 618 (Data Manipulation and Analysis) | SI 630 (Natural Language Processing: Algorithms and People) | SI 664 (Database Application Design) | SI 671 (Data Mining: Methods and Applications) | DATASCI 406 (Computational Methods in Statistics and Data Science) | STATS 506 (Computational Methods and Tools in Statistics) | STATS 507 (Data Science Analytics using Python) | STATS 551 (Bayesian Modeling and Computation) | STATS 606 (Computation and Optimization Methods in Statistics) 

Program Notes

• The cumulative GPA must be B (3.0) or better, as required by Rackham Graduate School.
• At least 25 units of graduate-level coursework (from above requirements) must be completed during residency in the Data Science program. Of these 25, 18 must be at the advanced graduate level (500 level or above in LSA, UMSI, and CoE, and 600 level or above in SPH).
• Each course cannot satisfy more than 1 requirement.
• Program requirements on page 1 (courses listed before the Capstone section) may be fulfilled by having taken approved equivalent classes in prior education with grades B- or better. The waiver applications are typically considered before the start of the program. 
• MATH 403 can be fulfilled by EECS 203 if taken before program start. 
• EECS 402 can be fulfilled by EECS 280 if taken before program start.
• EECS 403 can be fulfilled by EECS 281 if taken before program start.
• Expertise in Data Science Techniques part 1 can be fulfilled by STATS 413 if taken before program start.
• Expertise in Data Science Techniques part 2 can be fulfilled by EECS 445 if taken before program start.

Interested in applying? Read through the information about the Master's in Data Science application and our Frequently Asked Questions (FAQs) .  Please also review Rackham Graduate School's minimum requirements to apply , submitting tests , and if relevant the Required Academic Credentials from Non-U.S. Institutions .

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Admissions - PhD in Survey and Data Science

Admission Deadline

For best consideration, applications must be received by January 14, 2022. 

Admission Criteria

Applicants for admission to the Ph.D. program should hold a Masters degree. Provisional admission status may be granted to candidates with a undergraduate degree and other evidence of outstanding potential.

• Complete the  University of Maryland Graduate School application form .
• Provide an official transcript for all undergraduate and graduate courses
• Complete an essay describing their experience and interest in survey methodology
• Submit three letters of recommendation

Qualifying Exam

Qualifying examinations will be given to all students seeking the Ph.D. These will generally be taken by the end of the first year of the student's enrollment in the program. The goal of the examination is to assure that all Ph.D. students share a basic foundation of the interdisciplinary knowledge important to Survey and Data Science. The Ph.D. advisor assigned to the student will provide counsel on what preparations are needed for the individual student prior to taking the qualifying examination.

The qualifying examination will cover the material treated in courses required of both the statistical and social science concentrations of the M.S. in Survey and Data Science. In addition, it will cover two specialty content areas: one on statistical theory and methods for those Ph.D. students seeking to specialize in statistical science; one on statistical methods and data analysis for those seeking to specialize in the social sciences.

No course work is required prior to taking the qualifying examinations, although many students may choose to take courses to fill gaps in their backgrounds.

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Survey and Data Science (online) (MPDS)

Graduate Degree Program College: Behavioral and Social Sciences

The Joint Program in Survey Methodology (JPSM) blends together faculty with diverse disciplinary backgrounds all devoted to teaching state-of-the-art practices in the statistical and methodological aspects of surveys and data.  The program's faculty primarily come from the University of Maryland, University of Michigan and Westat, supplemented by instructors from a number of federal statistical agencies.  Many of these faculty are leading researchers and statisticians in the field of survey methodology, thereby providing an unparalleled educational experience to the students.  JPSM's offerings include onsite PhD and Master’s degrees as well as online Certificates and a Professional Master's degree.

