1647. Data Vis Dispatch: January 3, January 10, January 17, January 24, January 31

1648. The list of 2022 visualization lists

1649. From Documents to Data: A Framework for Total Corpus Quality

1650. Efficient Python for Data Scientists

1651. Deep R Programming

1652. Minimalist Data Wrangling with Python

1653. Everything You Wanted to Know about the Kernel Trick (But Were Too Afraid to Ask)

1654. fastStat: All of REAL Statistics

1655. Big, Open and Linked Data: Effects and Value for the Economy

1656. Introducing geom_terrorbar()

1657. A public repository of personal ML/DS blogs

1658. simple-data-analysis.js: Easy-to-use JavaScript library for most common data analysis tasks

1659. Quantile Regression as an useful Alternative for Ordinary Linear Regression

1660. Winners of the 2022 Table Contest

1661. Combining plots in ggplot2

1662. Prediction Modeling with the Cox model – all about the baseline hazard

1663. Multiple Regression using caret

1664. MLOps: The Whole Game

1665. An intro to dplyr::across

1666. Probabilistic Machine Learning: Advanced Topics

1667. Annotated History of Modern AI and Deep Learning

1668. A list of open geospatial datasets available on AWS, Earth Engine, Planetary Computer, NASA CMR, and STAC Index

1669. Introduction to Graph Machine Learning

1670. A Succinct Summary of Reinforcement Learning

1671. Combining R and Python with {reticulate} and Quarto

1672. A Guide To Getting Data Visualization Right

1673. Level Up Your Python

1674. Exploratory spatial data analysis with Python

1675. 6 tips for creating charts for social media

1676. balance: a python package for balancing biased data samples

1677. A Guide to Analyzing Large *N*, Large *T* Panel Data

1678. On Moving from Statistics to Machine Learning, the Final Stage of Grief

1679. SQL and noSQL approaches to creating & querying databases (using R)

1680. The Illustrated Machine Learning website

1681. An overview of parsing algorithms

1682. Introduction to data structures and algorithms

1683. Understanding Deep Learning

1684. AI4PH: Text Analyses with R

1685. Easily re-using self-written functions: the power of gist + code snippet duo

1686. The Bitter Lesson

1687. Logistic regression is not fucked

1688. These Are Not the Effects You Are Looking For: The Fallacy of Mutual Adjustment and How to Avoid It

1689. Discovering the best Chess960 variation

1690. ggplot tricks

1691. Alone R package: Datasets from the survival TV series

1692. Geospatial distributed processing with furrr

1693. Visualising the 2022 Australian federal election with geom_sugarbag

1694. Time series resources

1695. Notes on Hainmueller, Mummolo & Xu: How Much Should we Trust Estimates from Multiplicative Interaction Models?

1696. Equivalence testing for linear regression

1697. Simpson’s Paradox and Existential Terror

1698. Recreating a chart from history: a beginner’s look in the data vis world

1699. Improving the responsiveness of Shiny applications

1700. Telling Stories with Data: With applications in R

1701. ggpathway: A tutorial for pathway visualization using tidyverse, igraph, and ggraph

1702. Pandas Illustrated: The Definitive Visual Guide to Pandas

1703. Much Ado About Sampling

1704. My journey from R to Julia

1705. Never Test for Normality

1706. Foundations of Data Science

1707. Course Materials for Advanced Data Analytics in Economics

1708. 6 easy ways to map population density in R

1709. Mathematical Logic through Python

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