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Data Science Reference Library
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Sports analytics reading list. Real estate investing with machine learning. Strategy-analytics. Intro to modern stats. What does a data product manager do, exactly?
Machine learning interviews. Understanding p-values. Applied NLP Thinking. Geospatial data in R. Intro to OpenLineage.
Intro to data meshes. How-to navigate "expert" advice. Personalization approaches. Linear Algebra for machine learning. Reproducible data science. Geospatial analysis.
Scaling metrics. Building effective data science teams. Jupyter in a browser. Command line data science. Visualizing distributions. R for public health.
Automated data wrangling. Flat Data. Analytics Engineering: the quiet revolution. Machine learning platform lessons. Pitfalls of causal insights.
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 machine learning 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?
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