Skip to main content

Post-Baccalaureate Certificate in Fundamentals of Survey Statistics, 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 Post-Baccalaureate Certificate in Fundamentals of Survey Statistics, Online (Z133) has a 12-credit curriculum that provides advanced training in sampling design and estimation for individuals who have graduate-level coursework in statistics but desire specific knowledge and training in survey statistics.

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:

  • Core (7 credits) 
  • Recommended (2 credits)
  • Electives (3 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 Course Number Title
Core SURV400 Fundamentals of Survey and Data Science
Core SURV626 Sampling I
Core SURV702 Analysis of Complex Survey Data
Recommended SURV735 Data Privacy and Data Confidentiality (previously Data Confidentiality and Statistical Disclosure Control)
Recommended SURV726 Multiple Imputation
Recommended SURV667 Introduction to Record Linkage with Big Data Applications
Recommended SURV662 Small Area Estimation
Recommended SURV636 Sampling II
Recommended SURV750 Step by Step in Survey Weighting
Elective SURV631 Questionnaire Design
Elective SURV751 Introduction to Big Data and Machine Learning
Elective SURV656 Web Survey Methodology
Elective SURV725 Item Nonresponse and Imputation
Elective SURV627 Experimental Design and Causal Inference
Elective SURV635 Usability Testing for Survey Research
Elective SURV665 Introduction to Real World Data Management
Elective SURV736 Web Scraping and APIs
Elective SURV624 Privacy Law
Elective SURV673 Introduction to Python and SQL
Elective SURV752 Introduction to Data Visualization
Elective SURV611 Review of Statistical Concepts
Elective SURV706 Generalized Linear Models
Elective SURV612 Ethical Considerations for Data Science Research
Elective SURV675 Modern Workflow in Data Science
Elective SURV753 Machine Learning II
Elective SURV699E Survey Design and Implementation in International Contexts

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

Semester Year Type Course Number Section Code Credits
Fall 1 Core SURV400 PLS* 3
Fall 1 Core SURV626 PLS* 2
Spring 1 Core SURV702 PLS* 2
Spring 1 Recommended SURV735 PLS* 2
Fall 2 Recommended SURV726 PLS* 1
Fall 2 Elective SURV*** PLS* 1
Spring 2 Elective SURV*** PLS* 1


  • 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 statistical survey methods. This will include mastery of the main aspects of sample design, creation of estimators, understanding of specialized techniques for sampling and estimation, analysis, and data summarization.
  • Analyze solutions to survey design problems in a practical setting.
  • 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.
Questions? Contact Us