Jody Williams Joint Program in Survey Methodology 1218S LeFrank Hall 7251 Preinkert Drive University of Maryland College Park, MD 20742 Telephone: 301.314.7911 Email:   [email protected]

Website:    https://jpsm.umd.edu  

Courses:  SURV

Relationships:   Intermediate Survey Methodology (Z011)     Survey Methodology (SURV)     Survey Statistics (Z010)

General Requirements

• Statement of Purpose: Complete a one-page essay describing relevant work experience, interest in survey and/or data science, and expected benefits of enrolling in this degree program. (Uploaded to the Statement of Purpose in the application.)
• Transcript(s): Should show previous coursework or knowledge in mathematical/applied statistics demonstrated by completion of 6 credits of applied statistical methods courses covering content from probability theory through basic regression techniques (including both OLS and logistic regression)
• TOEFL/IELTS/PTE ( international graduate students )

Program-Specific Requirements

• Supplementary Application
• Supplementary Application Two:  Prerequisites 
• Description of Research/Work Experience (optional)

*Visa Eligibility: This program is not eligible for I-20 or DS-2019 issuance by the University of Maryland.

Applicants must have earned a four-year baccalaureate degree from a regionally accredited U.S. institution, or an equivalent degree from a non-U.S. institution. Applicants must have earned a 3.0 GPA (on a 4.0 scale) in all prior undergraduate and graduate coursework.

Application Deadlines

Resources and links:.

Program Website:   http://jointprogram.umd.edu/ Application Process:  www.gradschool.umd.edu/admissions

• Survey and Data Science, Master of Professional Studies (M.P.S.)

Training will be provided by permanent and adjunct faculty in the University of Maryland's Joint Program in Survey Methodology. Online lectures will be conducted via accessible video conference systems and Webinar tools.

Students will be instructed that to fully participate, they will need to purchase a webcam and headset with a microphone, and have a reliable computer and Internet access. Recorded lectures will be posted online at announced times and will be available online at any time thereafter during the course.  Weekly discussions or help sessions will be held at scheduled, fixed times once per week.  

As officially admitted students to the University of Maryland, students in this program will have access to all University resources that are accessible in the online environment.  Students in online programs are assessed an online student services mandatory fee which supports access to these University resources.  

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Doctoral Degree Requirements

The Michigan Program in Survey and Data Science (MPSDS) doctoral program consists of several important milestones: 

• Required Coursework and Registration
• Completion of Responsible Conduct of Research and Scholarship (RCRS) Training 
• Achieving Candidacy  

Prospectus Proposal

Writing and defending dissertation, required coursework.

Students are required to take four semesters of PhD level seminars, fulfill a cognate requirement, complete successfully a qualifying and comprehensive examination, develop and write a doctoral dissertation, and to defend the dissertation before a doctoral committee.

Students admitted to the PhD program ordinarily would have completed a course of study equivalent to the Master's degree program. Students may be admitted to the PhD program without such a background, but they will be expected to complete the required background during their first year of PhD coursework. All students must take the qualifying examination at the end of the first year. The examination is written with no outside aids (closed book, closed note) during a one-day examination period, and will cover the contents of the Master's degree program of study.

The PhD program consists of 18 credits of survey and data science and cognate courses, including the four-semester doctoral seminar sequence typically taken in both the first and second year. Seminars are jointly taught by faculty from the social and statistical science areas. The first seminar introduces the doctoral student to areas of integration of social and statistical science approaches in the design, collection, and analysis of surveys.   The second develops and refines doctoral student skills in survey methodology, particularly toward identification of research problems, specification of hypotheses/theorems to extend current understanding of the field, and planning for original research in the field.

It is during the second year course of study students will identify a specialty area within survey methodology and elect courses in the area. At the conclusion of the second year of study, students who have successfully completed the qualifying examination must earn a passing grade on the final paper and final presentation at the end of the last term of enrollment in the Doctoral Seminar. The paper and presentation are designed to assess whether a student has sufficient knowledge and creativity to complete a dissertation.  Students successfully completing the final term in the Doctoral Seminar will be advanced to candidacy, typically at the beginning of their third year of study.

Candidates will, with the ongoing guidance of a doctoral committee, propose and conduct dissertation research that leads to an original scholarly contribution. All doctoral committees will be interdisciplinary, drawing members with backgrounds from a social and a statistical science disciplines, regardless of the students area of interest.

Responsible Conduct of Research and Scholarship (RCRS) Training

Effective for new students in the fall 2014 term, all Ph.D. students are required to complete training in the responsible conduct of research and scholarship before advancing to candidacy.  For MPSDS doctoral students, this training is offered in academic years that begin in fall of an odd numbered years as part of the doctoral seminar in which all first and second year students are required to enroll.

