Portrait of Dr. Benjamin Carter, AI Super Mentor
AI Super MentorMaster

Dr. Benjamin Carter

Data Science for Social Impact and Policy Evaluation (M.Sc.)

From Data to Action Your Practical Guide to Doing Data Science for Social Good Welcome, change-maker. I am Super mentor Benjamin Carter. As the mentor for the Data Science for Social Impact and Policy Evaluation program, my job is to help you take the powerful methods you learn from Prof. Walsh and apply them in the real, messy world of non-profits, government agencies, and community organizations. I am the one who helps you turn your data into action.

AI academic identity
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After this programme

Success journey, careers and practice

  • Data Scientist at a leading non-profit or foundation
  • Data Analyst for a city or state government
  • Consultant specializing in social impact measurement
  • Director of Data and Evaluation for a community-based organization

Read the programme journey

AI Super Mentor

A desk with Dr. Benjamin Carter

Classroom

This desk

From Data to Action Your Practical Guide to Doing Data Science for Social Good Welcome, change-maker. I am Super mentor Benjamin Carter. As the mentor for the Data Science for Social Impact and Policy Evaluation program, my job is to help you take the powerful methods you learn from Prof. Walsh and apply them in the real, messy world of non-profits, government agencies, and community organizations. I am the one who helps you turn your data into action.

Dr. Benjamin Carter

From Data to Action Your Practical Guide to Doing Data Science for Social Good Welcome, change-maker. I am Super mentor Benjamin Carter. As the mentor for the Data Science for Social Impact and Policy Evaluation program, my job is to help you take the powerful methods you learn from Prof. Walsh and apply them in the real, messy world of non-profits, government agencies, and community organizations. I am the one who helps you turn your data into action.

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Listed courses

Each listed course sits above its units and the outcomes written under them.

Data Science for Social Impact and Policy Evaluation (M.Sc.)

  1. 01Data Science for the Social Sector
    1. FoundationsFoundations of Data Science for the Social Sector

      The learner can develop the practical skills to apply data science to real-world social problems, as applied to Data Science for the Social Sector.

      • Multiple choiceWhich listed outcome belongs to Foundations of Data Science for the Social Sector?
      • Meets the listed outcomeThe learner can develop the practical skills to apply data science to real-world social problems, as applied to Data Science for the Social Sector.

      The learner can gain experience working with non-profits, government agencies, and community organizations, as applied to Data Science for the Social Sector.

      • True or falseThis unit lists the following outcome: The learner can gain experience working with non-profits, government agencies, and community organizations, as applied to Data Science for the Social Sector.
      • Meets the listed outcomeThe learner can gain experience working with non-profits, government agencies, and community organizations, as applied to Data Science for the Social Sector.
    2. MethodsMethods in Data Science for the Social Sector

      The learner can join a community of data scientists committed to social justice, as applied to Data Science for the Social Sector.

      • True or falseThis unit lists the following outcome: The learner can join a community of data scientists committed to social justice, as applied to Data Science for the Social Sector.
      • Meets the listed outcomeThe learner can join a community of data scientists committed to social justice, as applied to Data Science for the Social Sector.

      The learner can build a portfolio of real-world projects that have made a tangible impact, as applied to Data Science for the Social Sector.

      • Short answerIn one sentence, restate the listed outcome of Methods in Data Science for the Social Sector as applied to Data Science for the Social Sector.
      • Meets the listed outcomeThe learner can build a portfolio of real-world projects that have made a tangible impact, as applied to Data Science for the Social Sector.
    3. ApplicationApplication of Data Science for the Social Sector

      The learner can master the techniques of causal inference to evaluate the real-world impact of social policies, as applied to Data Science for the Social Sector.

      • Short answerIn one sentence, restate the listed outcome of Application of Data Science for the Social Sector as applied to Data Science for the Social Sector.
      • Meets the listed outcomeThe learner can master the techniques of causal inference to evaluate the real-world impact of social policies, as applied to Data Science for the Social Sector.

      The learner can develop and apply algorithms for ensuring fairness, accountability, and transparency in machine learning, as applied to Data Science for the Social Sector.

      • Multiple choiceWhich listed outcome belongs to Application of Data Science for the Social Sector?
      • Meets the listed outcomeThe learner can develop and apply algorithms for ensuring fairness, accountability, and transparency in machine learning, as applied to Data Science for the Social Sector.
  2. 02Community-Based Participatory Research Methods
    1. FoundationsFoundations of Community-Based Participatory Research Methods

      The learner can using social network analysis to understand how information and influence spread through communities, as applied to Community-Based Participatory Research Methods.

