What will you learn in Advanced Predictive Modelling in R Certification Training Course
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Master advanced regression techniques, including regularization (Lasso, Ridge) and generalized linear models.
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Implement classification algorithms such as logistic regression, decision trees, and support vector machines.
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Apply ensemble methods: random forests, gradient boosting, and stacking models for improved accuracy.
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Perform time series forecasting using ARIMA, exponential smoothing, and state-space models.
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Explore unsupervised learning: k-means clustering, hierarchical clustering, and principal component analysis.
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Validate and tune models with cross-validation, ROC/AUC analysis, and hyperparameter optimization.
Program Overview
Module 1: Course Introduction & R Setup
2 hours
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Topics: Course objectives, R environment setup, package installation (caret, forecast, randomForest).
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Hands-on: Configure RStudio, install libraries, and run sample scripts.
Module 2: Advanced Regression Techniques
3 hours
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Topics: Regularization methods (Lasso, Ridge), GLMs, diagnostics.
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Hands-on: Build and compare penalized regression models on real datasets.
Module 3: Classification Algorithms
3 hours
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Topics: Logistic regression, decision trees, support vector machines, model performance metrics.
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Hands-on: Train classifiers, evaluate with confusion matrices, and tune parameters.
Module 4: Ensemble Methods
3.5 hours
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Topics: Bagging, random forests, gradient boosting machines (GBM), stacking ensembles.
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Hands-on: Implement and ensemble models using caret and mlr frameworks.
Module 5: Time Series Forecasting
2.5 hours
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Topics: ARIMA modeling, exponential smoothing, seasonal decomposition, forecast accuracy.
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Hands-on: Forecast sales data and evaluate model assumptions.
Module 6: Unsupervised Learning
2.5 hours
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Topics: k-means clustering, hierarchical clustering, PCA for dimensionality reduction.
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Hands-on: Segment customers and visualize clusters using ggplot2.
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Job Outlook
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Predictive modeling experts are in demand in finance, healthcare, marketing, and tech, with salaries ranging $85K–$130K.
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Skills in R and advanced analytics open roles as Data Scientist, Quantitative Analyst, and Analytics Engineer.
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Proficiency in model deployment enhances opportunities in production analytics and MLOps.
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Expertise in time series and ensemble methods is particularly valued for forecasting and risk modeling.
Explore More Learning Paths
Elevate your predictive analytics and R programming skills with this carefully selected course designed to help you model complex datasets, forecast trends, and make data-driven decisions.
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