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Data Science Reference Library
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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.
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.
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