Data Science combines statistics, programming and domain knowledge to extract insights from data. R is a widely used language for statistical computing, data analysis and visualization. It provides rich libraries that simplify data manipulation, modeling and reporting.
- Use popular R libraries like dplyr, ggplot2 and tidyr for data cleaning and visualization.
- Perform statistical analysis and build models using packages such as caret and randomForest.
- Work with real datasets in RStudio to analyze, visualize and interpret results efficiently.
Installation of R
This section explains how to install R and RStudio and understand the basic R environment.
Foundations of R
This section covers the fundamental concepts of R programming, including syntax, variables, data types, operators and data structures that form the base for data analysis
Data Preprocessing in R
In this section, we will explore how to preprocess data in R by handling missing values, converting data types and preparing datasets for analysis.
- Tidyverse Packages
- Data Preprocessing
- Data Cleaning
- Handling Missing Values
- Removing duplicates
- Handling outliers
- Converting data types
- Renaming columns
Data Visualization in R
This section explains how to create meaningful visualizations to explore and present data. It covers ggplot2 for static plots and Shiny and Plotly for interactive visualizations.
- Data Visualization
- Data Visualization using ggplot2
- Boxplot
- Density Plot
- Violin Plot
- Heatmap
- Interactive Visualizations with shiny
- Interactive Visualization with Plotly
Data Analysis with R
This section covers techniques for exploring datasets and extracting insights. It includes exploratory data analysis, data aggregation, feature scaling and encoding categorical variables.
- Data Analysis
- Exploratory Data Analysis
- Data Aggregation and Grouping
- Feature scaling
- Encoding categorical variables
Statistical Analysis in R
This section covers statistical methods for analyzing and interpreting data. It includes descriptive statistics, inferential tests, probability distributions, correlation and multivariate analysis.
1. Descriptive Statistics
2. Inferential Statistics
- Probability Distributions
- Confidence Intervals
- Parametric Tests
- Non-Parametric Tests
- Hypothesis Testing
- ANOVA (Analysis of Variance)
- Covariance and Correlation
3. Multivariate Tests in R
- Factor Analysis
- Multivariate Tests
- Principal Component Analysis (PCA)
- Multivariate Analysis of Variance (MANOVA)
Machine Learning in R
This section introduces machine learning concepts and their implementation in R. It covers regression, classification, clustering, cross-validation, evaluation metrics and time series analysis.
- Introduction
- Setting up Environment for Machine Learning
- Supervised and Unsupervised Learning
- Regression Techniques
- Classification Techniques
- Cross-Validation
- Evaluation Metrics
- Clustering
- Linear Discriminant Analysis
- Time Series Analysis
- Exponential Smoothing
Deep Learning in R
This section explains neural network models and their implementation in R using packages like keras and tensorflow. It includes different neural network architectures and optimization techniques.
- Types of Neural Networks
- Architecture of Neural Networks
- Recurrent Neural Networks
- Gated Recurrent Units (GRUs)
- Long Short-Term Memory (LSTM)
- Convolutional Neural Networks (CNNs)
- Generative Adversarial Networks (GANs)
- Gradient Descent Algorithm
- Stochastic Gradient Descent
- Hyperparameter Tuning
- Boosting
Projects
This section includes practical R projects to apply the concepts covered in this tutorial. It focuses on real-world data analysis, visualization, statistical modeling and machine learning using R.