As new sources of digital data proliferate in developing economies, there is the exciting possibility that such data could be used to benefit the world’s poor. Recent examples from the research literature show how satellite imagery and deep learning can be used to identify and target pockets of extreme poverty; how mobile phone metadata can help track and stop the spread of Covid-19 and other diseases; how social media analytics can improve disaster response; and how machine learning algorithms can help smallholder farmers optimize planting and harvesting decisions – to name just a few examples.

Through a careful reading of recent research and through hands-on activities and dataset analysis, this course introduces students to the opportunities and challenges for data-intensive approaches to international development. The course will also explore the social and ethical considerations that arise from the use of big data for development and introduce students to practical methods to engage directly with people and communities impacted to inform practical implementations of the use of big data for development. Students should be prepared to dissect, discuss, and replicate academic publications from several fields including development economics, human computer interaction, machine learning, information science, and social science. The course is open to all students and does not have prerequisites — we welcome students with no technical background and no prior experience with social and ethical analyses. All students will be expected to complete a final project that involves some data analysis and some social/ethical analysis.

Learning objectives

After taking this course, students will be able to…

    • Pair methods to identify social and ethical considerations alongside technical approaches, and reflexively articulate the complementarities and tensions between approaches.
    • Identify applications where novel data sources (e.g., satellite imagery, phone data, financial data) can inform challenges of economic development.
    • Analyze real, large-scale LMIC data with tools from machine learning, data mining, and visualization.
    • Effectively communicate with policymakers and a range of stakeholders about the strengths and limitations of specific data-driven approaches to development. 
    • Understand strengths and limitations of methods that directly engage with people and communities who are impacted by the use of big data for development, and leverage methods for bridging across different forms of expertise: technical, policy, and experiential.


Teaching staff

    • Professor Joshua Blumenstock (jblumenstock@berkeley.edu) (pronouns: he/him/his)
    • Emily Aiken (emilyaiken@berkeley.edu) (pronouns: she/her/hers)
    • Zoe Kahn (zkahn@berkeley.edu) (pronouns: she/her/hers)


    • Tuesdays 2:00 – 4:30 in South Hall room 210
    • In-person attendance only, lectures will not be streamed or recorded

Office hours (for enrolled students)

    • Joshua Blumenstock: Tuesdays 11:00 – 12:30 in South Hall 207C.
    • Emily Aiken and Zoe Kahn: Tuesdays 1:00 – 2:00 in South Hall 107. Please sign up for a 15 minute slot via this link (if there are slots available day-of, you can also just come by). 

We enjoy teaching and talking with you. If you have questions or just have an idea you want to explore, come by our office hours. If none of the above times work for you, feel free to email us and we can set up a time that does.  


The best way to contact the teaching staff is via email, at the email addresses listed above. You can contact the most relevant member of the teaching team for your question or include all teaching staff on your message.