Ciencia de datos, estadística y matemáticas

Curriculum Guidelines for Undergraduate Programs in Data Science. Annual Review of Statistics and Its Application. 2017. Link.


  • Should Computer Science Be Required?. Link.
  • Five reasons why researchers should learn to love the command line. Link.
  • RStudio Webinars. Link.


  • rstudio cloud. Link.
    • Teaching R online with RStudio Cloud. Link.
    • Computing in the Cloud with Google and RStudio. Link.
  • Matlab online. Link.

Buscar ayuda

  • Stack Overflow. Link.***
  • Our coding club. Link. Link.
  • How do I? Link.
  • Carpentries para Latinoamérica. Link.
    • Upcomming workshops. Link.
  • Unimelbourne
    • Resbaz (Git, Matlab, R, Python, Shell, Nltk, Authorea). Link
    • Research Computing Services Training at GitLab. Link
  • Style guide. Link. Google Style Guide. Link.
  • Google’s R Style Guide. Link.


  • Introduction to Computer Science. Edx. Link.
  • Introduction to Computational Thinking. Math from computation, math with computation. Link.
  • Introducción a la Programación para Ciencias e Ingeniería. Link.
  • Programming Basics. EdX. Link.
  • HarvardX Biomedical Data Science Open Online Training. Link.
  • Algorithmic Thinking (Part 1). Coursera. Link
  • Blog DataCamp. Link.
  • Glossary of Computer Programming Terms. Link.
  • Link


  • Buscar ayuda y aprender
    • Crantastic. Link.
    • Datacamp. Link.***
    • Elementary Statistics With R. Link 
    • Quick-R. Link. ***
    • R-bloggers. Link.
    • Learnr4free. Link.
    • Lista de paquetes. Link.
    • R Programming Tutorials. Link.
    • Rseek. Link.
    • RStudio Cloud in the Classroom. Link.
    • RStudio Cheat Sheets. Link.
    • Link.
    • Programiz. Link.
    • R documentation. Link.
  • RStudio Cheatsheets. Link.
RStudio IDE cheatsheet. Link.
Base R. Link
Data import. Link
Data visualization with ggplot2 cheatsheet. Link.
  • Libros
    • Best Coding Practices for R. Link
    • Free R Reading Material. Link.
    • LEl arte de programar en R: un lenguaje para la estadística. Link.
    • R Cookbook. Proven Recipes for Data Analysis, Statistics, and Graphics. Link.
    • R in a Nutshell. Link.
  • Basico
    • Materiales de RStudio en Español. Link.
    • R para Ciencia de Datos. Link.
    • R para profesionales de los datos. Link.
    • Introduction to R. Datacamp. Link.
    • Learning R. Link.
    • AprendeR. Miriada X. Link.
    • Introduction to R for Data Science. Edx. Link.
    • Quick R. Link.
    • Swirl. Link.
    • Code School. Link.
    • Do More with R. Link.
    • R for Data Science. Grolemund & Wickham. Link
    • R para Ciencia de Datos. Link.
    • Applied R in the Classroom. Link.
    • rstudio4edu. A Handbook for Teaching and Learning with R and RStudio. Link.
  • Intermedio
    • Data Science. Link
    • Intermediate R. Datacamp. Link.
    • R for Data Science. Link.
    • R-Statistics-Essential-Training. Link.
    • R Programming. Coursera. Link.
    • R Workshop for Social Scientist NOAA. Link.
    • R-Bootcamp. Link.**
    • Awesome R Learning Resources. Link.
  • Avanzado
    • Advanced statistics. Stat tools. Link.
    • Ecological Models and Data in R. Link.
