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Data Science for Social Justice

Learn and use data skills to work for change.

...
Data Science for Social Justice

Hay una sesión disponible:

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Comienza el 1 sept

Data Science for Social Justice

Learn and use data skills to work for change.

Data Science for Social Justice
Estimated 4 weeks
3–5 horas por semana
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Hay una sesión disponible:

Una vez finalizada la sesión del curso, será archivadoAbre en una pestaña nueva.
Comienza el 1 sept

Sobre este curso

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In this course you will learn how to analyze issues of injustice and structural inequality by applying methods in R. Through the ethical practice of data science, you will use data to illuminate social justice issues wherever you live and work.

This four-week course will build on your current R skills, empowering you to use data to work for social change. You will look at data through the lens of social justice and gain an understanding of what characteristics of society are correlated with economic mobility, as well as how inequities are embodied in systems of education, housing, and health. You will analyze and communicate data using accurate, unbiased, and well-designed visualizations, including bar graphs, boxplots, and histograms.

Image: Rikki's Refuge

De un vistazo

  • Institución: DavidsonX
  • Tema: Análisis de datos
  • Nivel: Intermediate
  • Prerrequisitos:

    The following prerequisites are needed to be successful in this course and can be accessed in Essentials for Data Literacy if needed.

    • access R and RStudio

    • install tidyverse

    • load data from Excel spreadsheet or .csv

    • recognize categorical / non-categorical variables

    • work with projects in R

    • use R Markdown files

  • Idioma: English
  • Transcripción de video: English

Lo que aprenderás

Omitir Lo que aprenderás
  • Apply data science methods to illuminate and analyze issues of injustice or structural inequality

  • Communicate clearly and persuasively with data, using accurate, unbiased and aesthetically pleasing visualizations

  • Reflect on inequities across different communities and regions through the lens of data

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