待整理的资源

[XiangyunHuang]

统计历史

The Life, Letters and Labours of Francis Galton, by Karl Pearson
https://galton.org/pearson/

R 语言

An Introduction to R
https://colinfay.me/intro-to-r/

Outstanding User Interfaces with Shiny
https://unleash-shiny.rinterface.com/welcome.html

Using the flextable R package
https://ardata-fr.github.io/flextable-book/index.html

Big Book of R | R 语言书籍列表
https://www.bigbookofr.com/index.html

Applied Data Skills: Processing & Presenting Data
https://psyteachr.github.io/ads-v1/index.html

Deep R Programming
https://deepr.gagolewski.com/

Building reproducible analytical pipelines with R
https://raps-with-r.dev/

Creating beautiful tables in R with {gt}
https://gt.albert-rapp.de/

数据科学

Modern Data Science with R
https://mdsr-book.github.io/mdsr2e/

数据科学中的 R 语言
https://bookdown.org/wangminjie/R4DS/

A Data Science Pattern Language
https://dspatterns.netlify.app/

Practical R for Mass Communication and Journalism
https://www.machlis.com/R4Journalists/

R 数据分析指南与速查手册
https://bookdown.org/xiao/RAnalysisBook/

Data Analytics: A Small Data Approach
https://dataanalyticsbook.info/

Data Analysis and Visualization with R
https://www.michaelgastner.com/DAVisR2019/

时序分析

预测： 方法与实践--中文翻译书
https://otexts.com/fppcn/

Tidy time series forecasting with fable
https://tidyverts.github.io/tidy-forecasting-principles/

Applied Time Series Analysis for Fisheries and Environmental Sciences
https://atsa-es.github.io/atsa-labs/

Forecasting and Analytics with ADAM
https://openforecast.org/adam/

Tidy Finance with R
https://www.tidy-finance.org/

Data science for economists
https://github.com/uo-ec607/lectures

网络分析

Handbook of Graphs and Networks in People Analytics
https://ona-book.org/

Twitter for R programmers
https://www.t4rstats.com/

时空分析

Modelling Spatiotemporal Processes
https://edzer.github.io/mstp/

sits: Satellite Image Time Series Analysis on Earth Observation Data Cubes
https://e-sensing.github.io/sitsbook/index.html

Intro to GIS and Spatial Analysis
https://mgimond.github.io/Spatial/introGIS.html

Data Analysis and Machine Learning on Earth Observation Data Cubes with Satellite Image Time Series
https://e-sensing.github.io/sitsbook/

GeoBUGS Manual
https://www.mrc-bsu.cam.ac.uk/wp-content/uploads/geobugs12manual.pdf

Geographic Data Science with R: Visualizing and Analyzing Environmental Change
https://bookdown.org/mcwimberly/gdswr-book/

PyGIS - Open Source Spatial Programming & Remote Sensing
https://pygis.io/docs/a_intro.html

Mapping data in R
https://mapping-in-r.netlify.app/

Analyzing 1.1 Billion NYC Taxi and Uber Trips, with a Vengeance
An open-source exploration of the city's neighborhoods, nightlife, airport traffic, and more, through the lens of publicly available taxi and Uber data
https://toddwschneider.com/posts/analyzing-1-1-billion-nyc-taxi-and-uber-trips-with-a-vengeance/

Reverse Engineering Uber and Lyft Surge Pricing in Chicago
https://toddwschneider.com/

A Tale of Twenty-Two Million Citi Bike Rides: Analyzing the NYC Bike Share System
https://toddwschneider.com/posts/a-tale-of-twenty-two-million-citi-bikes-analyzing-the-nyc-bike-share-system/

Intro to R-Spatial for Healthy Places
https://makosak.github.io/Intro2RSpatialMed/index.html

PostGIS
https://slides.com/fxku/postgis/fullscreen

NHH ECS530 2021 course: Spatial data analysis (with R)
https://rsbivand.github.io/ECS530_h21/index.html

ECS530: Spatial data analysis I
https://rsbivand.github.io/ECS530_h21/ECS530_211115.html#Spatial_data

