Data Science Tutorial with R

Data Science is an interdisciplinary field, using various methods, algorithms, and systems to extract knowledge and insights from structured and unstructured data. Data Science combines concepts from statistics, computer science, and domain knowledge to turn data into actionable insights.

R programming is an open-source language, that is popular among data scientists for its rich ecosystem of libraries, ease of use, and strong support for statistical analysis.

In this tutorial, we will explore how the data science process is implemented in an R console or R studio, covering essential concepts, tools, and techniques commonly used in the field.

R Programming Basics for Data Science

R supports following data types:

• Data Types
• Variables
• R Operators
• Vectors
• DataFrames
• Lists
• Matrices

To get a detailed overview of R programming, you can refer to: R Programming Tutorial

Data Preprocessing in R

Data preprocessing involves cleaning, transforming, and preparing data before analysis. In R, there are several functions and packages available to handle common preprocessing tasks.

• What is Data Preprocessing?
• What is Data Cleaning?
• Importing Data in R Script
• Checking Missing Values using is.na()
• Handling Missing Values
• Removing duplicates
• Handling outliers
• Converting data types (as.numeric(), as.factor(), etc.)
• Renaming columns
• Feature scaling
• Encoding categorical variables
• Data Aggregation and Grouping
• Splitting data into training and testing sets
• Reshaping data
• Feature Selection with Caret Package
• Handling Imbalanced Data in R using SMOTE

Data Analysis with R

Data analysis involves examining and interpreting data to extract meaningful insights. In R, several methods and functions are available to perform various types of data analysis.

• What is Data Analysis?
• Data Analysis Process
• Exploratory Data Analysis in R
• Understanding dataset using str()
• Understanding dataset using using summary()
• Identifying correlations between features

Statistical Analysis in R

Statistical analysis is helps in understanding data and making data-driven decisions. R offers a wide range of functions for both descriptive and inferential statistics.

1. Descriptive Statistics

• Mean, Median and Mode
• Skewness
• Kurtosis
• Covariance and Correlation

2. Inferential Statistics

• Hypothesis Testing in R
• T-test in R
• Z-Test in R
• ANOVA (Analysis of Variance) in R
• Mann-Whitney U Test in R
• Kruskal-Wallis test in R Programming
• Wilcoxon Signed-Rank Test in R
• Shapiro-Wilk Test for Normality in R
• Chi-Square Test in R
• Kolmogorov-Smirnov Test in R
• Durbin-Watson Test in R

Multivariate Tests in R

• Multivariate Tests in R
• Principal Component Analysis (PCA)
• Factor Analysis
• Multivariate Analysis of Variance (MANOVA)

Time Series Analysis using R

• Autoregressive Integrated Moving Average (ARIMA)
• Exponential Smoothing
• Seasonal Decomposition of Time Series (STL)

Data Visualization in R

Data visualization helps in understanding and communicating data insights effectively. R provides powerful tools like ggplot2 for creating professional visualizations.

• Bar Plot
• Histogram
• BoxPlot
• Plotting Multiple Plots
• Data Visualization using ggplot2
  • geom_point()
  • geom_bar()
  • geom_line()
  • geom_boxplot()
  • Density Plot in R
  • Violin Plot in R
  • Heatmap in R
• Interactive Visualizations with shiny
• Interactive Visualization with Plotly

Machine Learning

Machine learning allows models to learn from data and make predictions. R provides extensive support for both supervised and unsupervised learning algorithms.

• What is Machine Learning?
• Types of Machine learning
• Supervised Machine Learning
• Unsupervised Machine Learning
• Reinforcement Learning

Machine learning Algorithms Implemented in R

R supports various machine learning algorithms:

• Linear Regression
• Logistic Regression
• Decision Trees
• Random Forest
• Support Vector Machines (SVM)
• K-Nearest Neighbors (KNN)
• Naive Bayes
• K-Means Clustering
• XGBoost

Model Evaluation Techniques

• Confusion Matrix
• Precision, Recall, and F1-Score
• ROC Curve and AUC
• Cross-validation
• Hyperparameter Tuning

Deep Learning

• Artificial Neural Networks (ANN)
• Convolutional Neural Networks (CNN)
• Recurrent Neural Networks (RNN)

Data Science with R is a comprehensive and powerful approach to tackling a wide range of problems. By combining statistical analysis, data visualization, and machine learning, R enables data scientists to gain deep insights from data.