Statistical Inference via Data Science: A ModernDive into R and the Tidyverse

Streamline a data analysis process

My Code Repository for Coursera Data Science Specialization by John Hopkins University

This repository has scripts and other files that are part of the lecture notes and assignments of the course "Advanced Statistical Inference" taught at FME, UPC Barcelonatech.

Targeted maximum likelihood estimation (TMLE) enables the integration of machine learning approaches in comparative effectiveness studies. It is a doubly robust method, making use of both the outcome model and propensity score model to generate an unbiased estimate as long as at least one of the models is correctly specified.

Some collection of codes that are used in data mining and data science related fields, developed by me

I often post solutions to textbook exercises, including: Bayesian Data Analysis (BDA) by Gelman et al; Causal Inference in Statistics Primer (CISP) by Pearl et al; Purely Functional Data Structures (PFDS) by Okasaki.

Maestria-Computo-Estadistico CIMAT 2018-2019

Comprehensive notes and solved problem sets from Casella-Berger: Statistical Inference (Chapters 6-9), covering essential concepts in Statistical Inference. Topics include Sufficiency, Point Estimation, Hypothesis Testing, and Interval Estimation, crucial for Statistical Machine Learning, Computational Statistics, and Probabilistic Robotics.

Identifying which households in Costa Rica have the highest need for social welfare assistance

This is a repository for resources completed as part of the Duke University Statistics with R Specialization.

Attempting to make Statistics for Machine Learning easy to learn and understand

Exercises from the Bayesian Cognitive Modeling book by Lee and Wagenmakers implemented in R and Stan.

2017-02 Middlebury Intro to Statistical & Data Sciences

A manuscripts of mine about mathematics and computer science.

An environmental data science project to assess Wildfire activity on the island of Sardinia.

Complete introductory statistics textbook with an emphasis on the biological & environmental sciences. Includes 26 chapters introducing key concepts for a foundational understanding of statistics and 9 complementary chapters of exercises with jamovi. Supplemental shiny apps illustrate book concepts. All code and datasets are freely available.

🦠🚨 Looking at COVID-19's impact on crime using statistical inference in R.

Material del curso intersemestral "Análisis de Varianza (ANOVA): Introducción a la modelización de datos en R" impartido en el verano de 2017 por Mtro. Said Jiménez, Josué Mendoza, y Diego Ángeles Valdez en la Facultad de Psicología, UNAM.

Impact analysis of the SuperSneakers 101 on marathon runners’ performance