Overview of R programming language and its applications in data analytics, including data manipulation, visualization, and statistical analysis.

Evaluating model performance, selecting appropriate evaluation metrics, and validating models using cross-validation techniques.

Installation and configuration of R and RStudio, setting up working directories, and navigating the RStudio interface.

Analyzing time-series data, forecasting future trends, and detecting seasonality and trends using the tsibble package.

Techniques for importing data from various sources such as CSV files, Excel spreadsheets, databases, and web APIs into R, and exporting results to different file formats.

Analyzing unstructured text data, performing sentiment analysis, and extracting insights from text using the tm package.

Using the dplyr package for data manipulation tasks such as filtering, sorting, summarizing, and joining datasets.

Introduction to big data analytics using the sparklyr package, connecting to Apache Spark clusters, and processing large-scale datasets.

Creating interactive and publication-quality visualizations such as scatter plots, histograms, box plots, and heatmaps using the ggplot2 package.

Building interactive web applications for data visualization and analysis using the Shiny framework.

Calculating and interpreting descriptive statistics such as mean, median, mode, variance, and standard deviation using R functions.

Applying R programming skills to real-world data analytics projects and case studies to demonstrate practical applications.

Understanding probability distributions, hypothesis testing, confidence intervals, and regression analysis for making inferences about data.

Learning best practices for coding in R, optimizing performance, debugging code, and collaborating with others using version control systems like Git.

Building predictive models using machine learning algorithms such as linear regression, decision trees, random forests, and support vector machines with the caret package.