    • Advanced R. Hadley Wickham. Link.
    • Mastering Software Development in R. Link.
    • Data Science in Education Using R. Link.
  • Tydiverse
    • Remaster the Tidyverse. Link.
    • El paquete dplyr. Link.
    • Dive into dplyr (tutorial). Link.
    • TidyTuesday***. Link.
rmarkdown cheatsheet. Link
  • Markdown
  • R markdown at rstudio. Link.
  • Introduction to R Markdown. Link.
  • R Markdown: The Definitive Guide. Link.
  • R Markdown. Wires computational statistics. Link.
  • Getting Started with R Markdown. Link — Guide and Cheatsheet. Link.
  • RMarkdown for Scientists. Link.
  • Advanced data manipulation and visualisation. Markdown. Link.
  • Communicating using Markdown. Link.
  • Building an R Markdown website. Link.
  • Slidev. Link.
Data wrangling
  • Ejercicios aplicados
    • Mean trophic levels of a genera from FishBase. Seascape models. Link. Link.
    • Coral reef degradation is not correlated with local human population density. Scientific Reports. 2016. Link. Code.
    • Macro Ecology and Spatial Statistics. Link.
    • R-packages for oceanographic data visualisation. Link.
    • Forecasting El Niño-Southern Oscillation (ENSO). Link.
    • R programming tools for conservation scientists. Link.
    • Conservation programming in R. Link.
  • Tablas en R
    • R Data.table. Link.
    • R gt package. Link
    • 10+ Guidelines for Better Tables in R. Link.
    • Data.Table Tutorial (with 50 Examples). Link.
    • Aggregation and Restructuring data (from “R in Action”). Link.
  • Gráficas en R
    • r-graph gallery. Link.
    • A Compendium of Clean Graphs in R. Link.
    • R Graphics Cookbook, 2nd edition. Link.
    • 3D ggplots with rayshader – RStudioRayshader. Link.
    • Rayrender. Link.
    • Data Visualization. Link.
    • Data Visualization in R. DataCamp. Link.
    • Graphs. QuickR. Link.
    • Creating memorable data visualisations using the R programming language – Chris Brown. Winter School Online Lectures. Link.
    • From data to viz. Link.
    • Interactive web-based data visualization with R, plotly, and shiny. Link.
    • dygraphs for R. Time series. Link.
    • BBC Visual and Data Journalism cookbook for R graphics. Link
    • Graphics Global Warming. IPCC. Link.
  • Otros temas
    • For en R. Link.
    • Easy and efficient data manipulation. Link.
Introducción a ggplot2 – Pablo Vallejo
  • ggplot2
    • ggplot2: Elegant Graphics for Data Analysis. 3rd edition. Link.
    • Overview. Link. Reference. Link.
    • Visualización de Datos usando ggplot2 Guía Rápida. Link.
    • Elegant Graphics for Data Analysis. Link.***
  • Shiny
    • a gRadual intRoduction to Shiny. Link.
    • Mastering Shiny. Link.
    • Shiny tutorial. ***Link. Link. Link
    • Shiny gallery. Link.
    • Shiny widget gallery. Link
    • Ejemplos Shiny apps
Shiny. Link
  • Bayesian Statistics
    • Learning Bayesian Statistics Course. Link.
    • Bayes Rules! An Introduction to Bayesian Modeling with R. Link.
    • Bayesian Data Analysis course at Aalto. github. Link.
    • Statistical Rethinking: A Bayesian Course (with Code Examples in R/Stan/Python/Julia). Link.
Learning bayesian statistics