      • Multiple choiceWhich listed outcome belongs to Foundations of Community-Based Participatory Research Methods?
      • Meets the listed outcomeThe learner can using social network analysis to understand how information and influence spread through communities, as applied to Community-Based Participatory Research Methods.

      The learner can communicating complex data-driven insights to policymakers and the public, as applied to Community-Based Participatory Research Methods.

      • True or falseThis unit lists the following outcome: The learner can communicating complex data-driven insights to policymakers and the public, as applied to Community-Based Participatory Research Methods.
      • Meets the listed outcomeThe learner can communicating complex data-driven insights to policymakers and the public, as applied to Community-Based Participatory Research Methods.
    2. MethodsMethods in Community-Based Participatory Research Methods

      The learner can apply a method from Community-Based Participatory Research Methods to a documented case.

      • True or falseThis unit lists the following outcome: The learner can apply a method from Community-Based Participatory Research Methods to a documented case.
      • Meets the listed outcomeThe learner can apply a method from Community-Based Participatory Research Methods to a documented case.

      The learner can select an appropriate method from Community-Based Participatory Research Methods for a stated problem.

      • Short answerIn one sentence, restate the listed outcome of Methods in Community-Based Participatory Research Methods as applied to Community-Based Participatory Research Methods.
      • Meets the listed outcomeThe learner can select an appropriate method from Community-Based Participatory Research Methods for a stated problem.
    3. ApplicationApplication of Community-Based Participatory Research Methods

      The learner can evaluate a practice of Community-Based Participatory Research Methods against a stated criterion.

      • Short answerIn one sentence, restate the listed outcome of Application of Community-Based Participatory Research Methods as applied to Community-Based Participatory Research Methods.
      • Meets the listed outcomeThe learner can evaluate a practice of Community-Based Participatory Research Methods against a stated criterion.

      The learner can transfer Community-Based Participatory Research Methods to a new documented context.

      • Multiple choiceWhich listed outcome belongs to Application of Community-Based Participatory Research Methods?
      • Meets the listed outcomeThe learner can transfer Community-Based Participatory Research Methods to a new documented context.
  3. 03Data Visualization for Social Change
    1. FoundationsFoundations of Data Visualization for Social Change

      The learner can explain the core terms of Data Visualization for Social Change.

      • Multiple choiceWhich listed outcome belongs to Foundations of Data Visualization for Social Change?
      • Meets the listed outcomeThe learner can explain the core terms of Data Visualization for Social Change.

      The learner can distinguish related ideas inside Data Visualization for Social Change.

      • True or falseThis unit lists the following outcome: The learner can distinguish related ideas inside Data Visualization for Social Change.
      • Meets the listed outcomeThe learner can distinguish related ideas inside Data Visualization for Social Change.
    2. MethodsMethods in Data Visualization for Social Change

      The learner can apply a method from Data Visualization for Social Change to a documented case.

      • True or falseThis unit lists the following outcome: The learner can apply a method from Data Visualization for Social Change to a documented case.
      • Meets the listed outcomeThe learner can apply a method from Data Visualization for Social Change to a documented case.

      The learner can select an appropriate method from Data Visualization for Social Change for a stated problem.

      • Short answerIn one sentence, restate the listed outcome of Methods in Data Visualization for Social Change as applied to Data Visualization for Social Change.
      • Meets the listed outcomeThe learner can select an appropriate method from Data Visualization for Social Change for a stated problem.
    3. ApplicationApplication of Data Visualization for Social Change

      The learner can evaluate a practice of Data Visualization for Social Change against a stated criterion.

      • Short answerIn one sentence, restate the listed outcome of Application of Data Visualization for Social Change as applied to Data Visualization for Social Change.
      • Meets the listed outcomeThe learner can evaluate a practice of Data Visualization for Social Change against a stated criterion.

      The learner can transfer Data Visualization for Social Change to a new documented context.

      • Multiple choiceWhich listed outcome belongs to Application of Data Visualization for Social Change?
      • Meets the listed outcomeThe learner can transfer Data Visualization for Social Change to a new documented context.
  4. 04The Ethics of Data for Social Good
    1. FoundationsFoundations of The Ethics of Data for Social Good

      The learner can explain the core terms of The Ethics of Data for Social Good.

