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
Credits: 5 credit per day
Course Mode: full-time
Provider: Blackbird Training Centre
