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: jodywill@umd.edu.
Overview
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.
Courses
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 |
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.