Data Science & Public Policy Master's Degree

Sep 24, 2019

A master's degree in data science & public policy can provide information about performing policy analysis with a data-driven scientific approach. This article explains more details about this type of program, including common courses and admission requirements.

In today's world, data science is crucial in handling ever-expanding information in various fields, and public policy is no exception. A master's degree program in data science & public policy often explores the analysis of critical social issues and robust decision making during policy creation.

Master's Degree in Data Science & Public Policy: General Information

To pursue a master's degree in data science & public policy, applicants are typically required to have a bachelor's degree. Though no specific bachelor's degree is needed, having an undergraduate degree in math or analytics can make the application stand out. Duration of this master's program is normally two years, and this type of program often contains some of the core courses given below.

Economics for Public Policy

This sort of course commonly provides an overview of how economics can be helpful in analysis and decision making in public policies. Students might learn the necessary analytical skills used in economic modeling and applying these skills in public policy areas like taxation, environmental/energy policy, economic stimulus, and monetary policy. Students might also learn how markets function correctly, concepts of market failures, and when government intervention is required.

Public Policy Analysis

This course commonly introduces public policy making process and analysis. Students might learn the fundamentals and process of policy design, ethical issues in policy design. It might also describe the role of public policy professionals in policymaking. It could give a solid understanding of making policy recommendations, speaking, and writing to policymakers. Sometimes this type of course also deals with tools for developing and implementing policies.

Statistics and Methodologies For Data Analysis

This course could provide a foundation for statistical concepts useful in data science. It might introduce methods and tools to summarize and present quantitative data. It may contain statistical concepts like probability, regression analysis, inferential statistics, time-series models, hypothesis testing. Besides, students could learn using powerful statistical software and programming concepts like Python and R, which are useful for better analysis.

Big Data Tools For Data Analysis

This kind of course commonly introduces the characteristics and challenges surrounding big data. Also, it may deal with the software tools and techniques used in the field of data science to handle and analyze big data. Students could learn data analytics programming and visualization techniques. It might also describe data management techniques like data modeling, and building Hadoop clusters using a cloud computing environment.

Data Visualization in Data Science

This course could introduce various visualization techniques required for communication, analysis, and decision support. Besides, students might learn software, which helps to present data graphically in efficient ways. Usually, this sort of courses also teaches identifying potential data, along with collecting, cleaning, and documenting data using software tools.

Data Mining in Data Science

This type of course usually teaches data mining techniques to detect previously unseen relationships and patterns in data. Also, integrating data mining tools with other statistical tools and visualizing options might be discussed. Some programs might include machine learning techniques like map-reduce for analyzing large data sets.

Admittance Requirements

Generally, applicants are expected to have a minimum GPA in undergraduate study. Also, strong quantitative GRE scores are helpful. A few institutions might require applicants to submit a clear and concise essay explaining academic or professional background and interests in choosing this program. Additionally, applicants are commonly asked to submit 2-3 recommendations from academic or professional references.

A master's degree in data science & public policy is typically a two-year program, which usually requires a minimum GPA in bachelor's studies and strong quantitative GRE scores for admission. This program commonly includes core courses of both public policy and data sciences, like economics for public policy, public policy analysis, big data management systems, and data visualization techniques.

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