Time Series Analysis for Retail Demand equips participants with advanced methodologies to analyze and predict consumer demand patterns using historical data. The course emphasizes practical application through project-based learning, allowing participants to engage with real-world datasets and develop actionable insights that can drive strategic decision-making in retail environments. Participants will explore various forecasting techniques, statistical models, and software tools that are essential for effective demand planning.
The curriculum is designed to foster interactive learning, encouraging collaboration among peers and the sharing of findings through publication in Cademix Magazine. By the end of the program, participants will have a comprehensive understanding of time series data manipulation, model selection, and performance evaluation, culminating in a final project that demonstrates their ability to apply these concepts to real retail scenarios. This hands-on approach not only enhances theoretical knowledge but also builds practical skills that are highly sought after in the job market.
Introduction to Time Series Analysis and its Applications in Retail
Data Collection and Preprocessing Techniques for Time Series
Exploratory Data Analysis (EDA) for Time Series Data
Seasonal Decomposition of Time Series
Autoregressive Integrated Moving Average (ARIMA) Models
Exponential Smoothing Methods
Advanced Forecasting Techniques: Prophet and Machine Learning Approaches
Model Evaluation Metrics and Forecast Accuracy
Implementation of Time Series Models using Python/R
Final Project: Developing a Comprehensive Demand Forecasting Model for a Retail Product