MS in Data Analytics and Policy
The Master of Science in Data Analytics and Policy prepares students to use analytics to tackle policy challenges in the public and private sectors. Students graduate with expertise in cutting-edge analytical methods relied upon by government agencies, research institutes, private companies, and non-profit organizations. The program emphasizes the application of analytics to substantive issues to develop students into data-driven leaders.
The schedule for completing this 12-course degree program is flexible. Many students work full-time while attending the program on a part-time basis and complete their degree in two years. Full-time students can complete the degree in one year. The MS in Data Analytics and Policy is offered primarily online, though some electives are offered at the Johns Hopkins Washington, DC Center each term.
Students may choose to focus within one of the following specialized areas: political behavior and policy analysis, geospatial analysis, statistical analysis, or public management. The electives cover a wide range of analytical methods, including machine learning, predictive analysis, text analysis, database management systems, computational modeling, civic technology, economic analysis, survey methodology, risk analysis, and data privacy.
Admissions Criteria for all Advanced Academic Programs
PROGRAM SPECIFIC REQUIREMENTS
In addition to the materials and credentials required for all programs, the Master of Science in Data Analytics and Policy requires:
- Statement of purpose (two pages double-spaced): Explain your reasons for seeking admission and how you will use the degree to advance your career. Your statement should also address your ability or potential to perform quantitative analyses.
- Writing sample (5-7 pages double-spaced). The writing sample should demonstrate your ability to make and support an argument. It does not need to be quantitative.
Program Requirements
| Code | Title | Credits |
|---|---|---|
| Core Courses | ||
| AS.470.681 | Probability and Statistics | 3 |
| AS.470.768 | Programming and Data Management | 3 |
| AS.470.673 | Data Visualization | 3 |
| AS.470.709 | Quantitative Methods | 3 |
| AS.470.862 | Capstone for Data Analytics and Policy | 3 |
| Electives 1 | ||
| Select seven of the following: | 21-23 | |
| Machine Learning and Neural Networks | ||
| Database Management Systems | ||
| Applied Performance Analytics | ||
| Computational Modeling for Policy and Security Analysis | ||
| DC Lab: Politics, Policy, and Analytics | ||
| Program Evaluation | ||
| Privacy in a Data-driven Society | ||
| Practical Applications of Artificial Intelligence | ||
| Survey Methodology | ||
| Urban Data Analytics | ||
| Data Science for Public Policy | ||
| Methods of Policy Analytics | ||
| Civic Technology and Smart Cities | ||
| Data Mining and Predictive Analytics | ||
| Data-Driven Campaigns and Elections | ||
| Healthcare Analytics and Policy | ||
| Cloud Computing in the Public Sector | ||
| Big Data Management Systems | ||
| Risk Management in the Public Sector | ||
| Public Policy Evaluation & the Policy Process | ||
| Global Political Economy | ||
| Economics for Public Decision-Making | ||
| Cognitive and Behavioral Foundations for Artificial Intelligence | ||
| Financial Management & Analysis in the Public Sector | ||
| Financial Management and Analysis in Nonprofits | ||
| Terrorist Financing Analysis and Counterterrorist Finance Techniques | ||
| Analyzing Social Media and Geospatial Information | ||
| Text as Data | ||
| The Budgetary Process | ||
| Unleashing Open Data with Python | ||
| Intelligence Analysis | ||
| Geospatial Analysis: Communicating with Multiple Audiences | ||
| Web GIS | ||
| Geographic Information Systems (GIS) | ||
| Geospatial Statistics | ||
| Spatial Analytics | ||
| Programming in GIS | ||
| Remote Sensing: Systems and Applications | ||
| Development and Management of GIS Projects | ||
| Spatial Databases and Data Interoperability | ||
| GIS and Spatial Decision Support Systems | ||
| Spatial Data Management: Quality and Control | ||
| GIS for Infrastructure Management | ||
| Geospatial Ontologies and Semantics | ||
| Cartographic Design and Visualization | ||
| Advanced Topics in Remote Sensing | ||
| Census Data Mining: Visualization and Analytics | ||
| Advanced Python Scripting for GIS | ||
| Web Application Development | ||
| GIS for Emergency Management | ||
| Geo Apps | ||
| System Architecture for Enterprise GIS | ||
| Artificial Intelligence and Machine Learning in Geospatial Technology | ||
| Drones in Geospatial Decision Making | ||
| Big Data Analytics: Tools and Techniques | ||
| Spatial Algorithms and Data Structures | ||
| Advanced Spatio-Temporal Statistics | ||
| 1 | With approval of the program director, students may also choose electives from selected degree programs within Advanced Academic Programs, including Government, Global Security Studies, Applied Economics, Communication and Energy Policy and Climate. |
Sequence of Study
It is recommended that students begin the program by taking AS.470.681 Probability and Statistics along with one elective. In the following term, it is recommended that students take AS.470.768 Programming and Data Management along with one elective. Students should then work through the additional core and elective requirements (generally taking one core course and on elective course per term). The final core course, AS.470.862 Capstone for Data Analytics and Policy, should be completed during the student’s final term (or penultimate term with permission from the student's adviser).
Concentrations
There are four concentrations offered through the MS in Data Analytics and Policy. The concentration in Statistical Analysis focuses on the use of advanced quantitative methods to make data-driven decisions. The concentration in Geospatial Analysis focuses on the applied use of spatially-distributed data. The concentration in Political Behavior and Policy Analysis prepares students to evaluate campaigns, elections, political institutions, and government programs using quantitative methods. Finally, the concentration in Public Management provides students with the tools and skills needed to solve management issues related to policy, finance, and administration. Pursuing a concentration is optional. To earn a concentration, four of the student’s electives must be in the concentration area.
Honors
Students in the MS in Data Analytics and Policy are eligible for program honors. Students who earn a grade of “A-” or better in all their coursework and the capstone seminar will graduate with the distinction of cum laude. Students who earn a grade of “A-” or better in all their coursework and earn an “A” or better in the capstone seminar will graduate with the distinction of magna cum laude. Students who earn the grade of “A” or better in all their coursework and an “A” or better in the capstone seminar will graduate with the distinction of summa cum laude. These honors are program-based and recognized by the Center for Advanced Governmental Studies only.