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Data for Good: Using Data Science in Nonprofits and NGOs

Data for Good: Using Data Science in Nonprofits and NGOs

34mIntermediate2021-01-19

Authors

Martin Kemka

Martin Kemka

Martin Kemka is the founder of Northraine, a machine learning production house.

Course details

The revolution in data science has transformed, for better or worse, the way companies do business in the last few decades. But data science isn't just for capitalists. AI is one of the most important forces for change developed in the last century, and we've only begun to recognize its true power and potential. In Data for Good: Using Data Science in Nonprofits and NGOs, Martin Kemka examines how data science and machine learning can be used outside of the for-profit sector. He covers parameters for how data works to influence our world, how the data-for-good concept can be applied, how to determine the best problems to solve with data, and what tools are needed. Martin also provides real-world case studies and challenges to further illustrate the lessons.

Skills covered

Nonprofit ManagementData Science FoundationsPersonaData ScienceLeadership and Management

Concepts

0. Introduction

  • 01 - Data science isn't just for capitalists

1. Data Use Overview

  • 02 - How data is used everyday
  • 03 - Oversight to data use
  • 04 - Machine learning and data science overview
  • 05 - Challenge - Define machine learning
  • 06 - Solution - Define machine learning

2. How the Data for Good Concept Is Applied

  • 07 - Application for data for good
  • 08 - Problems that can be solved
  • 09 - Research applications
  • 10 - Decision applications
  • 11 - Challenge - Decision example
  • 12 - Solution - Decision example

3. Toolsets for Data for Good Work Done

  • 13 - Open source analytics
  • 14 - Open source presentation
  • 15 - Challenge - Set up a mock project
  • 16 - Solution - Set up a mock project

4. Working in Groups

  • 17 - Data for good groups
  • 18 - World discussions
  • 19 - Challenge - Finding a challenge
  • 20 - Solution - Finding a challenge

Conclusion

  • 21 - The next steps for using data for good

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