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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:


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.

Focus Area 1 Focus Area 2 Course Number Title
Data Analysis - SURV662 An Introduction to Small Area Estimation
Data Analysis - SURV702 Analysis of Complex Survey Data
Data Analysis - SURV751 Introduction to Big Data and Machine Learning
Data Analysis - SURV753 Machine Learning II
Data Analysis Data Generating Process SURV627 Experimental Design and Causal Inference
Data Analysis Data Generating Process SURV673 Introduction to Python and SQL
Data Analysis - SURV611 Review of Statistical Concepts
Data Analysis - SURV706 General Linear Models
Data Analysis Data Curation & Storage SURV725 Item Nonresponse and Imputation
Data Analysis Data Curation & Storage SURV726 Multiple Imputation
Data Analysis Data Curation & Storage SURV750 Step by Step in Survey Weighting
Data Curation & Storage Data Generating Processes SURV667 Introduction to Record Linkage with Big Data Applications
Data Curation & Storage - SURV665 Intro to Real World Data Management
Data Generating Process - SURV736 Web Scraping and API
Data Generating Process - SURV636 Sampling II
Data Generating Processes - SURV626 Sampling
Data Generating Processes - SURV631 Questionnaire Design
Data Generating Processes - SURV635 Usability Testing for Survey Research
Data Generating Processes - SURV656 Web Survey Methodology
Data Output Research Question SURV612 Ethical Considerations for Data Science Research
Data Output Data Curation & Storage SURV675 Modern Workflow in Data Science
Data Output/Access - SURV624 Privacy Law
Data Output/Access - SURV735 Data Privacy and Data Confidentiality
Data Output/Access - SURV752 Introduction to Data Visualization
Research Question - SURV400 Fundamentals of Survey and Data Science

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

Semester Year Course Number Section Code Credits Per Term
Fall 1 SURV* PLS* 9
Spring 1 SURV* PLS* 9
Summer 1 SURV* PLS* 3-6
Fall 2 SURV* PLS* 6-9

Sample Plan of Study, Part-time

Semester Year Course Number Section Code Credits Per Term
Fall 1 SURV* PLS* 6
Spring 1 SURV* PLS* 6
Summer 1 SURV* PLS* 3
Fall 2 SURV* PLS* 6
Spring 2 SURV* PLS* 6
Summer 2 SURV* PLS* 3


  • 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.
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