空间统计 时空空间点数据 Shiny 应用 火灾数据
http://www.dicook.org/files/malaysiar/slides#28

课程
https://ds112-lendway.netlify.app/
Data Science skills
地图、动画、交互、获取数据等内容

文本分析

Supervised Machine Learning for Text Analysis in R
https://smltar.com/

医学统计

Medical Statis Notes
https://wxhyihuan.github.io/MedicalStatisNotes/

Data Analysis in Medicine and Health using R
https://bookdown.org/drki_musa/dataanalysis/

人力资源

R for HR: An Introduction to Human Resource Analytics Using R
https://rforhr.com/

机器学习

Explanatory Model Analysis
https://ema.drwhy.ai/

Interpretable Machine Learning
https://christophm.github.io/interpretable-ml-book/

Feature Engineering and Selection: A Practical Approach for Predictive Models
https://bookdown.org/max/FES/

Lightweight Machine Learning Classics with R
https://lmlcr.gagolewski.com/

Mathematics for Machine Learning
https://mml-book.github.io/

Deep Learning Book Chinese Translation
https://exacity.github.io/deeplearningbook-chinese/

Practical Deep Learning
https://course.fast.ai/

SK 官方文档中文版
https://sklearn.apachecn.org/#/

Introduction to Machine Learning Interviews Book
https://huyenchip.com/ml-interviews-book/

程序员如何优雅地挣零花钱
https://howto-make-more-money-easychen.vercel.app/

Algorithms, 4th Edition by Robert Sedgewick and Kevin Wayne
https://algs4.cs.princeton.edu/home/

Docker 从入门到实践
https://yeasy.gitbooks.io/docker_practice/content/

[XiangyunHuang]

书籍

Think Julia: How to Think Like a Computer Scientist
https://benlauwens.github.io/ThinkJulia.jl/latest/book

Statistics with Julia
Fundamentals for Data Science, Machine Learning and Artificial Intelligence
https://www.dbooks.org/statistics-with-julia-1534/read/

Julia Data Science
https://juliadatascience.io/

The Science of Functional Programming
https://github.com/winitzki/sofp

economic networks theory and computation
https://networks.quantecon.org/

Think Python
https://wizardforcel.gitbooks.io/think-python-2e/content/0.html

Statistical Thinking for the 21st Century
https://statsthinking21.github.io/statsthinking21-core-site/
源于 an undergraduate statistics course at Stanford (Psych 10/Stats 60).

https://kateto.net/workshops/sunbelt/sunbelt2021.pdf

Spatial Statistics for Data Science: Theory and Practice with R
https://www.paulamoraga.com/book-spatial/

Ecological Models and Data in R
https://math.mcmaster.ca/~bolker/emdbook/book.pdf

Math for Machine Learning
http://users.umiacs.umd.edu/~hal/courses/2013S_ML/math4ml.pdf

Artificial Intelligence: A Modern Approach by Russell and Norvig.
https://github.com/mhahsler/CS7320-AI

A comprehensive 10-page probability cheatsheet that covers a semester's worth of introduction to probability.
https://github.com/wzchen/probability_cheatsheet

Linear Algebra via Exterior Products
https://github.com/winitzki/linear-algebra-book

Handbook of Data Visualization
https://haralick.org/DV/Handbook_of_Data_Visualization.pdf

MATH20802: Statistical Methods: Likelihood, Bayes and Regression
https://strimmerlab.github.io/courses/2022-23/MATH20802/index.html

如何用10天吃掉pyspark？
https://github.com/lyhue1991/eat_pyspark_in_10_days

《数据科学工程实践》一书的Jupyter Notebook库
https://github.com/xieliaing/Data_Science_Industrial_Practice

自学计算机科学
Path to a free self-taught education in Computer Science
https://github.com/ossu/computer-science

数学文化 期刊
https://global-sci.org/mc.html

The Missing Book
https://tmb.njtierney.com/

APPLIED MISSING DATA ANALYSIS
https://www.appliedmissingdata.com/

Introduction to Probability for Data Science
https://probability4datascience.com/

ALGORITHMS FOR DECISION MAKING
https://algorithmsbook.com/#

Tidy Finance with R
https://www.tidy-finance.org/

Python for Data Science
https://aeturrell.github.io/python4DS/welcome.html

Tidy Modeling with R
https://www.tmwr.org/

Doing Data Science in R
https://github.com/mark-andrews/ddsr

ISLR tidymodels labs
https://emilhvitfeldt.github.io/ISLR-tidymodels-labs/

Introduction to Data Science
Data Analysis and Prediction Algorithms with R
https://rafalab.github.io/dsbook/

