Data Analysis with R

Introduction to Data Analysis with R introduces learners to the essential concepts and tools for data analysis using R. This module covers data importing and exporting, R packages for data science, and provides a foundation for subsequent modules.

Data Wrangling focuses on pre-processing data in R, including handling missing values, data formatting, normalization, and converting categorical values to numeric variables. This module equips learners with essential skills for preparing data for analysis.

Exploratory Data Analysis delves into descriptive statistics, data grouping, analysis of variance (ANOVA), and correlation statistics. Learners will gain insights into exploring and understanding data relationships, a crucial step in the data analysis process.

Model Development in R covers the development of predictive models using simple linear, multiple linear, and polynomial regression methods. Learners will also explore model assessment and decision-making techniques, essential for effective model development.

Model Evaluation provides learners with the knowledge and skills to evaluate models for overfitting and underfitting conditions. Additionally, this module covers techniques such as regularization and grid search to tune model performance, ensuring reliable data analysis results.

The final module, Project, offers learners the opportunity to apply their acquired skills to a practical project, further reinforcing their understanding of data analysis with R.