Demand Planning with Python

Demand Planning with Python provides a comprehensive exploration of advanced methodologies in demand forecasting, leveraging Python's powerful data analysis capabilities. Participants will engage in hands-on projects that simulate real-world scenarios, enabling them to apply theoretical concepts to practical applications. The course emphasizes the importance of data-driven decision-making in retail, trade, and logistics, equipping learners with the tools necessary to optimize inventory management and enhance supply chain efficiency.
Throughout the program, participants will delve into various forecasting techniques, including time series analysis, regression models, and machine learning algorithms tailored for demand planning. By working on interactive projects, learners will gain experience in data manipulation, visualization, and model evaluation. The course culminates in a final project where participants will develop a comprehensive demand forecasting model using Python, which they can publish in Cademix Magazine, showcasing their expertise to potential employers and peers.
Introduction to Demand Planning Concepts
Overview of Python for Data Analysis
Data Collection and Preprocessing Techniques
Time Series Analysis for Demand Forecasting
Regression Analysis and Its Applications
Introduction to Machine Learning for Forecasting
Evaluating Forecast Accuracy and Performance Metrics
Case Studies in Demand Planning
Interactive Project: Building a Demand Forecasting Model
Final Project Presentation and Publication Opportunity

Equip participants with the skills to develop and implement effective demand forecasting models using Python.

Graduates, job seekers, business professionals, and optionally researchers or consultants interested in demand forecasting.

Basic knowledge of Python programming and familiarity with data analysis concepts.