Variability and Consistency in Early Language Learning
The Wordbank Project
https://langcog.github.io/wordbank-book/

课程

广义线性模型，分类数据分析
https://erbiostat.wixsite.com/bina1/online-tutorials

Data Science in Practice
https://course2022.scientistcafe.com/

Bayesian statistics with R
https://oliviergimenez.github.io/bayesian-stats-with-R/

EDS 221: Scientific programming essentials
https://allisonhorst.github.io/EDS_221_programming-essentials/

The R-Bootcamp is meant to be a gentle and gradual introduction to manipulating and visualizing data in R using the tidyverse suite of packages. Topics covered include:
https://github.com/laderast/RBootcamp

https://harvard-iacs.github.io/2021-CS109A/

CS109a: Introduction to Data Science

1. data collection ‐ data wrangling, cleaning, and sampling to get a suitable data set
2. data management ‐ accessing data quickly and reliably
3. exploratory data analysis – generating hypotheses and building intuition
4. prediction or statistical learning
5. communication – summarizing results through visualization, stories, and interpretable summaries

Building Production-Quality Shiny Applications
https://shinyprod.com/

Machine learning with tidymodels
https://workshops.tidymodels.org/

https://evalf22.classes.andrewheiss.com/
Program Evaluation for Public Service
Combine research design, causal inference, and econometric tools to measure the effects of social programs

文章

Data Scientist: The Sexiest Job of the 21st Century
https://hbr.org/2012/10/data-scientist-the-sexiest-job-of-the-21st-century
Thomas H. Davenport and DJ Patil

Is Data Scientist Still the Sexiest Job of the 21st Century?
https://hbr.org/2022/07/is-data-scientist-still-the-sexiest-job-of-the-21st-century
Thomas H. Davenport and DJ Patil

Sustainable Science as a Vocation
https://blogs.lse.ac.uk/impactofsocialsciences/2021/03/16/sustainable-science-as-a-vocation/

Happiness and Life Satisfaction
https://ourworldindata.org/happiness-and-life-satisfaction

Some models are useful, but how do we know which ones? Towards a unified Bayesian model taxonomy
https://arxiv.org/abs/2209.02439

Pareto Smoothed Importance Sampling
https://arxiv.org/abs/1507.02646v8

How much should we trust staggered difference-in-differences estimates?
https://andrewcbaker.netlify.app/publication/blw_did/BLW_DID.pdf

笔记

林荟 阅读笔记
https://hui1987.com/

材料

https://github.com/mcanouil/awesome-quarto
https://github.com/terryum/awesome-deep-learning-papers

Welcome to the Deep Learning Tutorial!
http://ufldl.stanford.edu/tutorial/

世界不平等
https://wid.world/

Tidy Verbs for Fast Data Manipulation
https://github.com/hope-data-science/tidyfst

[XiangyunHuang]

博客

R 语言 https://www.gastonsanchez.com/ Gaston Sanchez
R 语言 https://rdpeng.org/ Roger D. Peng
R 语言 https://mikeyharper.uk/ Michael Harper
R 语言 https://beamilz.com/ Bea Milz
R 语言 https://robertmitchellv.com/ Robert Mitchell
R 语言 https://laustep.github.io/stlahblog/ Stéphane Laurent
R 语言 https://visit.randy.city/ Randy Lai
R 语言 https://uncharteddata.netlify.app/ Kyle Cuilla
R 语言 https://yutani.rbind.io/ Hiroaki Yutani
R 语言 https://aosmith.rbind.io/ Ariel Muldoon
R 语言 https://asimumba.rbind.io/blog Aaron Simumba
R 语言 https://parallelr.com/ Patric Zhao
R 语言 https://putrinprod.com/# Heather Nolis and Jacqueline Nolis
贝叶斯 https://www.mjandrews.org/ Mark Andrews
网络分析 https://www.mr.schochastics.net/ David Schoch
网络分析 https://www.alexpghayes.com/ alex hayes
数据可视化 http://sophiebeiers.com/ Sophie Beiers
数据可视化 https://www.tanyashapiro.com/ Tanya Shapiro
数据可视化 https://jkunst.com/blog/ Joshua Kunst
文本分析 https://statsmaths.github.io/ Taylor Arnold
数据科学 http://rafalab.dfci.harvard.edu/ Rafael Irizarry
数据科学 https://simplystatistics.org/ Jeff Leek, Roger Peng, and Rafa Irizarry
空间分析 https://www.mrworthington.com/ Matt Worthington
深度学习 https://colah.github.io/ Christopher Olah

