R

I’ve applied R for statistical analysis, data visualization, and predictive modeling. These projects underscore my ability to utilize R for analyzing and presenting data effectively. Key examples include:

  • Leveraging R for advanced statistical analysis and data cleaning in professional settings.

  • Creating clear and impactful visualizations to support data-driven decisions.

  • Using R to model and interpret complex datasets for actionable insights.

Stepping Up with Data: A Fitbit-to-Bellabeat Wellness Analysis

This project explores consumer trends using Fitbit data to enhance Bellabeat's marketing strategy. Leveraging R for data cleaning and visualization, alongside Tableau for dashboards, the analysis focused on user activity metrics like steps, calories, and sleep patterns over a 31-day period. Despite dataset limitations, actionable insights were drawn to recommend strategies such as bundled memberships and device pairing to boost customer engagement. This work showcases skills in data analysis, visualization, and strategic thinking to support data-driven decision-making.

R You Seeing This? Decoding Early Covid-19 Trends.

This project analyzed early COVID-19 data during its initial outbreak to uncover patterns and insights into mortality rates, gender disparities, and age-related risks. Using a dataset sourced from Kaggle, the project explored critical questions, such as identifying regions with the highest deaths, comparing gender-based mortality, and evaluating age-specific fatality rates.

Key skills and tools used include: Data Cleaning & Preparation; Statistical Analysis, Visualization, and the use of R for data analysis and Tableau for the visualization.

This project demonstrated proficiency in data wrangling, statistical analysis, and visualization while tackling real-world challenges like incomplete data. It highlighted the importance of demographic factors in understanding the pandemic's impact.