Data Science for Good, Part 3

This is the third and final part of a three-article series about Data Science for Good. To recap, the first articleexplains what Data Science for Good is and how you can get involved. The second article discusses the institutions and projects that have come about in this field as well as the problems that they strive to solve. In this final installment, we will introduce some of the key people, research and resources in the field of Data Science for Good.

A recompilation of the links shared in these articles can be accessed via this Github repository. Feel free to make suggestions to include them on the repository of Data Science for Good.


In many ways, it can be said that Data Science for Good was invented in the 18th century. How could this be? The field of data science was not even “invented” until the 1960s-1970s. One has to understand the philosophical context of the field to see why the roots of Data Science for Good reside in the 1700s, through the rise of the Enlightenment. The principles of the Enlightenment are foundational components of Data Science of Good due to the similarity of mindsets for those in each field.. Principles such as reason, fact-based science, and humanism, align quite well with the spirit of using data for societal good.

I came upon this realization while reading Enlightenment Now by Steven Pinker. It is a worthwhile book, that will update your knowledge of the world to its current status through the lens of the principles of Enlightenment. The book summarizes and explains in a very straightforward manner many important findings in societal topics such as life expectancy, inequality, the environment, poverty, health, wealth, human rights, etc. Throughout these explanations, the book brings to light many truths that are misconstrued to readers such as myself. Let me put it this way: If I were running a Data Science for Good company, this would be one of the books I would give to everyone in the company.


Source: Deep Learning on Medium