Harness the power of data analysis and visualization with our comprehensive R Programming course. Designed for both beginners and experienced professionals, this course will help you build a strong foundation in R—one of the leading programming languages for statistical computing and data science.
Who should take this Course?
The R Programming Online Course is ideal for data analysts, statisticians, researchers, and students who want to perform data analysis, statistical modeling, and data visualization using R. It’s also suitable for professionals working in data science, finance, bioinformatics, and academia. No prior experience with R is required, but a basic understanding of statistics and programming concepts is helpful for effective learning.
Course Curriculum
Pre-Model Building Steps
The Course Overview
Performing Univariate Analysis
Bivariate Analysis – Correlation, Chi-Sq Test, and ANOVA
Detecting and Treating Outlier
Treating Missing Values with `mice`
Regression Modelling-In Depth
Section Introduction
Interpreting Regression Results and Interactions Terms
Performing Residual Analysis and Extracting Extreme Observations With Cook’s Distance
Extracting Better Models with Best Subsets, Stepwise Regression, and ANOVA
Validating Model Performance on New Data with k-Fold Cross-Validation
Building Non-Linear Regressors with Splines and GAMs
Classification Models and caret Package-In Depth
Section Introduction
Understanding the Concept and Building Naive Bayes Classifier
Building k-Nearest Neighbors Classifier
Building Tree Based Models Using RPart, cTree, and C5.0
Building Predictive Models with the caret Package
Selecting Important Features with RFE, varImp, and Boruta
Core Machine Learning-In Depth
Section Introduction
Understanding Bagging and Building Random Forest Classifier
Implementing Stochastic Gradient Boosting with GBM
Regularization with Ridge, Lasso, and Elasticnet
Building Classifiers and Regressors with XGBoost
Unsupervised Learning
Section Introduction
Clustering with k-means and Principal Components
Determining Optimum Number of Clusters
Understanding and Implementing Hierarchical Clustering
Clustering with Affinity Propagation
Building Recommendation Engines
Time Series Analysis and Forecasting
Section Introduction
Stationarity, De-Trend, and De-Seasonalize
Understanding the Significance of Lags, ACF, PACF, and CCF
Forecasting with Moving Average and Exponential Smoothing
Forecasting with Double Exponential and Holt Winters
Forecast with ARIMA Modelling
Text Analytics-In Depth
Section Introduction
Corpus, TDM, TF-IDF, and Word Cloud
Cosine Similarity and Latent Semantic Analysis
Extracting topics with Latent Dirichlet Allocation
Sentiment Scoring with tidytext and Syuzhet
Classifying Texts with RTextTools
ggplot2
Section Introduction
Manipulating Legend, AddingText, and Annotation
Drawing Multiple Plots with Faceting and Changing Layouts
Creating Bar Charts, Boxplots, Time Series, and Ribbon Plots
ggplot2 Extensions and ggplotly
Speeding Up R Code
Section Introduction
Implement Parallel Computing with doParallel and foreach
Write Readable and Fast R Code with Pipes and DPlyR
Write Super Fast R Code with Minimal Keystrokes Using Data.Table
Interface C++ in R with RCpp
Build Packages and Submit to CRAN
Section Introduction
Build, Document, and Host an R Package on GitHub
Performing Important Checks before Submitting to CRAN