https://www.qiushiyan.dev/ 闫求识
https://jinjipang.com/ 逄金吉
https://licw.net/ 承文
https://cn.linhui.org/ 林荟

书籍（包含未出版）

Geocomputation with R https://geocompr.robinlovelace.net/ Robin Lovelace, Jakub Nowosad, Jannes Muenchow.
Spatial Modelling for Data Scientists https://gdsl-ul.github.io/san/ Francisco Rowe, Dani Arribas-Bel

Introduction to Computational Social Science https://bookdown.org/markhoff/css/ Mark Hoffman
Methods for Network Analysis https://bookdown.org/markhoff/social_network_analysis/ Mark Hoffman
Applied Social Network Analysis in Education https://bookdown.org/chen/snaEd/ Bodong Chen

Handling Strings with R
Supervised Machine Learning for Text Analysis in R https://smltar.com/
Tidy Text Mining with R http://tidytextmining.com/ Julia Silge and David Robinson
Workshop for Text Mining Using Tidy Data Principles https://juliasilge.github.io/tidytext-tutorial/
Text Analysis Using R https://quanteda.github.io/Text-Analysis-Using-R/ Kenneth Benoit and Stefan Müller

统计计算 https://bookdown.org/watthu16/_Book_StatCompWithR/
Beyond Multiple Linear Regression 线性模型、逻辑回归、泊松回归 https://bookdown.org/roback/bookdown-BeyondMLR/ Paul Roback and Julie Legler
Improving Your Statistical Inferences https://lakens.github.io/statistical_inferences/
ANOVA and Mixed Models: A Short Introduction Using R https://stat.ethz.ch/~meier/teaching/anova/ Lukas Meier
Survival Analysis in R https://bookdown.org/mpfoley1973/survival/ Michael Foley
Modern R with the tidyverse https://modern-rstats.eu/ Bruno Rodrigues
Reproducible Analytical Pipelines - Master’s of Data Science https://rap4mads.eu/ Bruno Rodrigues

Bayesian models of perception and action https://www.cns.nyu.edu/malab/bayesianbook.html Wei Ji Ma, Konrad Kording, and Daniel Goldreich
Artificial Intelligence: A Modern Approach, 4th US ed. http://aima.cs.berkeley.edu/ Stuart Russell and Peter Norvig
Understanding Deep Learning https://udlbook.github.io/udlbook/ Simon J.D. Prince
Multimodal Deep Learning https://slds-lmu.github.io/seminar_multimodal_dl/ Matthias Aßenmacher

[XiangyunHuang]

Julia

The Julia Programming Language
https://github.com/JuliaLang
An organization for the JuMP modeling language and related repositories.
SciML Open Source Scientific Machine Learning
https://github.com/SciML
A Julia machine learning framework
https://github.com/alan-turing-institute/MLJ.jl
Differentiation Tools in Julia
https://github.com/JuliaDiff
Mathematical Optimization in Julia
https://github.com/JuliaOpt
An organization for the JuMP modeling language and related repositories.
https://github.com/jump-dev
Data visualization in Julia
https://github.com/JuliaPlots
Data manipulation, storage, and I/O in Julia
https://github.com/JuliaData
Highly productive web development with Julia
https://github.com/GenieFramework
Software that connects the Julia and Python languages.
https://github.com/JuliaPy
Statistics and Machine Learning made easy in Julia
https://github.com/JuliaStats

[XiangyunHuang]

The phrase "does not work" is not very helpful, it can mean quite a few things including:

