R Programming Corporate Training Course

The module provides an understanding of programming languages for data analysis, visualization, and statistical computing.

In this module, we will focus on techniques for manipulating and cleaning data in R. Team will also learn how to import data from various sources, including CSV files, Excel spreadsheets, databases, and APIs.

The team will learn various techniques for visualizing data in R, including basic plotting, advanced graphics, and interactive visualizations in this module. They will also be able to use data visualization techniques throughout any data analysis projects.

This module will dive into statistical analysis in R, including descriptive statistics, probability distributions, and inferential statistics.

Here we will cover the basics of machine learning in R, including supervised and unsupervised learning, model evaluation, and model selection. We will also learn how to perform classification, logistic regression models, and k-nearest neighbor models.

In this module, we will use the skills that we have learned in the previous modules to complete a real-world project that applies R to a specific problem or dataset

We will cover essential topics for managing and manipulating data in R. Loop functions are a key tool for automating repetitive tasks. This module teaches participants how to use loops in R to process large amounts of data efficiently.

The module discusses functions for simulations in R. It provides tools for generating random numbers, running Monte Carlo simulations, and conducting statistical simulations. It also provides a graphical interface for visualizing the profiler output, making it easier to understand where time is spent in the code.