Forecasting with R: A Practical Approach

Forecasting with R: A Practical Approach equips participants with essential skills in time series analysis and predictive modeling using R. This hands-on course emphasizes real-world applications, allowing learners to engage in projects that simulate industry challenges. Participants will gain proficiency in utilizing R for data manipulation, visualization, and the development of forecasting models that are applicable in various business contexts.
The curriculum is structured to facilitate interactive learning, where participants will explore diverse forecasting techniques and their implementation through R. By the end of the program, learners will have completed a comprehensive final project, showcasing their ability to apply the concepts learned to a practical forecasting scenario. This project can also be considered for publication in Cademix Magazine, providing an opportunity for participants to share their insights and findings with a broader audience.
Introduction to R for Forecasting
Data Preparation and Cleaning Techniques
Exploratory Data Analysis (EDA) with R
Time Series Decomposition and Trend Analysis
Exponential Smoothing Methods
ARIMA Modeling and Parameter Estimation
Seasonal Adjustment Techniques
Forecast Accuracy Metrics and Evaluation
Advanced Forecasting Techniques (e.g., Prophet, TBATS)
Final Project: Developing a Forecasting Model for a Real-World Dataset

To develop practical forecasting skills using R that can be applied in various professional settings.

Graduates, job seekers, business professionals, researchers, and consultants.

Basic knowledge of R programming and statistics.