• Your computer exploded.
• No explosion, but smoke is pouring out the back and microsoft's "NoSmoke" utility is not compatible with your power supply.
• The computer stopped working.
• The computer sits around on the couch all day eating chips and watching talk shows.
• The computer has started picketing your house shouting catchy slogans and demanding better working conditions and an increase in memory.
• Everything went dark and you cannot check the cables on the back of the computer because the lights are off due to the power outage.
• R crashed, but the other programs are still working.
• R gave an error message and stopped processing your code after running for a while.
• R gave an error message without running any of your code (and is waiting for your next command).
• R is still running your code and the time has exceeded your patience so you think it has hung.
• R completed and returned a result, but also gave warnings.
• R completed your command, but gave an incorrect answer.
• R completed your command but the answer is different from what you expect (but is correct according to the documentation).

There are probably others. Running your code I think the answer is the last one.

--- Greg Snow ¹

Footnotes

1. 来自 R 社区论坛收集的智语 fortunes::fortune(324)。 ↩

[XiangyunHuang]

混合模型

https://bbolker.github.io/mixedmodels-misc/
https://bbolker.github.io/mixedmodels-misc/glmmFAQ.html
https://sta310-sp22.netlify.app/
https://people.math.aau.dk/~sorenh/software/pbkrtest/
https://github.com/JuanLopezMartin/MRPCaseStudy
http://www.css.cornell.edu/faculty/dgr2/index.html
https://github.com/courtiol/LM2GLMM
https://ourcodingclub.github.io/tutorials/mixed-models/
https://ourcodingclub.github.io/tutorials/spatial-modelling-inla/
https://www.tjmahr.com/
https://meghan.rbind.io/blog/2022-06-28-a-beginner-s-guide-to-mixed-effects-models/
https://www.mrc-bsu.cam.ac.uk/wp-content/uploads/geobugs12manual.pdf

GLMM 的对数似然函数 拉普拉斯近似
https://juliamixedmodels.github.io/EmbraceUncertainty/

https://crsl4.github.io/julia-workshop/
https://psyteachr.github.io/stat-models-v1/

一篇硕士学位论文，可行的 INLA 的工业实践
RESTful Scraping API for Real Estate data, a Spatial Bayesian modeling perspective with INLA
https://nsmasterthesis.netlify.app/

混合效应模型 Stan INLA 等案例代码
https://github.com/julianfaraway/rexamples

数据开发

https://cn.aliyun.com/product/bigdata/ide
https://www.sqlstyle.guide/zh/
https://extendsclass.com/python-formatter.html
https://jsoneditoronline.cn/
https://regex101.com/
https://sqlformat.org/
https://dev.mysql.com/doc/
https://doris.apache.org/zh-CN/
https://tech.meituan.com/2016/03/31/spark-in-meituan.html
https://tech.meituan.com/2016/04/29/spark-tuning-basic.html
https://tech.meituan.com/2016/05/12/spark-tuning-pro.html
http://spark.apachecn.org/#/docs/1
https://spark.apache.org/sql/
https://hive.apache.org/
https://spark.apache.org/docs/latest/api/sql/index.html
https://dbaplus.cn/news-73-3416-1.html
https://ai.meituan.com/#data-list

博客列表

https://infowetrust.com/
https://r-charts.com/
https://yufree.cn/cn/2020/09/20/project-management/
https://www.zevross.com/
https://attalitech.com/
https://yihui.org/en/2018/08/25-years-of-data-science/
https://github.com/PietroViolo
https://opentopography.org/blog
https://yuejiang-nj.github.io/
https://www.liding.page/
https://space.bilibili.com/520819684
https://dansblog.netlify.app/
https://spencerschien.info/
https://youngstats.github.io/
https://tanyashapiro.netlify.app/
https://cuidi1996.github.io/
https://lo-ng.netlify.app/
https://www.offconvex.org/

张源源
https://hengqujushi.github.io
常象宇
https://xiangyuchang.github.io/
张潼
https://tongzhang-ml.org/
陈松蹊
https://www.songxichen.com/
袁晓如
http://vis.pku.edu.cn/yuanxiaoru/

cai jun 复旦大学 流行病学
https://blog.tonytsai.name/

Yang Liu
https://liuyanguu.github.io/

Robert M Haralick
https://haralick.org/
Johannes Lederer
https://johanneslederer.com/
JUDEA PEARL
http://bayes.cs.ucla.edu/jp_home.html

