The repository consists of a dataset with curated links to material dealing with statistics and data. There is a total of 1925 active links in the dataset. The 291 awesome/recommended links in the dataset are listed below. Feel free to add additional links to the dataset.
- Positron vs RStudio - is it time to switch?
- xkcd and Data Science
- TIL: dplyr::mutate()’s .keep argument
- Tips for data entry in Excel
- The brms Book: Applied Bayesian Regression Modelling Using R and Stan
- The Polars vs pandas difference nobody is talking about
- Parameterized plots and reports with R and Quarto
- Python for R users
- Understanding Gaussians
- Comparing data.table reshape to duckdb and polars
- Nested unit tests with testthat
- Visual Diagnostic Tools for Causal Inference
- Using property-based testing in R
- The Data Visualisation Catalogue
- Generalized Additive Models (GAMs) for Meta-Regression using brms
- Statistics Done Wrong: The woefully complete guide
- Transformations: an introduction
- Seven deadly sins of contemporary quantitative political analysis
- Reference Collection to push back against “Common Statistical Myths”
- Introduction to Modern Statistics
- Philosophy of Statistics
- An Introduction to Statistical Learning
- Wizard’s Guide to Statistics
- Everything is a Linear Model
- Statistics Minus The Math: An Introduction for the Social Sciences
- What Does Probability Mean in Your Profession?
- Seeing Theory: A visual introduction to probability and statistics
- The Only Probability Cheatsheet You’ll Ever Need
- How to Get Better at Embracing Unknowns
- The 9 concepts and formulas in probability that every data scientist should know
- Explaining base rate neglect
- Introduction to Probability for Data Science
- Causal Inference: What If (the book)
- Causal design patterns for data analysts
- The Effect: An Introduction to Research Design and Causality
- Papers about Causal Inference and Language
- Quantifying causality in data science with quasi-experiments
- Statistical Control Requires Causal Justification
- Gov 2003: Causal Inference with Applications
- STAT 286/GOV 2003: Causal Inference with Applications
- A First Course in Causal Inference
- DS-GA 3001.009: Special Topics in Data Science: Responsible Data Science
- Fairness and machine learning: Limitations and Opportunities
- When Is Anonymous Not Really Anonymous?
- 11 Short Machine Learning Ethics Videos
- Ethical Principles for Web Machine Learning
- Reflexivity in Quantitative Research: a Rationale and Beginner’s Guide
- Misinterpretations of Significance: A Problem Students Share with Their Teachers?
- The Null Ritual: What You Always Wanted to Know About Significance Testing but Were Afraid to Ask
- The New Statistics: Why and How
- Statistical significance and its part in science downfalls
- Common Misconceptions about Data Analysis and Statistics
- Understanding Statistical Power and Significance Testing
- Multiple Hypothesis Testing
- Statistical tests, P values, confidence intervals, and power: a guide to misinterpretations
- Understanding common misconceptions about p-values
- The Permutation Test: A Visual Explanation of Statistical Testing
- Hypothesis test by hand
- Improving Your Statistical Inferences
- The trouble with viral maps. Plus: Some fun viral maps!
- What to consider when creating choropleth maps
- Six quick tips to improve your regression modeling
- How to create confounders with regression: a lesson from causal inference
- Thinking Clearly About Correlations and Causation: Graphical Causal Models for Observational Data
- Common statistical tests are linear models (or: how to teach stats)
- The Truth About Linear Regression
- Least squares as springs
- A Crash Course in Good and Bad Controls
- Regression and Other Stories
- Linear Regression
- Logistic Regression
- Linear Regression
- Logistic regression is not fucked
- A User’s Guide to Statistical Inference and Regression
- Bayesian Inference: an interactive visualization
- Understanding Bayes: Evidence vs. Conclusions
- A Bayesian Model to Calculate Whether My Wife is Pregnant or Not
- Data science terminology
- Webcast: Machine Learning and Econometrics
- ML beyond Curve Fitting: An Intro to Causal Inference and do-Calculus
- A visual introduction to machine learning
- Interpretable Machine Learning: A Guide for Making Black Box Models Explainable
- What is machine learning, and how does it work?
