Master Advanced R for Data Science & Predictive Analytics

Course Description

This intensive 5-day course equips data scientists and analysts with advanced R programming skills for predictive analytics and machine learning. Participants will learn cutting-edge techniques in data manipulation, visualization, and modeling using R's powerful ecosystem. Through hands-on projects and real-world datasets, attendees will gain practical experience in developing sophisticated predictive strategies.

Learning Objectives

• Master advanced R programming techniques for efficient data analysis
• Develop and implement complex predictive models using machine learning algorithms
• Create compelling data visualizations and interactive dashboards with R
• Apply statistical methods and time series analysis for accurate forecasting
• Optimize R code for improved performance and scalability

Course Modules

Day 1: Advanced R Programming and Data Manipulation

• Functional programming in R
• Advanced data manipulation with dplyr and data.table
• Efficient memory management and performance optimization
• Working with large datasets and databases in R

Day 2: Advanced Data Visualization and Reporting

• Creating complex ggplot2 visualizations
• Interactive visualizations with plotly and shiny
• Automated reporting with R Markdown and flexdashboard
• Geospatial visualization techniques

Day 3: Machine Learning and Predictive Modeling

• Supervised learning algorithms (Random Forests, SVM, XGBoost)
• Unsupervised learning techniques (clustering, dimensionality reduction)
• Model selection, validation, and hyperparameter tuning
• Ensemble methods and stacking

Day 4: Time Series Analysis and Forecasting

• Advanced time series modeling with ARIMA and Prophet
• Multivariate time series analysis
• Anomaly detection in time series data
• Long short-term memory (LSTM) networks for sequence prediction

Day 5: Advanced Topics and Real-world Applications

• Natural Language Processing with R
• Network analysis and graph analytics
• Bayesian modeling and inference
• Deploying R models in production environments

Practical Wins for Participants

• Develop a portfolio-ready predictive modeling project
• Create an interactive dashboard for data exploration and visualization
• Implement a scalable machine learning pipeline for big data
• Design and deploy a real-time anomaly detection system