DataedX
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
EXPLORE.
Discover ways to learn about the data industry: programs, careers and resources.
UPSKILL.
Enhance or refresh data skills to help minimize tech's harms in your current role.
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
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
Visa
UPGRADE YOUR DATA CARD.
DataedX's Resource Picks
 Black Girls CODE the Future Coloring Book by Nia Asemota |  The Tech That Comes Next: How Changemakers, Philanthropists, and Technologists Create An Equitable World by Amy Sample Ward and Afua Bruce |  SQL for Data Scientists: A Beginner's Guide for Building Datasets for Analysis by Renee M. P. Teate |  Human-Centered Data Science An Introduction By Cecilia Aragon, Shion Guha, Marina Kogan, Michael Muller and Gina Neff |
|---|---|---|---|
 Digital Black Feminism by Catherine Knight Steele |  You Are a Data Person Strategies for Using Analytics on Campus by Amelia Parnell |  Getting Started in Data Science by Ayodele Odubela |  Race After Technology: Abolitionist Tools for the New Jim Code by Ruha Benjamin |
 Algorithms of Oppression How Search Engines Reinforce Racism by Safiya Umoja Noble |  Bite-Size Python: An Introduction to Python Programming by April Speight |  Career Rehab by Kanika Tolver |  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 by Ryan Wade |  Transfer Learning for Natural Learning Processing by Paul Azunre |  Bayes Rules! An Introduction to Bayesian Modeling with R by Alicia A. Johnson, Miles Ott, Mine Dogucu |  Design Justice: Community-Led Practices to Build the Worlds We Need by Sasha Costanza-Chock |
 An Introduction to Statistical Learning with Applications in R by Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani |  Trust in Machine Learning by Kush Varshney |  Deep Learning for Beginners by Dr. Pablo Rivas |  Data Ethics Framework by UK Government |
 164 Data Science Interview Questions & Answers by 365DataScience |  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 by Alvin E. Roth |  Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor by Virginia Eubanks |