Certificate in R Programming

TABLE OF CONTENTS

1. Pre-Model Building Steps

• Performing Univariate Analysis
• Bivariate Analysis – Correlation, Chi-Sq Test, and ANOVA
• Detecting and Treating Outlier
• Treating Missing Values with `mice`

2. Regression Modelling-In Depth

• 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

3. Classification Models and caret Package-In Depth

• 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

4. Core Machine Learning-In Depth

• 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

5. Unsupervised Learning

• Clustering with k-means and Principal Components
• Determining Optimum Number of Clusters
• Understanding and Implementing Hierarchical Clustering
• Clustering with Affinity Propagation
• Building Recommendation Engines

6. Time Series Analysis and Forecasting

• 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

7. Text Analytics-In Depth

• 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

8. ggplot2

• 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

9. Speeding Up R Code

• 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

10. Build Packages and Submit to CRAN

• Build, Document, and Host an R Package on GitHub
• Performing Important Checks before Submitting to CRAN
• Submitting an R Package to CRAN

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