  • The three technologies bioinformaticians need to be using right now. Link.
  • Curso Phyton (temario para aprender lenguaje). Link.
  • A Whirlwind Tour of Python. Link.
  • Introduction to Computer Science and Programming in Python. MIT. Link.***
  • Using Python for Research Videos. Link.
  • Getting Started with Cloud-Native HLS Data in Python. Link.
  • Practicas de programación con Python. Link.


  • Julia. Link.
  • Julia: come for the syntax, stay for the speed. Nature 2019. Link.
Introduction to Julia


  • Matlab online. Link.
  • MATLAB Academy. Link.
  • Matlab Unimelbourne. Link.
  • Aprende a programar con Matlab. Tutellus app. Link.
  • Computer Programming with MATLAB. Fitzpatrick and Ledeczi. Link
  • Matlab Wiki. Link.
  • MATLAB and Octave for Beginners. Edx. Link.
  • Highlights of Calculus. Prof. Gilbert Strang. Link.
    • Gilbert Strang site. Link.
  • The Climate Data Toolbox for MATLAB. Link.


  • Git Book (en español). Link.***
  • Happy Git and GitHub for the useR. Link.***
  • El Control de Versiones con Git. Link.
  • Git and GitHub. Link.
  • How to Use Git and GitHub. Class Central. Link. Udacity. Link.
  • Reproducible Analysis With R. Link.***
  • Github Guides. Link.
  • GitHub Pages. Link.
  • Building a Website with Blogdown, Hugo and Github Pages. Link.
  • GitHub Learning Lab. Link.
  • Otros servicios
    • Gitlab. Link.
    • ZenHub. Link (for managing GitHub issues).
    • Git Kraken. Link.
Git y Github | Curso Práctico de Git y Github Desde Cero
NOAA Social Science R Workshop. Getting starting with git and GitHub using RStudio NOAA. Link.
Linus Torvalds on Git


  • Google
  • Blogs a seguir
    • From the bottom of the heap. Link.
    • Recology. Link.
    • Methods in Ecology and Evolution. Link.
    • Sea Scape Models. Link.
    • Andrew Parnell. Talks. Link.
  • Eventos académicos
    • The second annual Symposium on Data Science and Statistics. Link
    • Links to slides from rstudio::conf 2019. Link.
    • RStudio Webinars. Link.
      • RStudio: A Single Home for R & Python. Link.


  • NEON code. Link.
  • EarthCube 2021 Notebooks. Link.
  • Geocomputation with R. Link.
  • Remote Sensing Image Analysis. Link.
  • Spatial Data Science with R. Link.
  • Geoscripting. Wageningen U. Link.
  • Converting HDFs to ArcGIS Rasters. Link.
  • Earth – Spatial data manipulation software. Link.
  • Maps in R. Link.
  • Spatial data in R: Using R as a GIS. Link.
  • Spatial data manipulation. Link.
  • An Introduction to Spatial Analysis in R. Seascape models. Link.
  • Working with Geospatial Data in R. Data Camp. Link.
  • Spatial ly. Link.
  • An Introduction to Spatial Data Analysis and Visualisation in R. Link.
  • ARE. Spatial data. Link.
  • RSToolbox. Link.
  • Tidy spatial data analysis. R Studio. Link.
  • Crop mapping with Sentinel-2 focusing on algorithms. Link.
  • Average seasonal colors of the USA. Link.
  • Artificial Intelligence (AI) for Earth Monitoring. Link.
  • R for Geographic Data Science. Link.
  • Network Common Data Form- NetCDF
    • Unidata. Link.
    • NetCDF metadata conventions. Link.
    • NetCDF and CF: The Basics. Link.
    • NetCDF in R. Link.
    • U Oregon. Geographic Data Analysis and Visualization: Topics and Examples. Link. NetCDF Link.
    • An Ecologist’s Guide to Working with Daymet and other NetCDF formatted Data. Video Link. Rpubs Link.
    • Working with NetCDF files. Link.
    • How to open and work with NetCDF data in R. Link.
    • NetCDF Ninja Software. Link.
    • Urban Demographics. Link.
    • ARE 202. Spatial data. Link.


  • Mapping sugarcane in Thailand using transfer learning, a lightweight convolutional neural network, NICFI high resolution satellite imagery and Google Earth Engine. 2021. Link.


  • MIT Open Courseware.
    • Fundamentals of linear algebra. Link. Resources. Link.
    • Matrix Methods in Data Analysis, Signal Processing, and Machine Learning. Link.
    • A 2020 Vision of Linear Algebra. MIT. Link.
  • Differential Equations and Linear Algebra. Matlab. Gilbert Strang. Link. Link. Link.
  • Eigenvectors and Eigenvalues. Link.
  • LAFF: Linear Algebra – Foundations to Frontiers. EdX Link. Youtube.
  • Computational Linear Algebra for Coders. Link.
  • The Feyman lectures on physics. Link. Algebra. Link.
  • Teoría de redes
    • D# Graph Theory. Link.
    • Networks. MIT. Link.
    • Essential graph theory for biologists. Winter School Online Lectures. Link.
    • MIT Matlab Tools for Network Analysis. Link.
    • Networked and Social Systems Engineering (NETS) by Michael Kearns. Link
    • Networked Life by Michael Kearns. Coursera. Link.
    • Social Network Analysis. Coursera. Link.
    • Networks, Crowds, and Markets. Link.
    • Network Science by Albert-László Barabási. Link.
    • Mining Massive Datasets. Coursera. Link.
    • ArcGis. OD cost matrix analysis. Link.
    • The Department of Network and Data Science at Central European University. Link.
    • Código
      • Network visualization. Link.
      • Network Analysis and Visualization with R and igraph. Link.
      • Systems Biology and Evolution Toolbox. Matlab. Link. Link.
      • Static and dynamic network visualization with R. Link.
      • Brain Connectivity Toolbox. Matlab. Link.
      • Network visualization with R. Link.
    • Lecturas
      • Newman M. 2010. Networks: An Introduction. Link.
      • From Graphs to Spatial Graphs. AREES. 2010. Link.