- Machine learning, explained
- How to avoid machine learning pitfalls: a guide for academic researchers
- Neural Networks and Deep Learning
- Visual explanations of core machine learning concepts
- Deep Neural Nets: 33 years ago and 33 years from now
- Machine Learning FAQ
- An Explorable Explainer of K-Means Clustering
- Failed Machine Learning (FML)
- Random Forests for Complete Beginners
- Probabilistic Machine Learning: Advanced Topics
- On Moving from Statistics to Machine Learning, the Final Stage of Grief
- The Illustrated Machine Learning website
- A Course in Machine Learning
- Deep Learning
- CS388: Natural Language Processing
- Survey Experiments in Practice
- 10 Things to Know About Survey Experiments
- I saw your RCT and I have some worries! FAQs
- Plots that Changed the World
- The statisticians at Fox News use classic and novel graphical techniques to lead with data
- The top ten worst graphs
- Data visualization: a reading list
- Data Visualization in Sociology
- An Economist’s Guide to Visualizing Data
- Beyond Bar and Line Graphs: Time for a New Data Presentation Paradigm
- Explained Visually
- Real Chart Rules to Follow
- How Histograms Work
- What’s so hard about histograms?
- Data Viz Project
- Fundamentals of Data Visualization
- Visualizing Outliers
- Why not to use two axes, and what to use instead
- From Data to Viz
- A biased tour of the uncertainty visualization zoo
- Mistakes, we’ve drawn a few
- Twenty rules for good graphics
- Your Friendly Guide to Colors in Data Visualisation
- How to pick more beautiful colors for your data visualizations
- How maps in the media make us more negative about migrants
- Hands-On Data Visualization: Interactive Storytelling from Spreadsheets to Code
- Visual Vocabulary
- Why you sometimes need to break the rules in data viz
- In defense of simple charts
- Dataviz Accessibility Resources
- Psyc 6135: Psychology of Data Visualization
- 10 ways to use fewer colors in your data visualizations
- A detailed guide to colors in data vis style guides
- Stop aggregating away the signal in your data
- Dataviz Inspiration
- Financial Times Visual Vocabulary
- What background color should your data vis have?
- Are Vertical Line Charts Ever a Good Idea?
- Friends Don’t Let Friends Make Bad Graphs
- Which fonts to use for your charts and tables
- What to consider when using text in data visualizations
- Beautiful Public Data
- Easy Graph Mistakes to Avoid
- Emphasize what you want readers to see with color
- How to design a useful (and fun!) color key for your data visualization
- The Data Visualisation Catalogue
- What data can and cannot do
- Beware the Big Errors of ‘Big Data’
- A short list of online articles and references on data journalism
- How to share data with a statistician
- The Quartz guide to bad data
- Data Organization in Spreadsheets
- Data: Sharing Is Caring
- Big data: A big mistake?