效率工具

https://www.doi2bib.org/
https://tableconvert.com/
https://zh.sg1lib.org/
https://mp.weixin.qq.com/
https://www.connectedpapers.com/
https://weixin.sogou.com/
https://cilicili.cn/
https://mdnice.com/
https://developers.weixin.qq.com/doc/offiaccount/Getting_Started/Getting_Started_Guide.html
https://www.iconfont.cn/
https://sci-hub.ee/
https://wheelofnames.com/zh-CN
https://www.veed.io/video-compressor
https://www.fontke.com/tool/rgbschemes/
https://pkgs.org/
https://wangchujiang.com/linux-command/
https://www.jsdelivr.com/github
https://github.com/MichelNivard/gptstudio
https://c.albert-thompson.com/latex-pretty/

视频编辑 shotcut https://github.com/mltframework/shotcut
矢量图片编辑 inkscape https://inkscape.org/
屏幕录制工具 obs-studio https://github.com/obsproject/obs-studio
视频播放 IINA https://iina.io/

网络分析

https://kateto.net/network-visualization
http://curleylab.psych.columbia.edu/netviz/netviz1.html#/
https://github.com/briatte/awesome-network-analysis
https://github.com/albertyumol/network_science_intro_R
http://blog.schochastics.net/post/network-centrality-in-r-introduction/
https://www.mr.schochastics.net/material/netVizR/
https://www.mr.schochastics.net/material/netAnaR/
https://mr.schochastics.net/
https://kpress.dev/blog/2022-07-30-the-office-part-iii-37-pieces-of-flair/
https://bookdown.org/markhoff/social_network_analysis/

[XiangyunHuang]

机器学习

Mosek 半正定锥优化
https://docs.mosek.com/modeling-cookbook/sdo.html

最优传输理论 优化问题 数学建模
https://lccurious.github.io/2020/01/30/optimal-transport/
Introduction to Optimal Transport Theory
https://lchizat.github.io/ot2021orsay.html

如何理解 glm（generalized linear model）函数中的weights
https://d.cosx.org/d/421754-glmgeneralized-linear-modelweights

从方差分析到线性模型，点出方差分析与线性模型的关系
https://stat.ethz.ch/~meier/teaching/anova

一般线性模型作为周边介绍一下，statmod: Statistical Modeling
https://cran.r-project.org/package=statmod

nlmeU 包 lmeinfo 包线性混合模型
https://www.math.pku.edu.cn/teachers/lidf/docs/Rbook/html/_Rbook/stat-lme.html

回归问题的数据集 波士顿房价 机器学习中回归问题一章

补充波士顿的地图和社区级波士顿的收入分布 待定
Arya A. Pourzanjani采用贝叶斯神经网络分析方法 预测房价。

Using data science to analyze my data science book
https://jnolis.com/blog/data_science_on_book/

空间数据分析

普通克里金预测、预测方差的代码检查
Spatial Interpolation and Spatial prediction
https://edzer.github.io/mstp/si2.html

绘制封面图片

10 米精度的 RGB 卫星图像 10-meter RGB Satellite Imagery - Ailinginae Atoll, Rongerik Atoll, and Rongelap Atoll (year 2020)
https://rmi-data.sprep.org/resource/10-meter-rgb-satellite-imagery-ailinginae-atoll-rongerik-atoll-and-rongelap-atoll-year

30 米精度的海拔数据 DEM
https://rmi-data.sprep.org/resource/1-arc-second-digital-elevation-model-and-hillshade
https://github.com/Pecners/rayshader_portraits
https://github.com/h-a-graham/rayvista
https://github.com/tylermorganwall/rayshader
https://github.com/jhollist/elevatr 30 米是最好的数据了

Create stacked tilted maps
https://github.com/marcosci/layer

Color palette package in R inspired by works at the Metropolitan Museum of Art in New York
https://github.com/BlakeRMills/MetBrewer

获取空间数据 rnationearth 包

R packages to download open spatial data
https://www.paulamoraga.com/tutorial-open-spatial-data/

https://github.com/GeostatsGuy/Resources

https://github.com/PietroViolo/typical_house_prices_county

房价数据用于空间分析
data("kc_housing", package = "mlr3data")