  • Introduction to Modern Statistics, First Edition. Link.
  • Graduate Statistics in R. Link .***
  • Online Statistics Education: An Interactive Multimedia Course of Study. Link.***
  • Foundations of Data Analysis – Part 2. EdX. Link
  • Explore Statistics with R. EdX. Link
  • Estadística inferencial. Khan Academy. Link
  • Cálculo poder estadístico alfa y beta. Gpower. Link
  • Probability. MIT Open Courseware. Link.
  • Seeing Theory. Brown University. Link.
  • Introduction to Statistical Genomics. Joshua Akey Spring 2008. Link.
  • Statistics for Biologists, Nature Points of Significance. Link.
  • Statistics for applications. Link.
  • Statistical Rethinking. Link.
  • Statistical rethinking with brms, ggplot2, and the tidyverse. Link.
  • Graduate Statistics in R. Link .***
  • JASP Software Link, and Center for Open Science Link.
  • Online Statistics Education: An Interactive Multimedia Course of Study. Link.***
  • Foundations of Data Analysis – Part 2. EdX. Link
  • Explore Statistics with R. EdX. Link
  • Estadística inferencial. Khan Academy. Link
  • Cálculo poder estadístico alfa y beta. Gpower. Link
  • Probability. MIT Open Courseware. Link.
  • Seeing Theory. Brown University. Link.
  • Introduction to Statistical Genomics. Joshua Akey Spring 2008. Link.
  • Statistics for Biologists, Nature Points of Significance. Link.
  • Statistics for applications. Link.
  • Statistical Rethinking. Link.
  • Probabilidad Básica. Introducción a los conceptos básicos de probabilidad. BrownU. Link.
  • BIOST561 «Computational Skills for Biostatistics. Link.


  • Central Limit Theorem. Link
  • La potencia y el tamaño del efecto. Link.

Análisis de varianza

  • ANOVA 1 – Calculando la Suma Total de Cuadrados (STC). Link.
  • ANOVA 2 – Calculando suma total de cuadrados dentro y entre (SCD y SCE). Link.
  • ANOVA 3 — Prueba de hipótesis con estadístico F. Link.

ANOVA medidas repetidas

  • Análisis de varianza factorial medidas repetidas (ANOVA). Link.
  • R – One Way Repeated Measures ANOVA. Link.
  • Learn R One Way Repeated Measures ANOVA Lecture 1. Link.
  • Repeated Measures in R. Link.
  • Repeated Measures and Longitudinal Data. Penn State. Link.
  • Notes on the use of R for psychology experiments and questionnaires Jonathan Baron. Link.


  • Regression Modeling in Practice. Coursera. Link.
  • Linear algebra: Projections, least squares and best straight line. Lec 15, Lec 16.

Análisis de componentes principales

  • What is principal component analysis?. Nature Biotechnology. 2008. Link.
  • A layman’s introduction to principal component analysis. Video. Link.

Diseño de experimentos

  • Designing, Running, and Analyzing Experiments. Coursera. Link.

Modelos lineales generalizados (GLM) y modelos mixtos

  • Resources:
    • Mixed models. GLMM FAQ Ben Bolker and others. Link.
    • Introduction to Linear Models with R. Link.
    • Linear regression in R. Link.
    • Choosing R packages for mixed effects modelling based on the car you drive. Link.
    • Introducción a los modelos mixtos. Link. Link.
    • Generalized Linear Models in R. Datacamp. Link.
    • Generalized Linear Models: understanding the link function. Link.
    • Generalized Additive Models in R. Link.
    • Introduction to Generalized Additive Models. Link.
  • Videos:
    • Maximum Likelihood, clearly explained. Videos: Link. LinkLink.
    • Mixed effects models with R. Videos: Link. Link.
    • The Power of Mixed Effects Models. Week 9. Designing, Running, and Analyzing Experiments. Coursera. Link.
  • Books:
    • Zuur, et al. 2009. Mixed Effects Models and Extensions in Ecology with R. Link.
    • Correa Morales, Juan Carlos; Salazar Uribe Juan Carlos. Introducción a los modelos mixtos / Medellín: Universidad Nacional de Colombia. Link.
    • Pinheiro José C.; Bates Douglas M. Mixed-Effects Models in S and S-PLUS. Link.
  • Readings:
    A brief introduction to mixed effects modelling and multi-model inference in ecology. PeerJ. Link.