- Tidy Data Rulebook
- Critical Dataset Studies Reading List
- A curated list of awesome posts, videos, and articles on leading a data team (small and large)
- List of tools and techniques for working with relational databases
- Listwise Deletion: It’s NOT Evil
- Using causal graphs to understand missingness and how to deal with it
- Visualizing Incomplete and Missing Data
- The Most Dangerous Equation
- The flawed logic of chasing large effects with small samples
- Sample Size Justification
- PolData: A dataset with political datasets
- English and European soccer results 1871-2020
- Awesome Public Datasets
- Software development skills for data scientists
- Ten Simple Rules for Effective Statistical Practice
- testing statistical software
- Coding habits for data scientists
- The Statistics Software Signal
- Awesome official statistics software
- Learning R for Researchers in Psychology
- R-Uni (A List of 100 Free R Tutorials and Resources in University webpages)
- Google’s R Style Guide
- A collection of datasets originally distributed in various R packages
- Getting Started with Charts in R
- A Guide to Getting International Statistics into R
- Data wrangling, exploration, and analysis with R
- Tufte in R
- RWeekly.org: Blogs to Learn R from the Community
- Efficient R programming
- Happy Git and GitHub for the useR
- Data Visualization: A practical introduction
- Hands-On Programming with R
- Introduction to Econometrics with R
- Learning Statistics with R
- How the BBC Visual and Data Journalism team works with graphics in R
- Hands-On Machine Learning with R
- Mastering Shiny
- Doing Meta-Analysis in R: A Hands-on Guide
- A ggplot2 Tutorial for Beautiful Plotting in R
- R Cookbook, 2nd Edition
- Data Science: A First Introduction
- Outstanding User Interfaces with Shiny
- Awesome R Learning Resources
- Bayes Rules! An Introduction to Applied Bayesian Modeling
- Crime by the Numbers: A Criminologist’s Guide to R
- Modern Data Science with R
- Lightweight Machine Learning Classics with R
- Modern Statistics with R
- Bayes Rules! An Introduction to Applied Bayesian Modeling
- Handbook of Regression Modeling in People Analytics
- R Without Statistics
- Supervised Machine Learning for Text Analysis in R
- Introduction to Data Science: Data Wrangling and Visualization with R
- Tidy Finance with R
- Regression Modeling Strategies
- Introduction to Multilevel Modelling
- Text Analysis Using R
- Deep R Programming
- Telling Stories with Data: With applications in R
- Deep Learning and Scientific Computing with R torch
- Lessons Learned From Running R in Production
- Tidy design principles
- Mixed Models with R
- Yet Again: R + Data Science
- Engineering Production-Grade Shiny Apps
- Overview of R Modelling Packages
- Exploring Data Science with R and the Tidyverse: A Concise Introduction
- Modern Data Visualization with R
- Feature Engineering A-Z
- An Introduction to R
- Causal Inference in R
- R in Production
- The brms Book: Applied Bayesian Regression Modelling Using R and Stan
- A Byte of Python
- The Python Graph Gallery
- Quantitative Economics with Python
- Another Book on Data Science: Learn R and Python in Parallel
- NumPy: the absolute basics for beginners
- Notes On Using Data Science & Machine Learning To Fight For Something That Matters
- Computational and Inferential Thinking: The Foundations of Data Science
- Data science Python notebooks
- Full Stack Python
- Introduction to Deep Learning – 170 Video Lectures from Adaptive Linear Neurons to Zero-shot Classification with Transformers
- A Concrete Introduction to Probability (using Python)
- Introduction to Python for Social Science
- Spatial Data Programming with Python
- Causal Inference for The Brave and True
- Getting started with NLP for absolute beginners
- Python for Data Analysis
- Coding for Economists
- Minimalist Data Wrangling with Python
- Pandas Illustrated: The Definitive Visual Guide to Pandas
- Google Python Style Guide
- Introduction to Statistical Learning with Applications in Python
- Practical Python Programming
- Advanced Python Mastery
- Computational Methods for Economists using Python
- Introduction to Computational Thinking
- Data Science in Julia for Hackers
- Geospatial Data Science with Julia
- A foundation in Julia
- Working with data in Julia
- Plotting data in Julia
- Romeo and Julia, where Romeo is Basic Statistics
- Stata Tutorial
- Tips for using Stata
- Dictionary: Stata to R
- List of resources for the Stata commands ‘margins’ and ‘marginsplot’
- Uncluttered Stata Graphs
- The Stata workflow guide
- The Stata Guide
- Stata-schemes
- Quick Stata Tips
- Spreadsheet mistakes - news stories
- 333 Excel Shortcuts for Windows and Mac
- Excel VBA Introduction
- 101 Excel Functions you should know
- It’s Too Hard to Publish Criticisms and Obtain Data for Replication
- Reproducing Statistical Results
- Sluggish data sharing hampers reproducibility effort
- Self Evaluation for Reproducible Science
- Science Isn’t Broken: It’s just a hell of a lot harder than we give it credit for
- A reading list for the Replicability Crisis
- Seven steps toward more transparency in statistical practice
- Do you know where your survey data come from?
- How to Run Surveys: A Guide to Creating Your Own Identifying Variation and Revealing
- Likert-Type Scale Response Anchors
