• Introduction to Data Frames — Understanding vectors and data frames, the foundation of data manipulation in R.
• Reading and Loading Data — Importing data from CSV, Excel, and other sources using readr and base R.
• Data Querying and Selection — Selecting specific rows, columns, and subsets using conditions and dplyr verbs.
• Data Filtering and Transformation — Grouping, aggregating with summarise(), and creating new variables with mutate().
• Data Visualization — Creating effective visualizations using ggplot2 to explore and communicate insights.
• Statistical Analysis — Applying descriptive statistics, t-tests, ANOVA, chi-square tests, correlation, and regression techniques.
• Exercises — Hands-on practice covering visualization, t-tests, and logistic regression.

By the end of this module, you were able to:

• Load and explore real-world datasets using data frames and dplyr.
• Perform data cleaning, transformation, and aggregation tasks.
• Create insightful visualizations to understand data patterns and trends.
• Conduct statistical analyses to test hypotheses and model relationships.
• Apply regression models for predictive insights.

After mastering these foundational skills, you can continue by:

• Learning Advanced Data Analysis techniques such as multivariate regression, clustering, and time series analysis in R.
• Exploring Machine Learning with R using packages like caret, tidymodels, or xgboost.
• Practicing Data Communication using interactive dashboards with shiny or flexdashboard.