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Data Science Reference Library
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Intro to Vector databases. Dagster vs. Airflow. Fast, flexible forecasting. Decision-driven. Visualization by example. Good/Bad data scientist.
Thinking in data. Spreadsheet munging strategies. Sports analytics w/ AI. Ethics and ML licensing. Cluster analysis viz.
Practical SQL for data analyis. How query-matching works. Metrics at scale. Notebooks evolved: reactive, reproducible, collaborative.
The Scaling Data Framework. Winning the KY Derby. Metadata analysis vs AI. Computational thinking. Art of mathematics. Missing layer for the modern data stack?
DataOps. Hire for weaknesses. CLI for Jupyter. Scikit-learn course. Selecting algorithms for time-series forecasting.
Working w/ data > RAM. Algorithmic bias vs data bias. Exploring network behavior. Keeping up w/ NLP. ggplot2 Geoms. Secure analytics.
Data monitoring at scale. Model work vs data work. Beyond PCA. Date-time handling for R. Finding signals in the noise.
Testing notebooks. Intro to PCA. Building an online ad platform. Ghosts in the data. Next-gen apps w/ GPT-3. Platforms vs PhDs.
Effective data monitoring. Scaling with First Principles. Working with color scales. The Data Journalism Handbook.
Data pricing. Hands-off anomaly detection. Defensible machine learning. How to write design docs. Understanding instrumental variables estimation. Pattern-based spatial analysis.
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