Educational Resource Hub

This course, developed in collaboration with the Community Health Academy at Last Mile Health, introduces learners to the core concepts of community health worker programs, and explores what is needed to build and strengthen large-scale programs in order to improve access to high-quality health services. The curriculum highlights the key components of designing community health systems, addresses common management challenges, and showcases lessons learned from a range of contributors — from community-level practitioners to government leaders and other global health experts. Through case studies of exemplar countries (including Ethiopia, Bangladesh, and Liberia), participants will learn from leaders across the globe how to advocate for, build, and optimize community health worker programs.

This introductory global health course aims to frame global health's collection of problems and actions within a particular biosocial perspective. It develops a toolkit of interdisciplinary analytical approaches and uses them to examine historical and contemporary global health initiatives with careful attention to a critical sociology of knowledge. Four physician-anthropologists - Paul Farmer, Arthur Kleinman, Anne Becker, and Salmaan Keshavjee - draw on experience working in Asia, Africa, Eastern Europe, and the Americas to investigate what the field of global health comprises, how global health problems are defined and constructed, and how global health interventions play out in both expected and unexpected ways.

The demand for skilled data science practitioners in industry, academia, and government is rapidly growing. The HarvardX Data Science program prepares you with the necessary knowledge base and useful skills to tackle real-world data analysis challenges. The program covers concepts such as probability, inference, regression, and machine learning and helps you develop an essential skill set that includes R programming, data wrangling with dplyr, data visualization with ggplot2, file organization with Unix/Linux, version control with git and GitHub, and reproducible document preparation with RStudio.

In each course, we use motivating case studies, ask specific questions, and learn by answering these through data analysis. Case studies include: Trends in World Health and Economics, US Crime Rates, The Financial Crisis of 2007-2008, Election Forecasting, Building a Baseball Team (inspired by Moneyball), and Movie Recommendation Systems.

Throughout the program, we will be using the R software environment. You will learn R, statistical concepts, and data analysis techniques simultaneously. We believe that you can better retain R knowledge when you learn how to solve a specific problem.

The goal of this book is to lay the foundation and provide useful introductory methods in environmental data science, but as a “live” book be able to extend into more advanced methods and provide a growing suite of research examples with associated data sets. We’ll briefly explore some data mining methods that can be applied to so-called “big data” challenges, but our focus is on exploratory data analysis in general, applied to environmental data in space and time domains. For clarity in understanding the methods and products, much of our data will be in fact be quite small, derived from field-based environmental measurements where we can best understand how the data were collected, but these methods extend to much larger data sets. It will primarily be in the areas of time-series and imagery, where automated data capture and machine learning are employed, when we’ll dip our toes into big data.

This four-course series is for those new to mapping and GIS, as well as anyone looking to gain a better understanding of how it all works and why. You will learn how to create GIS data, how to find and evaluate data found online, how to design an effective map, and how to filter data and analyze spatial relationships, and how to work with satellite imagery. In the capstone course, you will apply everything you have learned by either building a portfolio of your work through "mini projects" or planning and executing one large project.

Spatial data science allows analysts to extract deeper insight from data using a comprehensive set of analytical methods and spatial algorithms, including machine learning and deep learning techniques. This course explores the application of spatial data science to uncover hidden patterns and improve predictive modeling. You'll work with powerful analytical tools in Esri's ArcGIS software and learn how to integrate popular open data science packages into your analyses.

Explore the world of spatial analysis and cartography with geographic information systems (GIS). In this class you will learn the basics of the industry’s leading software tool, ArcGIS, during four week-long modules:

Week 1: Learn how GIS grew from paper maps to the globally integrated electronic software packages of today. You will install ArcGIS on your computer and learn how to use online help to answer technical questions.
Week 2: Open up ArcGIS and explore data using ArcMap. Learn the foundational concepts of GIS, how to analyze data, and make your first map.
Week 3: Make your own maps! Symbolize data and create an eye-catching final product.
Week 4: Share your data and maps and learn to store and organize your data.

This book will introduce you to the methods required for spatial programming. We focus on building your core programming techniques while helping you: leverage spatial data from OSM and the US Census, use satellite imagery, track land-use change, and track social distance during a pandemic, amongst others. We will leverage open source Python packages such as GeoPandas, Rasterio, Sklearn, and Geowombat to better understand our world and help predict its future. Some Python programming experience is required, however the material will be presented in a student-friendly manner and will focus on real-world application.