Good Tech Fest 2020-21

Who Is Good Tech Fest for?

We are a community of people that believe in creating impact in this world. That there is something about the world as it is today that we want to see changed for tomorrow. We are the ones working for that change and we believe that if we can responsibly use data and technology that we can be more effective in achieving that mission. We are nonprofit executive directors, foundation program officers, social entrepreneurs, data analysts, product managers, technologists, data scientists, and just plain nerds. We come from around the world to learn and grow together.

Can I present?

Absolutely! Apply to present here. We accept applications to present on a rolling basis and you can apply to present at any of our half-day events, Good Tech Fest, or both!

Do you need sponsors for Good Tech Fest?

Absolutely. This event would not happen without the generous support of sponsors. You can learn about sponsorship opportunities here and contact us with your interest here.

What does my registration include?

All registrants will receive access to the live virtual event, recordings of all of the sessions during that event, and access to our online Slack community. 

Can I get a refund if I can't make it?

We do not offer refunds but you are able to transfer your registration to someone else.

Do you offer scholarships?

You bet! We want to make this as accessible as possible so if you'd like to attend but can't afford the registration fee please contact us via the form below.

The Good Tech Fest Data Science half-day is reminiscent of our old Do Good Data events. It's bringing some of the best data scientists, analysts, visualization experts, and more to talk about the state of data science in the social sector and provide tangible advice for what leaders can do to utilize data in their organizations. During our keynote we will hear from some great practitioners from GivingTuesday, DataKind, Data.org and DrivenData about how they are seeing the sector evolve and how organizations are effectively using data to drive impact.

The details of the breakout sessions are listed below. If you are registered for the Data Science half day event, all of the half day events, or the conference and the half day events you will receive an email soon with a link to the keynote and registration details for the breakout sessions.

As always, if you register for the event you have access to all of the available recordings, even for the sessions you did not attend (or if you can't attend at all live). Don't miss out on this awesome content. Get you and your team registered today!

Speakers Include

• Afua Bruce, DataKind
• Woodrow Rosenbaum, GivingTuesday
• Katherine Townsend, Data.org
• Peter Bull, DrivenData
• Caitlin Augustin, DataKind
• Nick Hamlin, Global Giving
• Erika Salomon, Medic Mobile
• Michael Dowd, DataKind
• Christine Chung, DrivenData
• Emily Miller, DrivenData
• Jay Qi, DrivenData
• Tammy Glazer, DrivenData
• Sarah Barker, OurCommunity

Breakout Session Details

Data Integrity - Nick Hamlin, Erika Salomon, and Michael Dowd with Caitlin Augustin as moderator

12pm CT

Digital tools are making it easier to collect digital health data about patients closer to where they live, to better understand their health needs and treat them faster, and thereby saving lives. Community health workers (CHWs) around the world have been empowered with such tools, logging millions of patient interactions, and yet CHW-collected data is often considered low quality and not reliable for data-driven decision making. At a systemic level, this mistrust in the data quality restricts its potential impact. Without trust in community-collected health data, how can health system managers, leaders, and policymakers confidently invest in frontline health systems, make informed decisions about public health policy, and quickly grasp opportunities to optimize health service delivery and patient outcomes? 

Building confidence in health worker generated data is critical for tracking patients, providing appropriate and timely care, and, importantly, for maintaining integrity across data aggregation pathways into national health information systems. 

In the same way that automated test suites can be used to catch bugs in large-scale software products, similar tests can be created for digital tools being used in the most remote and fragile settings. In this session, attendees will learn how three renowned organizations are harnessing the power of data science to create sustainable, open-source, best-in-class data quality software modules that can automatically detect problematic data for rapid remediation and empower data services and health system managing teams. Enabling them to understand, improve, trust, and use their data. 

Co-created by DataKind, a global data science nonprofit, and Medic Mobile, these open-source solutions aim to be accessible and democratizing for those trying to use and trust community collected information.

Empowering Collaborative Research & the GivingTuesday Data Platform - Woodrow Rosenbaum 

12pm CT

The GivingTuesday Data Commons is a collaborative research initiative including more than 70 collaborators in the US and teams in 47 countries. The platform that has been developed to support these research initiatives includes data assets, code, dashboards and findings, including years of donation transactions from more than 25,000 organizations. The GivingTuesday Data Commons is launching research hubs with partners including Neon, Charity Navigator, and VolunteerMatch. These collaborative workspaces provide researchers with access to data and analysis tools to explore all aspects of giving. In this session you will learn about the Data Commons platform and resources and how you can get involved.

Introduction to Reproducible Machine Learning in Python - Christine Chung and Jay Qi

1pm CT

Come ready to get your data science on. The goal of this workshop is to build your first machine learning model while learning the best tools and practices for reproducibility. You’ll need a laptop and some enthusiasm to get started. We will start from raw data and end by making a set of predictions. This will be an applied workshop looking at a real problem. We’ll be modeling the spread of dengue fever based on time, climate, and location. This will be based on a competition running on DrivenData. The primary goal is for participants to understand that machine learning isn’t magic. In the time it takes to run a tutorial, we can load a dataset, train a model, and make predictions. The secondary goal is to introduce participants to best practices for reproducible machine learning throughout the modeling pipeline. One of the focuses of the example will be demonstrating the resources where participants can learn more about each of the steps in the process.

Data Science + Human Centered Design - Emily Miller and Tammy Glazer

1pm CT

When we’re talking about social impact, we’re not simply measuring and improving a single bottom line number. Improving lives can be measured in many different ways, and (as any data scientist knows) you can only optimize what you measure. How do we think about the work of a data scientist when our objective is less clear? Our new approach joins the quantitative insights from data science with qualitative insights of human-centered design. The interplay between data and design is a powerful new way of thinking about how data can improve products, outcomes, and ultimately lives. In addition to the concrete ways in which data scientists can use design thinking in their work, we present a case study of increasing usage of mobile money among poor and rural farmers in Tanzania. Through both qualitative and quantitative insights, we designed and tested new products to provide financial services to people who have never had access to a bank account before.

Grants AI: How Data is Driving the Dollars - Sarah Barker

2pm CT

Billions of dollars flow into the not-for-profit sector via government, philanthropic, and corporate grants. New tools and techniques for working with data are creating opportunities and challenges when it comes to funding social change.

In this talk we will explore: 

• How can data science help you win grants? 
• How can grantmakers use data science to decide who and what to fund? 

We will walk through specific use cases for applying data science across the grants process and what it means for your organisation’s funding.