Reproducible Research Course Project - 1 from Johns Hopkins University by Coursera

📈 📌 This course presents the fundamentals of inference in a practical approach for getting things done. After taking this course, students will understand the broad directions of statistical inference and use this information for making informed choices in analyzing data.

Análisis y visualización de datos de actividad sísmica en México con R. Cómo analizar y visualizar la historia sísmica de México con datos del SSN (Servicio Sismológico Nacional)

Using R tools like ggplot2 and dplyr, analyzed small business data to understand uptake patterns for federal aid. Employed logistic regression and decision trees to pinpoint key predictors. Explored strategies like mailer campaigns to boost application rates and conducted power analysis for potential randomized trials.

A case study that will analyze publicly available data sets, provided by the course, for a bike share program based in Chicago.

A repository having all data science concepts programmed in R.

A data visualization journey using the R programming language.

Coursework for COMP3354 Statistical Learning

Gauging how Support Vector Machine Algorithm behaves with Hyperparameter Tuning

Canonical Analysis using R Notebook

Functions for a better EDA in R

Data Analytics to find optimal locations for ambulance

Comparison of performance of Tree-based methods for credit card fraud detection

Ecologia interativa app

R-implementation of clustering using mixed-type data (continous and discrete)

This repository contains files related to a longitudinal study of global democratic backsliding conducted by Lia Smith and Kayley Watson. The study utilizes R programming language for data analysis and visualization.

🌼 Collection of materials to accompany the R25 funded "Data Science for Environmental Health" short course