Post-Baccalaureate Certificate in Fundamentals of Survey Statistics, Online (Z133)
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@example.com.
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 can be completed in twelve months of continuous part-time enrollment. See Designation of Full-time/Part-time Status.
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 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||SURV702||Analysis of Complex Survey Data|
|Recommended||SURV735||Data Privacy and Data Confidentiality (previously Data Confidentiality and Statistical Disclosure Control)|
|Recommended||SURV667||Introduction to Record Linkage with Big Data Applications|
|Recommended||SURV662||Small Area Estimation|
|Recommended||SURV750||Step by Step in Survey Weighting|
|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||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|
- 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|
- Features 100% online instruction with engaging and interactive learning.
- Uses the semester academic calendar with classes held in the 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.
- 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.