Supply Chain Operations Analytics

A free 27-lesson course. Every lesson has a video walkthrough and a Jupyter notebook you can download or open in Google Colab; 3 lessons include real datasets. Tools covered: pandas, NumPy, Matplotlib, statsmodels, seaborn, SQLite.

Supply Chain Operations Analytics

Python Basics for Supply Chain Analytics

Numbers, Dates, and Quantities in Operations

Control Flow for Operational Decision Logic

Functions for Supply Chain Calculations

Loading and Exploring Supply Chain Datasets

Order-Level and Demand Analysis in Supply Chain Operations Analytics | Full Training

Daily and Monthly Demand Aggregations in Supply Chain Analytics

Inventory Level Tracking with Pandas

Handling Missing and Dirty Operations Data

Introduction to SQL for Supply Chain Analysts

Joining Orders, Inventory, and Supplier Tables in Supply Chain Analytics

SQL vs Pandas for Operational Reporting: Key Differences & Best Practices

Mastering Core Supply Chain Views for Enhanced Operations Analytics

Introduction to Demand Forecasting Data

Time Series Decomposition Techniques for Accurate Demand Forecasting in Supply Chain

Moving Average and Exponential Smoothing Models in Supply Chain Analytics

ARIMA and SARIMA for Demand Forecasting in Supply Chains

Forecast Accuracy Evaluation and Monitoring in Supply Chain Operations

Understanding Inventory Dynamics in Supply Chain Analytics

Safety Stock and Reorder Point Modeling

ABC Inventory Classification in Supply Chain Analytics

Supplier Performance Metrics in Supply Chain Analytics

Lead Time Variability Analysis in Supply Chains

On-Time Delivery and Fulfillment Rates in Supply Chain Analytics

Building Supply Chain Dashboards with Streamlit

Transforming Operations Notebooks into Scalable Production Pipelines in Supply Chain Analytics

End-to-End Supply Chain Analytics Project