This course provided a comprehensive introduction to data manipulation and analysis using R and the tidyverse package. The focus was on core data science techniques, including importing, tidying, transforming, and visualizing data. Best practices in data cleaning, coding style, and data modeling were emphasized, with a strong foundation in statistical concepts like outliers, variance, standard deviation, and linear regression models.
The most impactful project involved analyzing real-world data sets to uncover meaningful insights. I worked on reshaping data for better usability, detecting and handling anomalies, and creating visualizations that told a compelling data story. The project required calculating summary statistics, modeling relationships through linear regression, and effectively communicating the findings through visualizations.
Throughout the course, I developed essential data analysis skills, such as data transformation, cleaning, and visualization using R and tidyverse. I gained a deep understanding of how to summarize, model, and present data in a way that highlights key insights. These skills enhanced my ability to tell data-driven stories and strengthened my overall coding and analytical capabilities.