Data science (and machine learning) are often associated with profit-focused corporate use-cases, for example, advertising, recommendation engines, fraud detection, predictive analytics, and so on.

But that's not the whole picture.

It is also possible to use data science in ways which have a positive impact on society and the environment.

In this post, I will give you an introduction to the problem domain and hopefully give you a few ideas about how you can get started.

The common good

First of all, the common good is a broad concept. So let's get specific.

A decent working definition might be any sort of data science application that can be used in pursuit of the United Nations' sustainable development goals:

No poverty Zero hunger Good health and well-being Quality education Gender equality Clean water and sanitation Affordable and clean energy Decent work and economic growth Industry, innovation, and infrastructure Reduced inequalities Sustainable cities and communities Responsible production and consumption Climate action Life below water Life on land Peace, justice, and strong institutions Partnership for the goals

Some examples:

Using network insights to make efficient use of resources to provide humanitarian aid to the most at-risk people

Developing intelligent and adaptable supply chains with a lower impact on the environment

Using deep learning to help diagnose diseases

Get started

If you're into data science, and you want to make an impact, here are four ideas for getting started.

Figure out what matters to you

Have a think about what matters to you the most. Whether that's climate change, access to clean water, shared infrastructure, poverty, disaster relief, and so on.

Just as data science can be applied to many areas of business, it can probably also be applied to whatever issue you have identified.

For example:

Satellite imagery and deep learning have been used to help understand environmental conservation in the Brazilian Amazon

Enlitic is using deep learning to make doctors faster and more accurate

Standford University's Sustainability and Artificial Intelligence Lab is using satellite imagery and deep learning to predict poverty

There's probably already a project working on the problem you're interested in. (And if there isn't, perhaps you could start one!)

Level-up your skills

Level-up your skills by contributing to existing projects or by participating in data science challenges for the common good.

You can learn data science and machine learnings skills on any suitable dataset, so why not chose one that tackles challenges that are meaningful to you?

For example, you could get involved with one of these organizations:

Delta Analytics "collaborates with nonprofits to generate positive social impact" with data-driven solutions

Uptake uses "data science and technology to address the world’s most pressing problems"

Datakind harnesses "the power of data science in the service of humanity"

Additionally, the United Nations itself occasionally advertises data science opportunities.

Sites like Kaggle and DrivenData offer a range of challenges that task you with various meaningful challenges. For example, coming up with predictive maintenance solutions for water pumps in Tanzania.

Thee challenges combine the best of both worlds: you get to learn something new, while also potentially improving the world.

Create your own opportunities

If you can't find an opening, perhaps you can create one by offering your skills to an existing project.

Did you know it takes up to 8,000 liters of water to make one pair of conventionally manufactured jeans? That's about 54 bathtubs full...

How does this relate to data science?

Well, maybe this is something you could help with. Either directly, by pitching in your skills to support a sustainable fashion company improve the efficiency and operation of its production line. Or indirectly, by offering to donate your time to an organization that will help improve water management in the affected area.

Not-for-profit organizations in particular stand to gain the most from voluntary participation, because many cannot afford to hire a full-time data scientist.

Get involved

Look for relevant communities on Meetup, Facebook, Twitter, LinkedIn, or any other social media platform. Participate in discussions, read up, watch videos, and start talking with people who care about the same stuff.

Wrap up

Data science can be used to change society in significant ways, and there are a surprising amount of opportunities to get involved—whether that's paid work or volunteering.

Volunteering isn't possible for everyone, but if you have the capacity for it, it's a great way to level-up your skills can help you land paid positions that allow you to contribute in even more significant ways.

Ready to dig deeper? Check out these resources:

Do you have something you'd like to share on this topic? We’d love to hear about it. Get in touch!

In the meantime, check out our recent machine learning miniseries if you want to learn more about CrateDB, machine learning, and data science.