R Programming Online Course

R Programming Online Course

Unlock the power of data analysis and visualization with our R Programming online course. This course is tailored for beginners and professionals looking to enhance their expertise in R, one of the most popular programming languages for statistical computing and data science.

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
• Submitting an R Package to CRAN