The Video Game Data Capstone Project focused on a comprehensive analysis of video game sales data. The goal was to extract meaningful insights into industry trends, regional preferences, and market penetration regarding gaming genres and platforms. Starting with meticulous data cleaning and processing in RStudio, the project concentrated on curating pertinent columns like game names, sales figures across regions, genres, and platforms. The dataset served as the foundation for subsequent analyses. The project's findings highlighted top-selling games, trends across different years, and regional sales patterns across North America, Europe, Japan, and other regions. The analysis underscored genre/platform dominance in various areas, showcasing market penetration levels and varying popularity. These insights provided a strong foundation for developing targeted business strategies in the gaming industry. These strategies encompassed tailored marketing campaigns, regional-specific game development approaches, market penetration planning, partnerships, user engagement strategies, and iterative game development based on feedback loops. Visual representations, including bar charts, pie charts, and line plots, were employed to enhance the presentation of findings. Overall, the project illuminated the nuanced landscape of video game preferences across regions, offering valuable insights for industry stakeholders in shaping targeted strategies and game development efforts.

Today, we conducted an exploratory analysis on a dataset containing information about tornado occurrences. We performed several steps including data loading, exploration, visualization, and analysis. Notably, the dataset covered tornado records spanning from 1794 to 2018, offering insights into over two centuries of tornado occurrences. Our analysis involved: Data Loading and Exploration: We loaded the dataset into RStudio, examining its structure, summary statistics, and unique entries within columns like 'County' and 'Damage Scale.' Visualization: Through visualizations like time series plots and bar charts, we visualized tornado frequencies over the years and examined tornado counts based on damage scales. Seasonal analysis also provided insights into tornado occurrences by month. Statistical Analysis: We conducted a simple linear regression to predict tornado occurrences using month and damage scale as predictors. RMarkdown Setup: We began crafting an RMarkdown report to encapsulate our findings and analysis, using code chunks to generate visualizations and summary statistics. Despite the dataset spanning from 1794 to 2018, we were able to derive valuable insights into tornado occurrences and their characteristics, laying the groundwork for a comprehensive analysis and report.