RESOURCES
Explore the Power of People Analytics
My friend and former colleague Nicholas Garbis & I came together to bring you "Explore The Power of People Analytics: A Guide for Business and HR Leaders." Available in print for sale (button below) or as a free eBook (https://futureworkplace.com/ebooks/people-analytics/).
People Analytics Career Starter Guide
This guide was created for those who are just starting out or early in applying people analytics to their career. The guide is designed equally for those interested in a dedicated people analytics career as well as those who are interested in business, leadership, human resources, or human capital related careers who want to incorporate more analytics about and for people.
The guide is being published in parts. Sign up for my newsletter to hear when each new section launches.
Heather's Recommended Readings
Heather's idea of a perfect bookshelf:
- Criado Perez, C. (2019) Invisible Women: Data bias in a world designed for men.
- Rosling, H., Rosling, O., & Rönnlund, A. R. (2018). Factfulness: Ten reasons we’re wrong about the world - and why things are better than you think.
- Rosenthal, C. (2018). Accounting for Slavery: Masters and management.
- Wheelan, C. (2013) Naked Statistics: Stripping the dread from the data.
- Merry, S. E. (2016) The Seductions of Quantification: Measuring human rights, gender violence, and sex trafficking.
- Nelson, D. M. (2015) Who Counts? The mathematics of death and life after genocide.
- Best, J. (2012) Damned lies and statistics: Untangling numbers from the media, politicians, and activists.
- D'Ignazio, C. & Klein, L. F. (2020). Data Feminism.
- O’Neil, C. (2016) Weapons of Math Destruction: How big data increases inequality and threatens democracy.
- Eubanks, V. (2017) Automating Inequality: How high-tech tools profile, police, and punish the poor.
- Wachter-Boettcher, S. (2018). Technically Wrong: Sexist Apps, Biased Algorithms, and Other Threats of Toxic Tech.
- Noble, S. U. (2018). Algorithms of Oppression: How Search Engines Reinforce Racism.
- Benjamin, R. (2019). Race After Technology: Abolitionist Tools for the New Jim Code.
- D’Ignazio, C. & Klein, L. (2020). Data Feminism. A new way of thinking about data science and data ethics that is informed by the ideas of intersectional feminism.