Formulas to fit a linear model, where:y represents the response variable; xx1x2x3 represent continuous variates; and upper case letters represent factors (From link).

y ~ xSimple regression
y ~ 1 + xExplicit intercept
y ~ -1 + xThrough the origin
y ~ x + I(x^2)Quadratic regression
y ~ x1 + x2 + x3Multiple regression
y ~ G + x1 + x2Parallel regressions
y ~ G / (x1 + x2)Separate regressions
sqrt(y) ~ x + I(x^2)Transformed
y ~ GSingle classification
y ~ A + BRandomized block
y ~ B + N * PFactorial in blocks
y ~ x + B + N * Pwith covariate
y ~ . -x1All variables except x1
y ~ . + A:BAdd interaction (update)
Nitrogen ~ Times*(River/Site)More complex design

Formulas for specifying random-effects structures in R used by lme4, nlme (nested effects only, although crossed effects can be specified with more work) glmmADMB and glmmTMB(From Ben Bolker and others).

(1|group)random group intercept
(x|group) = (1+x|group)random slope of x within group with correlated intercept
(0+x|group) = (-1+x|group)random slope of x within group: no variation in intercept
(1|group) + (0+x|group)uncorrelated random intercept and random slope within group
(1|site/block) = (1|site)+(1|site:block)intercept varying among sites and among blocks within sites (nested random effects)
site+(1|site:block)fixed effect of sites plus random variation in intercept among blocks within sites
(x|site/block) = (x|site)+(x|site:block) = (1 + x|site)+(1+x|site:block)slope and intercept varying among sites and among blocks within sites
(x1|site)+(x2|block)two different effects, varying at different levels
x*site+(x|site:block)fixed effect variation of slope and intercept varying among sites and random variation of slope and intercept among blocks within sites
(1|group1)+(1|group2)intercept varying among crossed random effects (e.g. site, year)

Generalized additive models (GAMs)

  • Generalized additive models for large data sets. Link.


  • aRtsy: Generative Art with R and ggplot2. Link.


  • Time Series for Ecologists and Climatologists: course timetable. Link.
  • Accounting for autocorrelation in R. Link.
  • Manipulating Time Series Data in R: Case Studies. Datacamp. Link.
  • Time Series Analysis. MIT. Link.
  • Applied Time Series Analysis. Penn State U. Link.


  • Introduction to Bayesian Inference with Stan. Link
  • Robust Bayesian linear regression with Stan in R. Link.
  • StanCon 2018. Link. Materials from StanCon. Link.
  • **Statistical Rethinking: A Bayesian Course Using R and Stan. Link.

Temas varios

  • Nonparametric Tests of Group Differences. Link.
  • The Statistical Bootstrap and Other Resampling Methods. Link. Link. Link.
  • Ten Simple Rules for Effective Statistical Practice. PLOS. 2016 Link.
  • Statisticians issue warning over misuse of P values. Nature. 2016. Link

Open software for statistical analysis

  • JASP. Link and Center for Open Science Link.
  • SAS University Edition. Link.

Meta-Analisis and data papers

  • Introduction to Systematic Review and Meta-Analysis. Coursera. Link.
  • Fixed effects vs Random effects. Link.
  • Software: RevMan.Cochrane. Link.
  • What are data papers? Data and metadata. Ecology. Link.


  • CRExplorer. Link.
  • HistCite. Link.
  • Loet Leydesdorff. Link.
  • Cited References Explorer. Link.
  • Bibliometrics for Librarians. Link.**
  • Handbook of Graphs and Networks in People Analytics: With Examples in R and Python. Link
  • Paquetes de R:
    • Package ‘bibliometrix’. Link. Link.


  • Datos
    • UN Office on Drugs and Crime. Link.
  • Imagenes
    • ImageJ. Link.
    • Digitizing data from old figures with ImageJ. Link.
    • WebPlotDigitizer. Link.
    • Tabula. A tool for liberating data tables locked inside PDF files. Link.
  • Otras lecturas
    • May, R.M. 2004. Uses and Abuses of Mathematics in Biology. Science. (303) 5659: 790-793. Link.
    • Oreskes N, et al. 1994. Verification, Validation, and Confirmation of Numerical Models in the Earth Sciences. Science. Link.

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