Achieving Candidacy 

Students applying to the PhD program in survey and data science come from a variety of backgrounds.  To help assure that all PhD students are equally prepared for candidacy, students must:

• Complete all course requirements in accordance with program policy,
• Complete four terms in the Doctoral Seminar,
• Earn a passing grade on the Qualifying Examination within twelve months of entering the program, and
• Earn a passing grade on the fnal paper and presentation at the end of the last term of the Doctoral Seminar.

The purpose of the Qualifying Examination (QE) is to test students on their mastery of survey and data science at a level equivalent to that of the successful MS students from MPSDS.  All PhD students must complete the QE within twelve months os their entry into the program.  The exam is offered the 4th Friday in May, which makes it possible for incoming students to take the exam before they formally begin the PhD program.

The Doctorl Seminar, final paper and oral presentation  are designed to assess whether a student has sufficient knowledge, creativity, and aptitude to develop and complete a dissertation. More specifically, the paper and presentation tests students’ ability to identify research questions and design a research study that helps to resolve the problem, communicate the study’s rationale and design in writing, and defend the paper in an oral exchange.  Students prepare a paper to be be presented in a meeting of the student, instructors of the Doctoral Seminar, and any faculty, students or staff who wish to attend.  The presentation will take place no later than May 1 of the student's second year in the program. Final decisions about Candidacy will be made following the student's presentation in the Doctoral Seminar at a full faculty meeting.  If approved, the student will be recommended for candidacy to Rackham Graduate School ( Rackham Graduate School Candidacy Guidelines ).

Detailed documents outlining the examination policies can be requested via email  [email protected] .

The prospectus is a doctoral candidate’s proposal or plan for dissertation research and writing.  In unison with writing a prospectus, a student must assemble a dissertation committee following Rackham Graduate School Guidelines  and work with the designated MPSDS administrative contact to submit the required committee paperwork.

A dissertation prospectus is a proposal for research that has not yet been completed.  The prospectus should contain: (1) an abstract of the specific aims of the investigation; (2) the background and significance of the proposed research, including the conceptual framework; (3) the research design and methods of procedure, including measurement techniques to be used, if applicable; (4) analysis strategies to be followed; (5) a tentative timetable.

Students are required to meet this milestone before the end of the 3rd year of their doctoral studies (end of winter term).

Detailed documents outlining the prospectus proposal policy can be requested via email  [email protected] .

The dissertation is a document in which a student presents his or her research and findings.  It is a comprehensive scholarly product that represents the student's own work.  The final steps for the completion of the doctoral degree entail preparing the dissertation for oral defense, conducting the oral defense and submitting the final copy of the dissertation to Rackham Graduate School.

Students are required to complete this milestone before the end of the 4th year of their doctoral studies (end of winter term).

Rackham Graduate School provides many resources related to the completion of this milestone including The Dissertation Handbook , which will provide details about the process of completing the dissertation.

Master of Professional Studies in Survey and Data Science

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• MPS - Survey and Data Science, Online

Joint Programs in Survey Methodology, Online

Master of professional studies in survey and data science, online .

Joint Programs in Survey Methodology, Online are offered through the Joint Program in Survey Methodology in the College of Behavioral and Social Sciences .

Mentoring and advising are an essential part of the program. Students meet with faculty and the academic program director to ensure that educational goals and career learning and development goals are met. Students should contact Jody D. Williams, Executive Director, via email: [email protected] .

The Master of Professional Studies in Survey and Data Science, Online (MPDS) has a 30-credit curriculum that provides advanced training in areas needed to formulate research goals, determine which data are suited to achieving those goals, professionally collect data, curate and manage the data, analyze it, and communicate results from data analyses. 

• Neither big data nor surveys are sufficient by themselves these days to answer relevant social science research questions. The program systematically combines both aspects, and has a heavy emphasis on understanding the data generating processes.
• Training is meant for professionals interested in broadening their knowledge and understanding of the emerging fields of data sciences, how sample surveys are conducted, practical applications of data analysis and survey methodology, and data management, along with the skills needed to communicate results.
• Program is administered jointly with the University of Mannheim, Germany and in cooperation with the Catholic University of Santiago de Chile—providing participants a rich international context in their study.
• Can be completed in fifteen months of continuous full-time enrollment. Part-time enrollment is welcome. See  Designation of Full-time/Part-time Status .

