Build fairer, more inclusive organisations โ principles and practice.
Data Science & Analytics
Module-by-module breakdown of Diversity, Equity, and Inclusion (DEI), from foundations to a certified capstone project.
Concepts
โข Diversity as composition, equity as process, inclusion as experience
โข Equality against equity and the different interventions each implies
โข Why representation alone predicts very little about retention
Bias
โข Unconscious bias, the IAT and its weak individual predictive validity
โข Why one-off bias training shows little durable effect, and can provoke backlash
โข Structural and process interventions that outperform awareness alone
Process
โข Structured interviews, scoring rubrics and blind review in hiring
โข Promotion, pay review and work allocation as the higher-leverage points
โข Meeting practice, credit attribution and psychological safety
Measurement
โข Representation, retention and progression tracked by stage, not in aggregate
โข Pay gap analysis, adjusted and unadjusted, and how each is misused
โข Survey design, small-group anonymity and the limits of engagement scores
Change
โข Accountability held by managers rather than delegated to a DEI office
โข Legal boundaries around positive action and quotas in different jurisdictions
โข Resistance, fatigue and communicating the case without overclaiming
e-Certificate and e-Marksheet issued on successful completion.