Courses / Data Science / Analytics / R Programming
Data Science / Analytics

R Programming

Salim Rana
Couse Completed

105

Category

Data Science

Upcomming Batch

15 Sept, 2024

Review

About Course

The R Programming Training at Brain dot provides you with a thorough understanding of the fundamentals of the R Programming Language and R Studio under the supervision of an expert. By the end of the R Programming Training Course, you will be familiar with key concepts such as Data Analysis Techniques, Data Mining, Data Manipulation, Array Calculations, Storage Facility, Data Handlings, Loops, Data Visualization Using Graphical Representation, and Real-Time Statistical Analysis with R.

Show more

Course Objectives

  • Understand the fundamentals of R programming, including syntax and data structures.
  • Perform data manipulation and cleaning using dplyr and tidyr.
  • Create insightful visualizations with ggplot2 and other R visualization packages.
  • Conduct statistical analysis, hypothesis testing, and data modeling in R.
  • Work with different data formats, including CSV, Excel, and databases.
  • Develop reproducible research reports with R Markdown and automate workflows.
  • Build interactive data applications using Shiny for dynamic reporting and visualization.

Course Curriculum

Overview of R and its Ecosystem
Setting Up R and RStudio
Basic R Syntax and Data Types
Variables, Operators, and Expressions
Writing and Running Scripts in R
Basic Functions and Control Structures

Working with Vectors, Matrices, and Lists
Data Frames: Creation, Manipulation, and Indexing
Data Import and Export: CSV, Excel, and Databases
Data Cleaning with dplyr and tidyr
Handling Missing Data and Data Transformation

Introduction to ggplot2 for Data Visualization
Creating Basic and Advanced Plots (Bar, Line, Scatter)
Customizing Plots: Themes, Colors, Labels
Faceting and Plotting Multiple Variables
Interactive Visualizations with plotly and shiny

Descriptive Statistics and Summary Functions
Hypothesis Testing (t-tests, Chi-square tests)
Correlation and Regression Analysis
Analysis of Variance (ANOVA)
Introduction to Statistical Modeling in R

Working with Dates and Times in R
String Manipulation with stringr
Functional Programming Concepts in R
Data Manipulation with data.table
Creating Reproducible Reports with R Markdown

Introduction to Machine Learning with R
Supervised Learning: Linear and Logistic Regression
Unsupervised Learning: Clustering Techniques
Model Evaluation and Validation Techniques
Case Studies and Real-World Applications

End-to-End Data Analysis Project
Building an Interactive Dashboard with Shiny
Case Study on Data Science with R
Best Practices for R Programming

Ratings & Reviews

4.5

Rated 4 out of 1 Rating

5 star
82%
4 star
30%
3 star
15%
2 star
6%
1 star
10%

Featured review

Devi

2 weeks ago

This course gave me a strong foundation in R, especially for data analysis. The focus on practical applications like data cleaning, visualization, and statistical analysis was exactly what I needed

Helpful?

Nithish

2 weeks ago

The course covered a wide range of topics, from basic R syntax to advanced techniques like data manipulation with dplyr and visualization with ggplot2. The interactive exercises kept me engaged throughout.

Helpful?
This course includes:
Duration 40 hrs
Skill Level Beginner
Language Tamil / English
Certificate Yes