Data plays an increasingly important role in powering today’s enterprises, governments and society as a whole. With the rapid pace of innovation, data science, advanced analytics and Artificial Intelligence (AI) are becoming increasingly central and critical to business today. Over time, social impact organizations will deem these tools as core to achieving their mission.
This 7-week course exposes the students to the application and use of data analytics in setting public policy. The course does so by teaching introductory technical programming skills that allow students to learn and apply Python code on pertinent public policy data, while emphasizing on applicability. The course is accompanied by readings for each class in order to contextualize why data analytics supplements but doesn’t replace the student / professional role in setting public policy.
R is a powerful open source language and environment for statistical computing and graphics. R provides a wide selection of statistical and graphical techniques. It is rapidly becoming the leading language in data science and statistics. R can easily tackle linear and nonlinear modelling, statistical tests, time series analysis, classification, clustering and more.
In our increasingly data-reliant and data-saturated society, people who understand how to leverage data to generate insights have the power to change the world. Data visualization and storytelling is a crucial skill for policy and data analysts, communications and marketing professionals, and managers and decision-makers within nonprofits, social organizations and the government. With the advent of visualization tools that do not require coding, data storytelling in the digital age is also an attainable skill set for people with varying levels of technical ability.
Understanding geographic relationships between people, land use, and resources is fundamental to planning. Urban planners routinely use spatial analysis to inform decision-making. This course will introduce students to Geographic Information Systems (GIS), a tool to analyze and visualize spatial data. The course will emphasize the core functions of GIS: map making, data management, and spatial analysis. Students will learn cartographic best practices, how to find and create spatial data, spatial analysis methodology, and how to approach problem solving from a geographic perspective.
The goal of this course is to develop the key data analytics skill sets necessary to inform evidence-based policy. Its design offers hands-on training in how to make sense of and use large scale real world heterogeneous datasets in the context of addressing real world problems. Students will learn how to scope a policy problem, understand the data generation process, how to manage, combine, and structure data, and how to create, measure and analyze the effect of different data decisions.
This half-semester course will focus on the analysis of data. We will discuss cleaning raw data – including trimming, variable transformations, and dealing with missing data – before turning to complex survey data. We will discuss how regression analysis differs when using complex survey data. Students will take real data and produce a cleaned version, as well as perform simple analyses using multiple regression. One key skill you will learn in this class is Stata, a commonly used statistics package.
The goal of this course is to train advanced students on the principles, practices, and technologies required for good database design, management, and security. An introduction to the concepts and issues relating to data warehousing, governance, administration, security, privacy and alternative database structures will be provided. The course concentrates on building a firm foundation in information organization, storage, management, and security.
Research is an important part of the policy process: it can inform the development of programs and policies so they are responsive to community needs, it can help us determine what the impacts of these programs and policies are, and it can help us better understand populations or social phenomena. This half-semester course serves as an introduction to how to ethically collect data for research projects, with an in-depth look at focus groups and surveys as data collection tools. We will also learn about issues related to measurement and sampling.
The goal of this course is to establish a first-principles understanding of the qualitative and quantitative techniques, tools, and processes used to wield data for effective decision-making. Its approach focuses on pragmatic, interactive learning using logical methods, basic tools, and publicly available data to practice extracting insights and building recommendations. It is designed for students with little prior statistical or mathematical training and no prior pre-exposure to statistical software.
The past decade has seen the increasing availability of very large scale data sets, arising from the rapid growth of transformative technologies such as the Internet and cellular telephones, along with the development of new and powerful computational methods to analyze such datasets. Such methods, developed in the closely related fields of machine learning, data mining, and artificial intelligence, provide a powerful set of tools for intelligent problem-solving and data-driven policy analysis.
The past decade has seen the increasing availability of very large scale data sets, arising from the rapid growth of transformative technologies such as the Internet and cellular telephones, along with the development of new and powerful computational methods to analyze such datasets. Such methods, developed in the closely related fields of machine learning, data mining, and artificial intelligence, provide a powerful set of tools for intelligent problem-solving and data-driven policy analysis.