EdX

Data Science for Social Justice (edX)

Data Science for Social Justice (edX)

Learn and use data skills to work for change. 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.

Class Deals by MOOC List - Click here and see EdX's Active Discounts, Deals, and Promo Codes.

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.

What you'll learn

  • 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

Prerequisites:
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

Go to Class
MOOC List is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

Related Courses

Estadística Aplicada a los Negocios (edX) EdX
Galileo University,GalileoX

Estadística Aplicada a los Negocios (edX)

Aprende las principales herramientas y técnicas de la estadística descriptiva y la estadística inferencial para analizar e interpretar datos desde la perspectiva de negocios facilitando la toma de decisiones. Este curso proporciona una introducción al análisis de datos en base a las principales herramientas estadísticas, enfocándose en la estadística descriptiva y la estadística inferencial.

Self Paced
Self-Paced
Analytics for Decision Making (edX) EdX
Babson College

Analytics for Decision Making (edX)

Discover the foundational concepts that support modern data science and learn to analyze various data types and quality to make smart business decisions. Want to know how to avoid bad decisions with data? Making good decisions with data can give you a distinct competitive advantage in business. This statistics and data analysis course will help you understand the fundamental concepts of sound statistical thinking that can be applied in surprisingly wide contexts, sometimes even before there is any data! Key concepts like understanding variation, perceiving relative risk of alternative decisions, and pinpointing sources of variation will be highlighted.

Self Paced
Self-Paced
Data Science and Machine Learning Capstone Project (edX) EdX
IBM

Data Science and Machine Learning Capstone Project (edX)

Create a project that you can use to showcase your Data Science skills to prospective employers. Apply various data science and machine learning techniques to analyze and visualize a data set involving a real life business scenario and build a predictive model. Now that you've taken several courses on data science and machine learning, it’s time to put your learning to work on a data problem involving a real life scenario. Employers really care about how well you can apply your knowledge and skills to solve real world problems, and the work you do in this capstone project will make you stand out in the job market.

Self Paced
Self-Paced
Basics of Statistical Inference and Modelling Using R (edX) EdX
University of Canterbury,UCx

Basics of Statistical Inference and Modelling Using R (edX)

Learn why a statistical method works, how to implement it using R and when to apply it and where to look if the particular statistical method is not applicable in the specific situation. Basics of Statistical Inference and Modelling Using R is part one of the Statistical Analysis in R professional certificate.

Self Paced
Self-Paced
Data Science Tools (edX) EdX
IBM

Data Science Tools (edX)

Learn about the most popular data science tools, including how to use them and what their features are. In this course, you'll learn about Data Science tools like Jupyter Notebooks, RStudio IDE, and Watson Studio. You will learn what each tool is used for, what programming languages they can execute, their features and limitations and how data scientists use these tools today.

Self Paced
Self-Paced
Data Science: R Basics (edX) EdX
HarvardX,Harvard University

Data Science: R Basics (edX)

Build a foundation in R and learn how to wrangle, analyze, and visualize data. This course will introduce you to the basics of R programming. You can better retain R when you learn it to solve a specific problem, so you’ll use a real-world dataset about crime in the United States. You will learn the R skills needed to answer essential questions about differences in crime across the different states.

Self Paced
Self-Paced
Probability and Statistics in Data Science using Python (edX) EdX
University of California, San Diego,UC San DiegoX

Probability and Statistics in Data Science using Python (edX)

Using Python, learn statistical and probabilistic approaches to understand and gain insights from data. The job of a data scientist is to glean knowledge from complex and noisy datasets. Reasoning about uncertainty is inherent in the analysis of noisy data. Probability and Statistics provide the mathematical foundation for such reasoning.

Self Paced
Self-Paced
Data Processing and Analysis with Excel (edX) EdX
Rochester Institute of Technology,RITx

Data Processing and Analysis with Excel (edX)

Learn to use Excel to organize and clean data so it can be manipulated and analyzed. In this course, you will learn how to organize your data within the Microsoft Office Excel software tool. Once organized, we will discuss data cleaning. You will learn how to identify outliers and anomalies in the data, and how to identify and change data-types. Together we will develop a data analysis plan, after which we will apply analysis methods and tools, including exploratory analysis, evaluation of results, and comparison with other findings.

Self Paced
Self-Paced
Statistics and R (edX) EdX
HarvardX,Harvard University

Statistics and R (edX)

An introduction to basic statistical concepts and R programming skills necessary for analyzing data in the life sciences. We will learn the basics of statistical inference in order to understand and compute p-values and confidence intervals, all while analyzing data with R. We provide R programming examples in a way that will help make the connection between concepts and implementation.

Self Paced
Self-Paced
Visualización de Datos y Storytelling (edX) EdX
Tecnológico de Monterrey,TecdeMonterreyX

Visualización de Datos y Storytelling (edX)

Aprende en este curso en línea que es la visualización de datos, sus usos; los elementos que la conforman y la forma de poder utilizarla para el apoyo en la toma de las mejores decisiones para las empresas basadas en el análisis de datos. Digamos que necesitas comprender big data; miles o incluso millones de filas de datos, y tienes poco tiempo para hacerlo. los datos pueden provenir de tu equipo, en cuyo caso tal vez ya estés familiarizado con lo que estás midiendo y de los resultados que se esperan. O puede provenir de otro equipo, o tal vez de varios equipos a la vez, y estar completamente familiarizado.

Self Paced
Self-Paced
Advanced Statistical Inference and Modelling Using R (edX) EdX
University of Canterbury,UCx

Advanced Statistical Inference and Modelling Using R (edX)

Extend your knowledge of linear regression to the situations where the response variable is binary, a count, or categorical as well as to hierarchical experimental set-up. Advanced Statistical Inference and Modelling Using R is part two of the Statistical Analysis in R professional certificate. This course is directed at people who are already familiar with basic linear regression and fundamentals of statistical inference. It extends the knowledge of linear regression to the situations where the response variable is binary, a count, or categorical as well as to hierarchical experimental set-up.

Self Paced
Self-Paced