R Programming Training Course

R Programming Training

The R Programming Training at Yuva Sakthi Academy empowers participants to become proficient in data manipulation, advanced analytics, data visualization, and more using R. This course provides learners with the skills needed to leverage R Programming effectively in various business environments, enabling data-driven decision-making.

Upon completing the R Programming course, delegates will receive a certificate of completion that is accredited and recognized by reputable companies worldwide. This certification enhances your resume, significantly improving job prospects in the data analytics field. According to Indeed.com, R Programmers earn an average salary ranging from $71,059 to $123,534, depending on experience and the organization.

Our dedicated HR team supports delegates in securing employment by offering guidance in personality development, interview techniques, and presentation skills. We conduct training in aptitude, mock interviews, and HR interviews to prepare participants for successful placements in top multinational corporations (MNCs) and IT firms.

About R Programming Training Course

The R Programming Training Course covers essential topics such as data visualization, descriptive and predictive analytics, data exploration, and more using the R language. Participants will delve into various R packages, data structures, cluster analysis, graphic representation, and statistical methods through hands-on projects and real-world scenarios.

Our curriculum is tailored to reflect the latest industry trends, ensuring learners are equipped with practical knowledge that aligns with market demands. Delegates will gain experience through real-time assignments, helping them develop the competencies needed for a successful career in data science and analytics.

At Yuva Sakthi Academy, we are committed to providing 100% placement assurance, helping delegates connect with leading organizations like Cognizant, Saint-Gobain, TCS, Hexaware, Genpact, Standard Chartered, Cisco, Ericsson, and others. Our comprehensive support structure ensures that you are well-prepared to embark on a rewarding career in R programming and data analytics.

Module 1: Introduction to R and RStudio

Objectives:

• Understand the basics of R and its applications in data analytics.
• Familiarize with RStudio, the integrated development environment for R.

Topics:

• Overview of R: History, Features, and Applications
• Setting up R and RStudio: Installation and Configuration
• R Syntax and Data Types: Vectors, Lists, Matrices, Data Frames, and Factors
• Basic Operations: Arithmetic, Logical Operations, and Comparisons
• Importing and Exporting Data: CSV, Excel, and Other Formats

Module 2: Data Wrangling and Manipulation

Objectives:

• Learn data cleaning techniques and how to prepare data for analysis.

Topics:

• Introduction to Tidyverse: Overview of dplyr, tidyr, and ggplot2
• Data Cleaning: Handling Missing Values, Duplicates, and Outliers
• Data Transformation: Filtering, Selecting, Mutating, and Summarizing Data
• Reshaping Data: Pivoting and Unpivoting Tables
• Merging and Joining Data Sets: inner_join, left_join, right_join, and full_join

Module 3: Exploratory Data Analysis (EDA)

Objectives:

• Conduct exploratory data analysis to summarize the main characteristics of data.

Topics:

• Importance of EDA in Data Science
• Visualizing Data with ggplot2: Bar Charts, Histograms, Boxplots, and Scatter Plots
• Summary Statistics: Mean, Median, Mode, Variance, and Standard Deviation
• Correlation Analysis: Understanding Relationships between Variables
• Outlier Detection and Treatment: Identifying and Handling Outliers

Module 4: Statistical Analysis in R

Objectives:

• Apply statistical methods to analyze data and interpret results.

Topics:

• Descriptive vs. Inferential Statistics: Understanding the Differences
• Hypothesis Testing: t-tests, ANOVA, and Chi-Square Tests
• Regression Analysis: Simple Linear Regression, Multiple Regression, and Logistic Regression
• Time Series Analysis: Components of Time Series Data, ARIMA Modeling
• Non-Parametric Tests: Mann-Whitney, Wilcoxon Signed-Rank Tests

Module 5: Data Visualization

Objectives:

• Create informative and aesthetically pleasing visualizations.

Topics:

• Principles of Data Visualization: Effective Communication of Data
• Advanced ggplot2: Customizing Plots, Themes, and Labels
• Interactive Visualizations with plotly and Shiny
• Visualizing Geographic Data: Using the maps and ggmap packages
• Dashboards in R: Creating Interactive Dashboards with Shiny

Module 6: Introduction to Machine Learning with R

Objectives:

• Understand basic machine learning concepts and implement algorithms in R.

Topics:

• Overview of Machine Learning: Supervised vs. Unsupervised Learning
• Data Preprocessing for Machine Learning: Normalization, Encoding, and Splitting Data
• Implementing Algorithms: Decision Trees, Random Forests, and K-Nearest Neighbors
• Model Evaluation Techniques: Cross-Validation, Confusion Matrix, ROC Curve
• Introduction to Neural Networks: Using the keras package in R

Module 7: Advanced R Programming

Objectives:

• Develop a deeper understanding of R programming and advanced techniques.

Topics:

• Functions in R: Creating and Using Functions, Scope, and Environment
• Object-Oriented Programming in R: S3, S4, and R6 Classes
• Writing Efficient R Code: Vectorization, Apply Functions, and Performance Optimization
• Using APIs and Web Scraping: Accessing Data from Web Services and Websites
• Package Development: Creating and Sharing R Packages

Module 8: Capstone Project

Objectives:

• Apply the knowledge gained throughout the course in a comprehensive project.

Project Guidelines:

• Choose a real-world dataset.
• Perform EDA, data manipulation, statistical analysis, and create visualizations.
• Document the process and present findings through a report or presentation.
• Incorporate machine learning models as appropriate for the project.