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The course is designed for those who care about health and healthcare and wish to learn more about how to measure and improve that care – for themselves, for their institutions, or for their countries. Each session will be interactive and provide concrete tools that students can use. We will empower you to raise questions, propose concrete solutions, and promote change.

Today the principles and techniques of reproducible research are more important than ever, across diverse disciplines from astrophysics to political science. No one wants to do research that can’t be reproduced. Thus, this course is really for anyone who is doing any data intensive research. While many of us come from a biomedical background, this course is for a broad audience of data scientists.

To meet the needs of the scientific community, this course will examine the fundamentals of methods and tools for reproducible research. Led by experienced faculty from the Harvard T.H. Chan School of Public Health, you will participate in six modules that will include several case studies that illustrate the significant impact of reproducible research methods on scientific discovery.

This course will appeal to students and professionals in biostatistics, computational biology, bioinformatics, and data science. The course content will blend video lectures, case studies, peer-to-peer engagements and use of computational tools and platforms (such as R/RStudio, and Git/Github), culminating in a final presentation of a final reproducible research project.

We’ll cover Fundamentals of Reproducible Science; Case Studies; Data Provenance; Statistical Methods for Reproducible Science; Computational Tools for Reproducible Science; and Reproducible Reporting Science. These concepts are intended to translate to fields throughout the data sciences: physical and life sciences, applied mathematics and statistics, and computing.

Matt, Jess, and guest host Amruta Nori-Sarma examine the impact of cyclones on mortality in the US, they discuss the uneven impacts of global climate change, and Jess tells us what we will be eating in the future.

This course from the Harvard Humanitarian Initiative and HarvardX seeks to prepare learners to recognize and analyze emerging challenges in the humanitarian field. The course explores the ethical and professional principles that guide humanitarian response to conflict and disaster. Participants will learn the legal and historical frameworks that shaped these principles, test their applicability to the challenges faced by humanitarian actors today.

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.