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The technical pay gap. Data Science: Foundations, Challenges, Opportunities. Existential threat of data quality. Machine learning visual explainers.
Software development for data scientists. How random forests really work. Visualizing multicollinearity. Machine learning for conservation.
Trusting your data. How to protect your models. How to hire for DS roles. Horizon charts.
Making data actionable. Using BIG AI models in a startup. From academia to industry. Machine learning validity. Mental models for visualization.
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