Big Data for Social Good
Big social problems require big data solutions
Using real-world data and policy interventions as applications, this Harvard Online course will teach core concepts in data science, economics, and statistics and equip you to tackle some of the most pressing social challenges of our time.
4–5 hours per week
4–5 hours per week
What You'll Learn
The American Dream—the idea that through hard work any child can rise up and achieve a higher standard of living than their parents—is fading: only half of kids today will go on to earn more than their parents did. Why has this happened? And, how can we reverse the fading of the American Dream?
“Big data” is often associated with corporations seeking to improve products by collecting data on customers. What if we could use big data for social good—to address problems such as the fading American Dream, growing income inequality, or persistent racial disparities?
Big Data for Social Good will teach you how to use big data, coupled with the tools of data science and economics, to solve some of the most important social problems of our time. Big data can help us cut through politically charged debates and find out what policies actually work from a scientific perspective, making the often-discussed notion of “evidence-based policymaking” a reality. Using big data, we can see how the specific neighborhoods in which we grow up and the schools we attend shape our life outcomes—and how we can take these insights to create better opportunities for all.
The course will be delivered via HBS Online’s course platform and immerse learners in real-world examples from experts at industry-leading organizations. By the end of the course, participants will be able to:
- Examine historical income, education and family support, and geography to understand how these economic factors lead to upward mobility
- Understand how big data is used to identify the causes of socioeconomic disparities and how data can lead to evidence-based action and outcomes
- Explore economic methodologies, such as statistical models, regression analysis, and quasi-experiments in data set combinations
- Utilize economic frameworks and apply them to your work
- Use evidence to engage and gain support of communities and constituents to drive systemic policy developments and changes
Your Instructor
Raj Chetty, PhD, is the William A. Ackman Professor of Public Economics at Harvard University and Director of Opportunity Insights. His research uses “big data” to understand how we can give children from disadvantaged backgrounds better chances of succeeding. Chetty's research combines empirical evidence and economic theory to help design more effective government policies. His work on topics ranging from tax policy and unemployment insurance to education and affordable housing has been widely cited in academia, media outlets, and Congressional testimony. He has received numerous awards for his research, including a MacArthur "Genius" Fellowship and the John Bates Clark medal, given to the economist under 40 whose work is judged to have made the most significant contribution to the field.
Real World Case Studies
Affiliations are listed for identification purposes only.
Geoffrey Canada
Hear from Geoffrey Canada, an educator, social activist, and author, about his pioneering work in helping children and families in Harlem and about place-based innovations.
Sarah Oppenheimer
Learn how Sarah Oppenheimer’s work bridges research with applied policy and practice to address poverty and support families’ outcomes.
Nathaniel Hendren
Explore research based on the core question “Do markets provide opportunity?”
Available Discounts and Benefits for Groups and Individuals
Experience Harvard Online by utilizing our wide variety of discount programs for individuals and groups.
Past Participant Discounts
Learners who have enrolled in at least one qualifying Harvard Online program hosted on the HBS Online platform are eligible to receive a 30% discount on this course, regardless of completion or certificate status in the first purchased program. Past Participant Discounts are automatically applied to the Program Fee upon time of payment. Learn more here.
Learners who have earned a verified certificate for a HarvardX course hosted on the edX platform are eligible to receive a 30% discount on this course using a discount code. Discounts are not available after you've submitted payment, so if you think you are eligible for a discount on a registration, please check your email for a code or contact us.
Nonprofit, Government, Military, and Education Discounts
For this course we offer a 30% discount for learners who work in the nonprofit, government, military, or education fields.
Eligibility is determined by a prospective learner’s email address, ending in .org, .gov, .mil, or .edu. Interested learners can apply below for the discount and, if eligible, will receive a promo code to enter when completing payment information to enroll in a Harvard Online program. Click here to apply for these discounts.
Gather your team to experience Data Privacy and Technology and other Harvard Online courses to enjoy the benefits of learning together:
- Single invoicing for groups of 10 or more
- Tiered discounts and pricing available with up to 50% off
- Growth reports on your team's progress
- Flexible course and partnership plans
Learner Experience
Who Will Benefit
Community activists and nonprofits
Use big data and economic principles to gain support and advocate for change in underserved communities.
Policy advocates and public sector employees
Serve the needs and requests of your communities better through evidence-based research and defined policy outcomes.
Students and Recent Graduates
Learn how to combine disparate data sets that lead to key findings, insights, and solutions for addressing economic societal problems.
Course Syllabus and Upcoming Calendars
Learning requirements: In order to earn a Certificate of Completion from Harvard Online and Harvard Business School Online, participants must thoughtfully complete all 8 modules, including satisfactory completion of the associated assignments, by stated deadlines.
- Study The Opportunity Atlas and Brownsville, Brooklyn cases
- Recognize some of the statistical techniques used to measure and map opportunity
- Explore granular variation in levels of upward mobility
- Study the moving to opportunity experiment
- Consider the ethical and societal impacts of social experiments
- Explore two methods for causal inference
- Interpret methods for establishing statistical significance
- Study cases like Creating Moves to Opportunity and the Harlem Children's Zone
- Describe the factors that are correlated with differences in upward mobility across places
- Understand the relationship between supply and demand
- Explain the distinction between constraints and barriers
- Study the American Dream and social capital
- Understand the concept of social capital
- Understand how economic policies can "pay for themselves" in the long terms
- Identify different statistical approaches to measuring upward mobility
- Investigate both redistributive policies and policies that invest in human capital
- Study the effect of mentorship
- Explain the relationship between economic growth and equality of opportunity
- Identify data sources for studying innovation
- Explore innovation as a potential path for increasing both equality of opportunity and economic growth
- Understand how to use propensity score reweighting
- Study college mobility rates
- Explore the extent to which colleges and universities in the US either promote or hinder upward mobility
- Understand how to measure the causal effect of college on a student’s outcomes
- Recognize the importance of both access and outcomes in determining a college’s Mobility Rate
- Understand methods for standardizing data from across different sources
- Study the importance of class size and teacher quality in determining students’ outcomes
- Understand dynamic models and steady states
- Explore differences in upward mobility by race/ethnicity and gender
- Explain that differences in upward mobility lead to the persistence of mobility gaps in “steady state”