Program Features

Plan  of study is divided into focus areas and students are required to complete a minimum number of credits in each area as follows:

• Research Questions (3 credits)
• Data Analysis (6 credits)
• Data Generating Processes (4 credits)
• Data Output/Access (3 credits)
• Electives (11 credits)

Students  enroll in a combination of 1-, 2-, or 3-credit courses. For the summer term, the fall or spring semester, a 1-credit course will meet for 4 weeks; a 2-credit course will meet for 8 weeks; and a 3-credit course for 12-weeks.

Below is a listing of all program courses. For a detailed course description that includes pre-requisites or co-requisites, see The Graduate School Catalog, Course Listing as follows: SURV Course Descriptions .

Registration Overview

• See the sample plan of study, below. Students should use this as a guide to develop a plan with the academic program director.
• Actual course offerings are determined by the program and may vary semester to semester. Students should note if a course has a pre-requisite or co-requisite.
• Specific class meeting information (days and time) is posted on UMD’s interactive web service services, Testudo . Once on that site, select “Schedule of Classes,” then the term/year. Courses are listed by academic unit. 
• The program uses specific section codes for registration, which are listed on the sample plan of study.

Sample Plan of Study, Full-time

Sample plan of study, part-time, overall .

• Features 100% online instruction with engaging and interactive learning.
• Uses the semester academic calendar with classes held in fall and spring semester (16 weeks each),and Summer Session (two 6-week sessions).
• Instruction provided by University of Maryland faculty and professionals in the field.

Online Learning

• Using advanced audio and video technology, UMD’s online learning environment delivers dynamic and interactive content.
• Featuring convenience and flexibility, online instruction permits asynchronous or synchronous participation.
• Lectures are video archived. Recorded lecture material will be posted online at a pre-specified time each week. Students who are unable to attend in real time can review the session through asynchronous participation.
• Students are required to view the class within a set period (usually one week) and must submit regular homework assignments that will be graded by teaching assistants.
• Online discussion forums, hosted by the instructor, are used for answering questions and reviewing material presented in lectures.
• At set intervals, students meet at local access points for a long weekend of intensive instruction and hands-on project work (the minimum would be once at the beginning and once during the program). These meetings are designed to foster the creation of a learning community, and further online interactions and collaborations.

Upon successful completion, graduates will have mastered the following competencies:

• Demonstrate competence in the understanding and application of basic concepts that form the foundation of data collection and analysis methods. This will include mastery of the main aspects of data acquisition and analysis from sampling and questionnaire design, through collection, curation, analysis, and summarization.
• Analyze solutions to practical, real-world problems.
• Be able to apply a range of data science techniques to the analysis of datasets of varying sizes (small to large).
• Critically examine published research to determine its strengths and weaknesses and appreciate the limitations and applicability of published findings.
• Produce written documents of a professional quality to communicate such analyses and assessments.

Facility for Rare Isotope Beams

At michigan state university, international research team uses wavefunction matching to solve quantum many-body problems, new approach makes calculations with realistic interactions possible.

FRIB researchers are part of an international research team solving challenging computational problems in quantum physics using a new method called wavefunction matching. The new approach has applications to fields such as nuclear physics, where it is enabling theoretical calculations of atomic nuclei that were previously not possible. The details are published in Nature (“Wavefunction matching for solving quantum many-body problems”) .

Ab initio methods and their computational challenges

An ab initio method describes a complex system by starting from a description of its elementary components and their interactions. For the case of nuclear physics, the elementary components are protons and neutrons. Some key questions that ab initio calculations can help address are the binding energies and properties of atomic nuclei not yet observed and linking nuclear structure to the underlying interactions among protons and neutrons.

