R Training for Data Science

Objectives

• Develop a solid understanding of the R programming language, including its syntax, data structures, and functions.
• Learn how to import, clean, and preprocess data in R using libraries such as dplyr and tidyr, allowing for effective data manipulation and preparation.
• Gain proficiency in data visualization using libraries like ggplot2, enabling you to create visually appealing and informative charts, graphs, and plots.
• Understand basic statistical concepts and techniques in R, including descriptive statistics, hypothesis testing, and regression analysis, to analyze and interpret data accurately.
• Learn various machine learning algorithms and techniques in R, such as decision trees, random forests, logistic regression, and k-means clustering, enabling you to build predictive models.
• Acquire knowledge of advanced data science topics in R, such as text mining, time series analysis, and spatial data analysis, expanding your capabilities to handle diverse datasets.
• Learn how to evaluate and validate machine learning models in R using techniques like cross-validation, performance metrics, and model selection to ensure accurate predictions.
• Understand the basics of data exploration and feature engineering in R, including techniques to uncover patterns, identify outliers, and preprocess variables for machine learning.