Graduate Certificate in Fundamentals of Survey and Data Science

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 Graduate Certificate in Fundamentals of Survey and Data Science, Online (Z129) has a 12-credit curriculum that prepares students to be data-centric research professionals, ready to use new technologies and methodologies to improve the quality of social and statistical data.

• Students receive technical training about how to collect, manipulate, and analyze data to answer a research question.
• Provides students with the skills and knowledge necessary to successfully lead projects and efforts that involve data from both surveys and non-traditional Big Data sources.
• Program can be completed in twelve months of continuous part-time enrollment. 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:

• Core (8 credits)
• Recommended (5 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 16-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.

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

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:

• Expertise in a variety of survey and data science methods, such as Data collection from surveys and APIs; Data cleaning and database management; Data analysis using traditional and modern tools including machine learning approaches; and Data visualization.
• Knowledge of the errors associated with survey estimates that should be accounted for when formulating conclusions.
• Familiarity with how massive datasets and data science tools can improve data and estimates from surveys.
• Awareness of social and ethical implications of their work and their behavior.