R for Applied Data Science

Applied Data Science at the basic level covers foundational skills for loading, cleaning, transforming, and visualizing structured data. Learners work with tidyverse and Jupyter Notebooks to explore datasets and build simple regression models.

Key Competencies:

• Data Ingestion: Load and explore data using dplyr.

• Data Cleaning: Identify and handle missing or duplicate values in datasets.

• Data Transformation: Convert categorical variables into a numerical format using encoding methods.

• Data Manipulation: Combine datasets using concatenation and merging operations in dplyr/tidyr.

• Data Visualization: Create visual representations such as histograms, bar charts, and line charts to analyze distributions and trends.

• Basic Modeling: Apply simple predictive models such as linear and logistic regression.

• Development Environment: Use Jupyter Notebooks to write and execute code.