At the core of Data Science is the idea of experimentation. Truly innovative solutions only emerge when you experiment with new ideas and applications. Failure is an acceptable byproduct of experimentation. Failures locate regions that no longer need to be considered as you search for a solution.
ABOUT US
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.
Be willing
to Fail.
Fail often and
learn quickly.
Keep the goal
in mind.
Dedication and focus
lead to success.
Complicated does not
equal better.
Be willing
to Fail.
Fail often and
learn quickly.
In addition to a willingness to fail, be ready to fail repeatedly. There are times when a dozen approaches must be explored in order to find the one that works. While you shouldn’t be concerned with failing, you should strive to learn from the attempt quickly. The only way you can explore a large number of solutions is to do so quickly.
Keep the goal
in mind.
You can often get lost in the details and challenges of an implementation. When this happens, you lose sight of your goal and begin to drift off the path from data to analytic action. Periodically step back, contemplate your goal, and evaluate whether your current approach can really lead you where you want to go.
Dedication and focus
lead to success.
You must often explore many approaches before finding the one that works. It’s easy to become discouraged. You must remain dedicated to your analytic goal. Focus on the details and the insights revealed by the data. Sometimes seemingly small observations lead to big successes.
Complicated does not
equal better.
As technical practitioners, we have a tendency to explore highly complex, advanced approaches. While there are times where this is necessary, a simpler approach can often provide the same insight. Simpler means easier and faster to prototype, implement and verify.a
Team
MATTHEW DEUSCHLE
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.
FAQ's
Contact us if you’ve got additional questions, want to start a project or to donate!
What is Data Science?
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).
What makes Data Science Different?
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.
What is the Impact of Data Science?
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.
What is Different Now?
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.’
How does Data Science Actually Work?
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.
Can Data Science be used to solve social and human problems?
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.