AI News, The Definitive Guide to Do Data Science for Good

The Definitive Guide to Do Data Science for Good

8 min read You are a fully-equipped (or aspiring) data scientist and want to use your precious skills for solving problems that really itch the world?

If you want to attend a conference on the topic I’d say Do Good Data in Chicago is the best bet as the leading conference on data, research, and analytics for social sector professionals.

DataKind is a true pioneer in the field and does a phenomenal job of getting volunteers excited about harnessing the power of data science in the service of humanity.

This initiative works on creating a network of volunteers, produces content to educate around the usage of data for social good (mostly infographics) and provides consulting services for social organizations (more about Data4Good in this blog post).

However, at the time of writing I got 0 results for “volunteer data scientist” and 1 result for “volunteer data analyst”.

It took me a bit to understand their “activation facilitation process”, but it’s a great idea (this diagram helps).

You can volunteer through their member organizations who provide data science and coding tasks of different complexity (check out this diagram to see the members’ services).

Apart from fellowships you might become what I call a “data angel”, a full-time data scientist working at a company that partners with a non-profit.

Their first data scientist was hired in early 2015 (see here) and you should check out DataKind’s careers page for upcoming positions.

They launched in 2014 and their approach is to take on a few large projects at a time rather than spreading their resources across many smaller projects.

Their vision is to build operational data science solutions for large-scale problems that affect millions of people.

As non-profits are beginning to understand that data science can help them achieve their goals, a few of them have already created full-time positions for data scientists.

You might also want to look for jobs in for-profit companies whose mission is to use cutting-edge data science to solve pressing societal problems.

Or Edgeflip in Chicago who want to enable non-profits and issue-based groups to better reach their online communities using data science.

Off the beaten path From my German perspective it seems like the vast majority of occasions to apply your skills for social good are in the U.S. I became interested in the field in 2013 and I didn’t find an organization in my city that allowed me to use my skills for social good.

Now I founded foodtracks.de which helps bakeries to become more efficient with the chance to save tons of food waste every day.

Five principles for applying data science for social good

Editor's note: Jake Porway expanded on the ideas outlined in this piece in his Strata + Hadooop World NYC 2015 keynote address, 'What does it take to apply data science for social good?'

It’s a satirical take on our sector’s occasional tendency to equate narrow tech solutions like “software-designed data centers for cloud computing” with historical improvements to the human condition.

Whether you take it as parody or not, there is a very real swell in organizations hoping to use “data for good.” Every week, a data or technology company declares that it wants to “do good” and there are countless workshops hosted by major foundations musing on what “big data can do for society.” Add to that a growing number of data-for-good programs from Data Science for Social Good’s fantastic summer program to Bayes Impact’s data science fellowships to DrivenData’s data-science-for-good competitions, and you can see how quickly this idea of “data for good” is growing.

Yes, it’s an exciting time to be exploring the ways new datasets, new techniques, and new scientists could be deployed to “make the world a better place.” We’ve already seen deep learning applied to ocean health, satellite imagery used to estimate poverty levels, and cellphone data used to elucidate Nairobi’s hidden public transportation routes.

At DataKind, we’ve spent the last three years teaming data scientists with social change organizations, to bring the same algorithms that companies use to boost profits, to mission-driven organizations in order to boost their impact.

Hillary Clinton, Melinda Gates, and Chelsea Clinton stood on stage and lauded the report, the culmination of a year-long effort to aggregate and analyze new and existing global data, as the biggest, most comprehensive data collection effort about women and gender ever attempted.

These datasets are sometimes cutely referred to as “massive passive” data, because they are large, backward-looking, exceedingly coarse, and nearly impossible to make decisions from, much less actually perform any real statistical analysis upon.

The promise of a data-driven society lies in the sudden availability of more real-time, granular data, accessible as a resource for looking forward, not just a fossil record to look back upon.

Mobile phone data, satellite data, even simple social media data or digitized documents can yield mountains of rich, insightful data from which we can build statistical models, create smarter systems, and adjust course to provide the most successful social interventions.

To affect social change, we must spread the idea beyond technologists that data is more than “spreadsheets” or “indicators.” We must consider any digital information, of any kind, as a potential data source that could yield new information.

“data science is not overhead.” But there are many organizations doing tremendous work that still think of data science as overhead or don’t think of it at all, yet their expertise is critical to moving the entire field forward.

As data scientists, we need to find ways of illustrating the power and potential of data science to address social sector issues, so that organizations and their funders see this untapped powerful resource for what it is.

It was clear that, like so many other well-intentioned efforts, the project was at risk of gathering dust on a shelf if the team of volunteers couldn’t help the organization understand what they had learned and how it could be integrated into the organization’s ongoing work.

Take, for example, a seemingly innocuous challenge like “providing healthier school lunches.” What initially appears to be a straightforward opportunity to improve the nutritional offerings available to schools quickly involves the complex educational budgeting system, which in turn is determined through even more politically fraught processes.

DataKind is piloting a collective impact model called DataKind Labs, that seeks to bring together diverse problem holders, data holders, and data science experts to co-create solutions that can be applied across an entire sector-wide challenge.

The current approach appears to be “get the tech geeks to hack on this problem, and we’ll have cool new solutions!” I’ve opined that, though there are many benefits to hackathons, you can’t just hack your way to social change.

Under this media partnership, we will be regularly contributing our findings to O'Reilly, bringing new and inspirational examples of data science across the social sector to our community, and giving you new opportunities to get involved with the cause, from volunteering on world-changing projects to simply lending your voice.

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