Resources

Content:

some personal projects I did for fun;
a selection of books, tutorials and tools that have helped me with my studies and projects;
some tutorials (⬈) and codes I store to reuse and not forget.

My personal projects

Data Science Project "One" (⬈): a journey from database to machine learning model deployment, with step-by-step tutorials. NoSQL, MongoDB, Python, shell scripting.

climeStats (⬈): An interactive interface to analyse climatic data. Shiny, RStudio, open data.

Relational database (⬈): an exercise of database modeling, creating tables and storing data. Video included. In Spanish. MySQL, MySQL Workbench, R.

aemetAPP (⬈): A web app to visualize and download meteorological data in Spain. Python, Flask, open data.

Useful links

Linear models

Generalized additive models: https://m-clark.github.io/generalized-additive-models/

Mixed-effect models:

with frequentist and bayesian methods: https://optimumsportsperformance.com/blog/mixed-models-in-sport-science-frequentist-bayesian
making predictions: http://optimumsportsperformance.com/blog/making-predictions-from-a-mixed-model-using-r/

Regression models and mixed-effects models: https://psyteachr.github.io/stat-models-v1/index.html

Bayesian statistics and modelling

Pensamiento Estadístico, Felipe Bravo, Department of Computer Science, University of Chile https://github.com/dccuchile/CC6104, https://www.youtube.com/playlist?list=PLppKo85eGXiXpvRVYM5ZJEHWWofjzuiXw

Markov-chain Monte Carlo Interactive Gallery https://chi-feng.github.io/mcmc-demo/

Introduction to Bayesian statistics in R & brms https://github.com/benjamin-rosenbaum/bayesian-intro

Multivariate statistical methods

Unraveling principal component analysis. https://peterbloem.nl/publications/unraveling-pca

Correspondence analysis, https://www.displayr.com/how-correspondence-analysis-works/ , http://www.sthda.com/english/articles/31-principal-component-methods-in-r-practical-guide/120-correspondence-analysis-theory-and-practice/

Videos about principal component analysis: Using prcomp and varimax for PCA in R, PCA : how to interpret the weights/loadings and Varimax rotation, StatQuest PCA Step-by-Step

Miscellaneous

Statistical testing and the intuition behind p-values through simulation: https://nullworlds.andrewheiss.com/

Understanding Maximum Likelihood: https://rpsychologist.com/likelihood/

Videos about statistics, machine learning and related topics https://www.youtube.com/@statquest, with and index of the published videos at https://statquest.org/video-index/

Alfonso Garcia Perez, professor of statistics, https://www.youtube.com/@alfonsogarciaperez9717, website https://www.uned.es/universidad/docentes/ciencias/alfonso-garcia-perez.html

Introducción a la inferencia a partir de las pendientes de rectas de regresión muestral. https://www.youtube.com/watch?v=bWG7-WmVtQE

Advanced R https://adv-r.hadley.nz/

Forecasting: Principles and Practice by R.J. Hyndman and G. Athanasopoulos, Monash University, Australia. https://otexts.com/fpp2/

Deep Learning, by Ian Goodfellow and Yoshua Bengio and Aaron Courville. https://www.deeplearningbook.org/

Deep Learning Book Series, by Hadrien Jean Ph.D, https://hadrienj.github.io/deep-learning-book-series-home/

R-squared: Where Geometry Meets Statistics. https://blog.minitab.com/blog/statistics-and-quality-data-analysis/r-squared-sometimes-a-square-is-just-a-square

Cross Correlation Functions and Lagged Regressions. https://online.stat.psu.edu/stat510/lesson/8/8.2

Regression ANOVA http://www.stat.yale.edu/Courses/1997-98/101/anovareg.htm

The Illustrated Machine Learning website. https://illustrated-machine-learning.github.io

An introduction to git and github https://product.hubspot.com/blog/git-and-github-tutorial-for-beginners

A visual introduction to machine learning. http://www.r2d3.us/

Extracting seasonality and trend from time series, https://anomaly.io/seasonal-trend-decomposition-in-r/index.html

Seasonal decomposition of short time series, https://robjhyndman.com/hyndsight/tslm-decomposition/

Wilcoxon–Mann–Whitney test and t-test https://cienciadedatos.net/documentos/17_mann%E2%80%93whitney_u_test

Seeing Theory, a visual introduction to probability and statistics. https://seeing-theory.brown.edu/

Probability distribution applets: https://homepage.divms.uiowa.edu/~mbognar/

Draw function graphs https://rechneronline.de/function-graphs/

Practice datasets by the SuperDataScience Team. https://www.superdatascience.com/pages/training