Data Science with R Programming

Overview of Data Science with R Programming Data Science is a rapidly growing field that involves analyzing and interpreting complex data sets. R programming is a powerful tool for data analysis,…

Created by

Stalwart Learning


Course Description

Overview of Data Science with R Programming

Data Science is a rapidly growing field that involves analyzing and interpreting complex data sets. R programming is a powerful tool for data analysis, visualization, and statistical computing. This training program is designed to provide a comprehensive understanding of data science concepts, techniques, and tools, with a focus on R programming. The program covers all aspects of the data science process, from data cleaning and manipulation to data visualization, statistical modeling, and machine learning.

This training program is ideal for professionals who want to enhance their skills in data science and R programming. It is also suitable for students who want to pursue a career in data science. The program is structured to provide a hands-on learning experience, with practical examples and case studies that help participants apply their learning to real-world scenarios.

Stalwart Learning is committed to providing high-quality training programs that meet the evolving needs of the industry. Our experienced trainers have a deep understanding of data science and R programming, and they use a practical and interactive approach to training.


40 Hours

Module 1: Introduction to Data Science
  • Understanding the role of data science in business and industries
  • Introduction to key concepts and terminology in data science
  • Overview of the data science workflow and process
  • Installing R and RStudio for data science
Module 2: R Programming Basics
  • Introduction to R programming language
  • Understanding data types, variables, and operators in R
  • Working with vectors, matrices, and arrays in R
  • R functions and control structures
Module 3: Data Manipulation with R
  • Importing and exporting data in R
  • Working with data frames and tibbles
  • Data cleaning and preprocessing techniques in R
  • Handling missing values and outliers in data
Module 4: Exploratory Data Analysis
  • Data visualization with ggplot2 in R
  • Descriptive statistics and summary measures in R
  • Exploratory data analysis techniques
  • Data exploration and visualization with R
Module 5: Statistical Modeling with R
  • Introduction to statistical modeling in R
  • Hypothesis testing and p-values
  • Regression analysis with R
  • Model evaluation and interpretation
Module 6: Machine Learning with R
  • Introduction to machine learning algorithms
  • Supervised learning algorithms: classification and regression
  • Unsupervised learning algorithms: clustering and dimensionality reduction
  • Evaluating and optimizing machine learning models in R
Module 7: Text Mining and Natural Language Processing
  • Introduction to text mining and NLP
  • Text data preprocessing in R
  • Text classification and sentiment analysis with R
  • Topic modeling and text summarization in R
Module 8: Time Series Analysis
  • Introduction to time series data
  • Time series data preprocessing and visualization in R
  • Time series forecasting techniques in R
  • Evaluating and validating time series models
Module 9: Data Science Projects and Case Studies
  • Undertaking end-to-end data science projects
  • Applying data science techniques to real-world datasets
  • Presenting and communicating data science findings
  • Best practices for data science projects in R
Module 10: Advanced Topics in Data Science with R
  • Advanced data visualization with R
  • Big data analytics with R and distributed computing frameworks
  • Deploying R models in production
  • Emerging trends and developments in data science with R