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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