Data Science is an auspicious and profound way of applying our curiosity and technical tradecraft to solve humanity’s toughest challenges. The growing power, importance, and responsibility of applying Data Science methodologies to these challenges is unimaginable. Our own biases and assumptions can have profound outcomes on business, national security, and our daily lives. A new class of practitioners and leaders are needed to navigate this new future. Data Scientists are our guides on this journey as they are creating radical new ways of thinking about data and the world around us.

Our Guiding Principles

The set of guiding principles that govern how we conduct the tradecraft of Data Science are based loosely on the central tenets of innovation, as the two areas are highly connected. These principles are not hard and fast rules to strictly follow, but rather key tenets that have emerged in our collective consciousness. We aim to follow them to guide our decisions, from problem decomposition through implementation.



Founder & Principal Data Scientist

I’m the founder of Data For Humanity. I started this journey because I was looking for a way to translate my Data Science skills into social causes I care about in order to faciliate effective action. I hold a MSc in Predictive Analytics from Northwestern University and have over 20 years of experience in data mining, programming and analytics. Through Data Science, I hope we can deepen engagement in social issues and empower people to make a difference in their communities and the world around.


Contact us if you’ve got additional questions, want to start a project or to donate!

Data Science is the art of turning data into actions. is is accomplished through the creation of data products, which provide actionable information without exposing decision makers to the underlying data or analytics (e.g., buy/sell strategies for nancial instruments, a set of actions to improve product yield, or steps to improve product marketing).

GWDGWWData Science supports and encourages shifting between deductive (hypothesis-based) and inductive (pattern-based) reasoning. This is a fundamental change from traditional analytic approaches. Inductive reasoning and exploratory data analysis provide a means to form or refine hypotheses and discover new analytic paths. In fact, to do the discovery of significant insights that are the hallmark of Data Science, you must have the tradecraft and the interplay between inductive and deductive reasoning. By actively combining the ability to reason deductively and inductively, Data Science creates an environment where models of reality no longer need to be static and empirically based. Instead, they are constantly tested, updated and improved until better models are found.

Data Science is required to maintain competitiveness in the increasingly data-rich environment. Much like the application of simple statistics, organizations that embrace Data Science will be rewarded while those that do not will be challenged to keep pace. As more complex, disparate datasets become available, the chasm between these groups will only continue to widen.

For 20 years IT systems were built the same way. We separated the people who ran the business from the people who managed the infrastructure (and therefore saw data as simply another thing they had to manage). With the advent of new technologies and analytic techniques, this artificial – and highly ineffective – separation of critical skills is no longer necessary. For the first time, organizations can directly connect business decision makers to the data. This simple step transforms data from being ‘something to be managed’ into ‘something to be valued.’

Let’s not kid ourselves – Data Science is a complex field. It is diffcult, intellectually taxing work, which requires the sophisticated integration of talent, tools and techniques. But as a guide, we need to cut through the complexity and provide a clear, yet effective way to understand this new world. To do this, we will transform the field of Data Science into a set of simplified activities: Acquire, Prepare, Analyze and Act.

Yes. However, it is important to note the potential limitations associated with approaching social problems from a data science perspective. Like any good project, we start with a series of basic questions to frame the problem at hand and may continously revise this as the data provides new insights. The bottom line: learn the math enough to interpret results correctly, understand the history and story of the social problem, and include people who will be affected by any work in the solution-making process. See our Focus Areas page for more info.