Yet, some ab initio methods struggle to produce reliable calculations for systems with complex interactions. One such method is quantum Monte Carlo simulations. In quantum Monte Carlo simulations, quantities are computed using random or stochastic processes. While quantum Monte Carlo simulations can be efficient and powerful, they have a significant weakness: the sign problem. The sign problem develops when positive and negative weight contributions cancel each other out. This cancellation results in inaccurate final predictions. It is often the case that quantum Monte Carlo simulations can be performed for an approximate or simplified interaction, but the corresponding simulations for realistic interactions produce severe sign problems and are therefore not possible.

Using ‘plastic surgery’ to make calculations possible

The new wavefunction-matching approach is designed to solve such computational problems. The research team—from Gaziantep Islam Science and Technology University in Turkey; University of Bonn, Ruhr University Bochum, and Forschungszentrum Jülich in Germany; Institute for Basic Science in South Korea; South China Normal University, Sun Yat-Sen University, and Graduate School of China Academy of Engineering Physics in China; Tbilisi State University in Georgia; CEA Paris-Saclay and Université Paris-Saclay in France; and Mississippi State University and the Facility for Rare Isotope Beams (FRIB) at Michigan State University (MSU)—includes  Dean Lee , professor of physics at FRIB and in MSU’s Department of Physics and Astronomy and head of the Theoretical Nuclear Science department at FRIB, and  Yuan-Zhuo Ma , postdoctoral research associate at FRIB.

“We are often faced with the situation that we can perform calculations using a simple approximate interaction, but realistic high-fidelity interactions cause severe computational problems,” said Lee. “Wavefunction matching solves this problem by doing plastic surgery. It removes the short-distance part of the high-fidelity interaction, and replaces it with the short-distance part of an easily computable interaction.”

This transformation is done in a way that preserves all of the important properties of the original realistic interaction. Since the new wavefunctions look similar to that of the easily computable interaction, researchers can now perform calculations using the easily computable interaction and apply a standard procedure for handling small corrections called perturbation theory.  A team effort

The research team applied this new method to lattice quantum Monte Carlo simulations for light nuclei, medium-mass nuclei, neutron matter, and nuclear matter. Using precise ab initio calculations, the results closely matched real-world data on nuclear properties such as size, structure, and binding energies. Calculations that were once impossible due to the sign problem can now be performed using wavefunction matching.

“It is a fantastic project and an excellent opportunity to work with the brightest nuclear scientist s in FRIB and around the globe,” said Ma. “As a theorist , I'm also very excited about programming and conducting research on the world's most powerful exascale supercomputers, such as Frontier , which allows us to implement wavefunction matching to explore the mysteries of nuclear physics.”

While the research team focused solely on quantum Monte Carlo simulations, wavefunction matching should be useful for many different ab initio approaches, including both classical and  quantum computing calculations. The researchers at FRIB worked with collaborators at institutions in China, France, Germany, South Korea, Turkey, and United States.

“The work is the culmination of effort over many years to handle the computational problems associated with realistic high-fidelity nuclear interactions,” said Lee. “It is very satisfying to see that the computational problems are cleanly resolved with this new approach. We are grateful to all of the collaboration members who contributed to this project, in particular, the lead author, Serdar Elhatisari.”

This material is based upon work supported by the U.S. Department of Energy, the U.S. National Science Foundation, the German Research Foundation, the National Natural Science Foundation of China, the Chinese Academy of Sciences President’s International Fellowship Initiative, Volkswagen Stiftung, the European Research Council, the Scientific and Technological Research Council of Turkey, the National Natural Science Foundation of China, the National Security Academic Fund, the Rare Isotope Science Project of the Institute for Basic Science, the National Research Foundation of Korea, the Institute for Basic Science, and the Espace de Structure et de réactions Nucléaires Théorique.

Michigan State University operates the Facility for Rare Isotope Beams (FRIB) as a user facility for the U.S. Department of Energy Office of Science (DOE-SC), supporting the mission of the DOE-SC Office of Nuclear Physics. Hosting what is designed to be the most powerful heavy-ion accelerator, FRIB enables scientists to make discoveries about the properties of rare isotopes in order to better understand the physics of nuclei, nuclear astrophysics, fundamental interactions, and applications for society, including in medicine, homeland security, and industry.

The U.S. Department of Energy Office of Science is the single largest supporter of basic research in the physical sciences in the United States and is working to address some of today’s most pressing challenges. For more information, visit energy.gov/science.