Master's Degree Programs in Data Science & Public Policy
This article provides details on master's degree programs in data science and public policy. It describes the duration of this program, learning outcomes, core courses, and the general admission requirements.
General Information about Master's Programs in Data Science and Public Policy
A master's degree program in data science and public policy provides exposure to the policy life cycle, the public policymaking process, and policy analysis; it also introduces several advanced analytical methods and their applications in public policy. This program typically includes several hands-on courses and capstone projects and is sometimes offered in an online delivery format. Though the duration of this program varies from school to school, it can usually be completed within 16-24 months. Some of the common courses in its curriculum are highlighted below.
Policy Analysis Fundamentals
This course provides an introduction to public policy analysis and often explains the importance of thorough research in public policymaking. Students get a chance to learn the process of making policies, about diagnosing issues, and research design and its evaluation to make policy decisions. By the end of this course, students could understand the significance of public policy professionals in policymaking and analyzing the frequently occurring issues in the policymaking process. Some schools also use this course to teach effective speaking and writing skills and how to analyze research summaries and articles to evaluate their design.
Economics in Public Policy
In this course, students could learn how to use economics in designing and analyzing public policy. This course may include several economic methodologies to analyze policy problems and concepts like equity and efficiency. Students may also study economic concepts like time discounting, decision making in uncertainty, and opportunity costs. In addition, they may also learn about how markets work, when markets perform well, causes of failures, and government intervention in improving the economic system.
An analytical statistics course usually aims to introduce fundamental to advanced statistics concepts useful for data analysis, and it also introduces several statistical tools for better decision making. Students can learn statistical concepts like probability, regression analysis, and inferential statistics, as well as see how to use these concepts and tools in public policy analysis. This course may detail several case studies and real-world software programs that lead toward extensive statistical analysis.
Machine Learning Concepts
This course generally introduces several machine learning concepts and explains their application in public policy data analysis and solving real-world problems. It could include machine learning concepts from linear regression to neural networks and also teach students how to use data mining and other advanced methods to build machine learning systems. In this course, students may also learn to apply appropriate algorithms based on the given data type and generate meaningful predictions using that algorithm.
Databases and Big Data Management
Generally, database-related concepts like data life-cycle, database architecture, big data management, data security, privacy, and data mining concepts are covered in this type of course. Students can learn advanced concepts related to data design, data storage, data processing, and analysis of big data on clusters. This course often discusses different types of databases. like relational databases, database management systems (DBMS), distributed databases, and SQL and NoSQL databases.
Students who enroll in this course may learn about different visualization techniques and software programs for better communication of data analysis outcomes to support effective decision making. They could learn to gather and document the data and design different interactive visualizations using graphics and statistics. Additional course topics may cover identifying potential problems related to public policy and using the data to derive practical solutions to them.
Students applying for a master's degree program in data science and public policy must hold a bachelor's degree from an accredited school; some schools may also require applicants to have a certain GPA, such as a 3.0 or higher. Students need to submit their GRE scores; some schools expect the students to have high quantitative scores in the GRE. Schools also require official transcripts from all previously attended schools and a one- to two-page resume explaining skills, background, professional experience (if any), and achievements. Additionally, students may need to submit an essay explaining career goals and aspirations, as well as some recommendation letters. Schools may also require a video interview as part of the admissions process.
A master's degree in data analysis and public policy equips students with necessary data analytical skills to analyze public policy and play a prominent role in the policymaking process and policy recommendations. This program can often be completed online within two years and is available for students holding a bachelor's degree.