DataedX

CONTINUING EDUCATION FOR DATA PROFESSIONALS AND PRACTITIONERS

Making any product or service today means knowing how to harness your data. But leveraging data so that it minimizes harms is way outside your expertise. You didn't learn this in the classroom and don't know where to start in the workplace. 

 

At DataedX, we take you from data overwhelm to data clarity. We assist professionals and practitioners on integrating equity in their data practices.

Hear more about what is data equity, consequences of algorithmic-based harms and the critical importance of humanizing data systems. 

Your Data Card

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EXPLORE.

Discover ways to learn about the data industry: programs, careers and resources.

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UPSKILL.

Enhance or refresh data skills to help minimize tech's harms in your current role. 

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PIVOT.

Become a data equity professional in order to identify and combat algorithmic-based harms. 

WHAT PEOPLE SAY

"I really enjoyed this afternoon’s discussion.  Thank you to all the panelists and behind the scenes team for everything you did to make today’s Inclusion in Data event a success!"

 

Rocky Hunter

Senior Vice President

Enterprise Data Office

Truist

Image by Jason Goodman

PAST
CLIENTS

 ACLU

Columbia Data Science Institute

Filene Research Institute

Harvard Digital Initiative + Gender Initiative

Kapor Center

Stanford PACS

National Association for Multi-Ethnicity in Communications

National Urban League

Twin Cities Innovation Alliance

Truist

Twin Cities Innovation Alliance

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Audience and Lecturer

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DataedX's Resource Picks

BGC-ColoringBook_edited

Black Girls CODE the Future Coloring Book by Nia Asemota

TheTechThatComesNext

The Tech That Comes Next: How Changemakers, Philanthropists, and Technologists Create An Equitable World by Amy Sample Ward and Afua Bruce

SQLforDataScientists

SQL for Data Scientists: A Beginner's Guide for Building Datasets for Analysis by Renee M. P. Teate

humancentereddatascience

Human-Centered Data Science An Introduction By Cecilia Aragon, Shion Guha, Marina Kogan, Michael Muller and Gina Neff

DigitalBlackFeminism

Digital Black Feminism by Catherine Knight Steele

YouAreADataPerson

You Are a Data Person Strategies for Using Analytics on Campus by Amelia Parnell

Getting Started in Data Science

Getting Started in Data Science by Ayodele Odubela

Race After Technology: Abolitionist Tools for the New Jim Code

Race After Technology: Abolitionist Tools for the New Jim Code by Ruha Benjamin

Algorithms of Oppression How Search Engines Reinforce Racism

Algorithms of Oppression How Search Engines Reinforce Racism by Safiya Umoja Noble

Bite-Size Python: An Introduction to Python Programming

Bite-Size Python: An Introduction to Python Programming by April Speight

Career Rehab

Career Rehab by Kanika Tolver

Shifting The Double Lives of Black Women in America

Shifting The Double Lives of Black Women in America by Charisse Jones and Kumea Shorter-Gooden

Advanced Analytics in Power BI with R and Python Ingesting, Transforming, Visualizing

Advanced Analytics in Power BI with R and Python Ingesting, Transforming, Visualizing by Ryan Wade

Transfer Learning for Natural Learning Processing

Transfer Learning for Natural Learning Processing by Paul Azunre

Bayes Rules!

Bayes Rules! An Introduction to Bayesian Modeling with R by Alicia A. Johnson, Miles Ott, Mine Dogucu

Design Justice

Design Justice: Community-Led Practices to Build the Worlds We Need by Sasha Costanza-Chock

An Introduction to Statistical Learning

An Introduction to Statistical Learning with Applications in R by Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani

Trust in Machine Learning

Trust in Machine Learning by Kush Varshney

Deep Learning for Beginners

Deep Learning for Beginners by Dr. Pablo Rivas

Data Ethics Framework

Data Ethics Framework by UK Government

164 Data Science Interview Questions & Answers

164 Data Science Interview Questions & Answers by 365DataScience

Weapons of Math Destruction: How Big Data increases inequality and threatens democracy

Weapons of Math Destruction: How Big Data increases inequality and threatens democracy by Cathy O'Neil

Who Gets What — and Why: The New Economics of Matchmaking and Market Design

Who Gets What — and Why: The New Economics of Matchmaking and Market Design by Alvin E. Roth

Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor

Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor by Virginia Eubanks