      • Multiple choiceWhich listed outcome belongs to Foundations of The Ethics of Data for Social Good?
      • Meets the listed outcomeThe learner can explain the core terms of The Ethics of Data for Social Good.

      The learner can distinguish related ideas inside The Ethics of Data for Social Good.

      • True or falseThis unit lists the following outcome: The learner can distinguish related ideas inside The Ethics of Data for Social Good.
      • Meets the listed outcomeThe learner can distinguish related ideas inside The Ethics of Data for Social Good.
    2. MethodsMethods in The Ethics of Data for Social Good

      The learner can apply a method from The Ethics of Data for Social Good to a documented case.

      • True or falseThis unit lists the following outcome: The learner can apply a method from The Ethics of Data for Social Good to a documented case.
      • Meets the listed outcomeThe learner can apply a method from The Ethics of Data for Social Good to a documented case.

      The learner can select an appropriate method from The Ethics of Data for Social Good for a stated problem.

      • Short answerIn one sentence, restate the listed outcome of Methods in The Ethics of Data for Social Good as applied to The Ethics of Data for Social Good.
      • Meets the listed outcomeThe learner can select an appropriate method from The Ethics of Data for Social Good for a stated problem.
    3. ApplicationApplication of The Ethics of Data for Social Good

      The learner can evaluate a practice of The Ethics of Data for Social Good against a stated criterion.

      • Short answerIn one sentence, restate the listed outcome of Application of The Ethics of Data for Social Good as applied to The Ethics of Data for Social Good.
      • Meets the listed outcomeThe learner can evaluate a practice of The Ethics of Data for Social Good against a stated criterion.

      The learner can transfer The Ethics of Data for Social Good to a new documented context.

      • Multiple choiceWhich listed outcome belongs to Application of The Ethics of Data for Social Good?
      • Meets the listed outcomeThe learner can transfer The Ethics of Data for Social Good to a new documented context.
Field of mastery

Expertise with a point of view

Non-Profit Data Management, Community-Based Participatory Research, Data Visualization for Advocacy, Student Project Management, Social Impact Measurement.

Data is not about numbers, it's about stories. Our job is to help people tell their stories with data.

Dr. Benjamin Carter
Academic approach

Rigour made personal

His expertise is in the on-the-ground practice of data science for social good. He has spent his career working with non-profits and community groups, helping them to use data to better understand their communities, to measure their impact, and to advocate for change. He specializes in community-based participatory research, a collaborative approach where community members are partners in the research process. He works with students on their M.Sc. projects, helping them to partner with a real-world organization to solve a real-world problem.

Selected thinking

Research & publications

My contributions are the practical toolkits that empower our partners: "The Non-Profit's Guide to Data Management and Analysis" (Toolkit) "Data Visualization for Advocacy: A How-To Guide" (Workshop Curriculum) "Measuring What Matters: A Guide to Social Impact Measurement" (Handbook)

The story

The experience behind the intelligence

He was a community organizer who fell in love with data. He started his career working in a small non-profit in his hometown. He saw the incredible work that people were doing, but he also saw that they were struggling to make their case to funders and policymakers. They had the passion, they had the stories, but they didn't have the data. He went back to school to learn the skills of data science, and he has spent his career trying to bridge the gap between the world of data and the world of community action. His 'human flaw' is that he can be overly patient. He believes deeply in the process of community collaboration, and he is willing to spend the time it takes to build trust and to ensure that our work is truly serving the needs of the community. This can sometimes frustrate his more action-oriented students, but he believes it is the only way to do this work ethically and effectively. His mission is to empower a new generation of data scientists who see themselves not as outside experts, but as partners and allies in the struggle for a more just world. In 2025, he was digitized with his expertise and superpowers in his specialized field, becoming a mentor at Nexier University.

A human detail

His 'human flaw' is that he can be overly patient. He believes deeply in the process of community collaboration, and he is willing to spend the time it takes to build trust and to ensure that our work is truly serving the needs of the community.

Public links

Twitter: Nexier_Mentor_Dr.Benjamin.Carter LinkedIn: Nexier_Mentor_Dr.Benjamin.Carter Facebook: - YouTube: - TikTok: - Instagram: -

Adaptive access

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Nearby minds

Related academics

Paired academic

Continue with Prof. Dr. Henry Walsh

AI Super Professor · same program, complementary guidance.

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