Basics of Data-Driven Demand Estimation

Basics of Data-Driven Demand Estimation provides participants with essential methodologies and practical applications for accurately forecasting demand in retail, trade, and logistics. The course emphasizes hands-on projects that facilitate the understanding of data analysis techniques, enabling learners to derive actionable insights from various data sources. Participants will engage in interactive sessions that foster collaboration and allow for the practical application of theoretical concepts.
The curriculum is designed to equip learners with the skills necessary to navigate the complexities of demand estimation. By the end of the course, participants will be able to utilize statistical tools and data-driven approaches to enhance their decision-making processes. The final project will challenge learners to apply their knowledge in a real-world scenario, culminating in a presentation of their findings, which may be published in Cademix Magazine.
Introduction to Demand Estimation Concepts
Overview of Data Sources for Demand Forecasting
Quantitative vs. Qualitative Forecasting Techniques
Statistical Methods for Demand Analysis
Time Series Analysis and its Applications
Utilizing Machine Learning for Demand Predictions
Demand Planning and Inventory Management
Case Studies in Retail Demand Forecasting
Tools and Software for Data Analysis
Final Project: Developing a Comprehensive Demand Estimation Model

To develop proficiency in data-driven demand estimation techniques applicable in retail, trade, and logistics sectors.

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

Basic understanding of statistics and familiarity with data analysis tools.