Objective: To equip students with the skills to perform more sophisticated statistical tests within their Exploratory Data Analysis (EDA) workflow using the ggstatsplot package, create interactive visualizations, and publish RPubs using the Breast Cancer Wisconsin dataset.

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This exploratory data analysis (EDA) examines the Breast Cancer Wisconsin dataset to identify key cellular features—such as clump thickness, bare nuclei count, cell size, and cell shapes—that distinguish between malignant and benign cases of tumors. Using statistical tests and interactive visualizations, we assess their diagnostic potential and relationships. Findings confirm these features are strong predictors of malignancy.

This Exploratory Data Analysis (EDA) explores patterns in the Breast Cancer Wisconsin dataset. The goal is to understand how certain cellular features differ between benign and malignant tumors. Through visualizations and statistical tests, It highlight which features are most useful in identifying whether a tumor is likely to be benign or malignant.

This HTML case study explores the 2015 World Happiness Report to uncover which economic and social indicators (GDP, life expectancy, social support, corruption, etc.) most influence happiness scores across 150+ countries. Visuals created in R.s

This case study analyzes Amazon’s top 50 bestselling books over 11 years to uncover patterns in genre, pricing, authorship, reviews, and reader preferences. Conducted using R, this report highlights strategic publishing trends.

This HTML case study explores fitness device usage data (Fitbit) to generate marketing strategy for Bellabeat. Visuals created in R.