Gaussian Process Boosting
spData 包内置的 house 数据集 关于 Lucas County, Ohio 房价的空间数据

地图投影的参数效果动画
https://www.d3indepth.com/geographic/

[XiangyunHuang]

文本分析

最全中华古诗词数据库, 唐宋两朝近一万四千古诗人, 接近5.5万首唐诗加26万宋诗. 两宋时期1564位词人，21050首词。
https://github.com/chinese-poetry/chinese-poetry

A/B 实验

AA 实验的目标是确保对照组和实验组的样本分布一致，对于目标指标来说，两个组没有显著差异

此处的点击率指标具有一般性，代表漏斗型的转化指标，具体地，搜索业务中的访购率，即购买人数/访问人数。

第一天
实验组 浏览 点击 点击率
对照组 浏览 点击 点击率

连续 7 天，有的天，实验组 > 对照组 的点击率 有的，反之

7 天，实验组和对照组分别有 7 个点击率数据，将点击率看作来自正态总体的样本
做点击率之差的均值检验，使用 Welch t 检验，是否合理？

比例趋势检验是否与此有关？无关

合并 7 天的数据，分别累加实验组和对照组的浏览量、点击量，再计算点击率，使用比例检验 / 卡方检验（Fisher 精确检验）？

国外有专门软件
PASS（Power Analysis and Sample Size）https://www.ncss.com/software/pass/
https://www.ncss.com/software/pass/pass-documentation/#Nonparametric
比例趋势检验 逻辑回归
Overlapping Experiment Infrastructure: More, Better, Faster Experimentation
https://github.com/oldratlee/translations/tree/master/overlapping-experiment-infrastructure-more-better-faster-experimentation

https://en.wikipedia.org/wiki/A/B_testing#cite_note-14

https://exp-platform.com/Documents/2019-FirstPracticalOnlineControlledExperimentsSummit_SIGKDDExplorations.pdf

Ron Kohavi
https://ai.stanford.edu/~ronnyk/

2012年高教社杯全国大学生数学建模竞赛赛题
A题 葡萄酒的评价
http://www.mcm.edu.cn/problem/2012/2012.html

统计计算

Householder 变换

https://gitlab.com/libeigen/eigen/-/blob/master/Eigen/Householder

[XiangyunHuang]

其他工具

• 《Git 的奇技淫巧》 https://github.com/521xueweihan/git-tips
• 《Bash 教程》阮一峰 https://github.com/wangdoc/bash-tutorial
• 《跟我一起写 Makefile》陈浩 https://github.com/seisman/how-to-write-makefile
• 《快乐的 Linux 命令行》Peter Wang 和 Billie Zhang https://billie66.github.io/TLCL/
• 《Linux 就该这么学》刘遄 https://www.linuxprobe.com/docs/LinuxProbe.pdf
• 《AWK 程序设计语言》https://github.com/wuzhouhui/awk
• Linux Tips, Tricks and Tutorials https://linuxize.com/
• 阮一峰, 2021. https://wangdoc.com/
• SSH 教程, 阮一峰, 2021. https://wangdoc.com/ssh/
• Bash 脚本教程, 阮一峰, 2021. https://wangdoc.com/bash/
• Bash 参考手册, 阮一峰, 2021. https://www.gnu.org/software/bash/manual/html_node/index.html

以及了解一些大数据处理工具:

• 15 天入门 SQL 查询 https://github.com/songxxiao/sqlexam
• 大数据入门指南 https://github.com/heibaiying/BigData-Notes
• 架构师技术图谱 https://github.com/toutiaoio/awesome-architecture

一些基础算法:

• 小浩算法 https://github.com/geekxh/hello-algorithm
• labuladong 的算法小抄 https://github.com/labuladong/fucking-algorithm
• 《Effective Modern C++》翻译 https://github.com/kelthuzadx/EffectiveModernCppChinese

[XiangyunHuang]

https://github.com/Jam3/math-as-code
a cheat-sheet for mathematical notation in code form

图形直观
Graphic notes on Gilbert Strang's "Linear Algebra for Everyone"
https://github.com/kenjihiranabe/The-Art-of-Linear-Algebra