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Data Science Reference Library
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Data pricing. Hands-off anomaly detection. Defensible machine learning. How to write design docs. Understanding instrumental variables estimation. Pattern-based spatial analysis.
The Modern Analytics Stack. AI trends. Violin plots. Learning path for transformers. The data visualization battleground.
Gartner: top data trends. Quantified Self infrastructure. Tracking data SLAs. Algorithmic audits. Data coalitions. Feature stores - a hierarchy of needs.
Data Mesh 101. Data science podcasts. Differential privacy. Level-up your ggplot2 skills. Autodata tools. AI on the battlefield.
Python surprises. Building the modern data stack. How to build data quality monitors w/ SQL. Supercharging Superset. Fixing the data science talent shortage.
Metadata's evolving role. Machine learning science fiction. Inside a hot-button research paper 🦜. Remote pair programming with R. TinyML fundmentals.
Causal design patterns. Why machine learning is hard to tune. Experimentation guardrails. Python EDA toolkit. COVID-19 modeling lessons. How to cite data sources.
Data monitoring at scale. AI for Good: for REAL? Legal questions for data science. Why business intuition > machine learning. Density plots. Internal tool design.
Data science as Atomic Habit. Elegant SQL w/ R. Bayesian statistics Primer. Julia update: Python challenger? Best-of Python machine learning. Notes from NeurIPS 2020.
Real-time machine learning in practice. Search tool for obscure datasets. Intro to probablistic machine learning. Python data validation.
No spam, ever.