Logistics Data Analyst Course

Lesson 1 • Core Logistics Metrics and KPIs

Defines the standard metrics used to measure logistics performance. Connects each KPI to a business decision it supports.

Lesson 2 • Data Sources in Logistics Operations

Surveys the systems that capture logistics data, including TMS, WMS, and ERP platforms. Students learn where to find data before they can analyze it.

Lesson 3 • Logistics Systems and Data Overview

Maps the major components of logistics networks and the data each produces. Establishes the vocabulary and mental model needed for all subsequent analysis work.

Lesson 1 • Transforming and Reshaping Data

Explains pivoting, unpivoting, merging, and aggregating logistics tables. These transformations convert raw records into the structures analysis requires.

Lesson 2 • Building Repeatable Data Pipelines

Introduces the concept of automating data preparation steps for recurring reports. Students create documented, reusable workflows that reduce manual effort.

Lesson 3 • Cleaning and Standardizing Records

Teaches techniques for fixing inconsistent formats, correcting errors, and standardizing fields. Clean data is the prerequisite for every analysis in later chapters.

Lesson 1 • Essential Formulas for Logistics Work

Covers lookup, conditional, and aggregation formulas applied to logistics scenarios. These formulas underpin every spreadsheet model built in this chapter.

Lesson 2 • PivotTables for Operational Reporting

Teaches building and customizing PivotTables to summarize large logistics datasets quickly. Students produce standard operational reports using only PivotTable features.

Lesson 3 • Spreadsheet Dashboard Design

Guides students through building a single-page logistics dashboard in a spreadsheet. Covers layout, chart selection, and dynamic linking of data to visuals.

Lesson 1 • Joining Multiple Logistics Tables

Explains INNER, LEFT, and multi-table JOINs using order, shipment, and carrier tables. Joining tables is essential for combining data from separate logistics systems.

Lesson 2 • Relational Database Concepts

Explains tables, keys, and relationships as they appear in logistics database schemas. Understanding schema structure is required before writing any queries.

Lesson 3 • Advanced SQL for Logistics Insights

Introduces window functions, CTEs, and conditional expressions for complex logistics queries. Students write production-quality SQL used in automated reporting pipelines.

Lesson 4 • Aggregation and Grouping in SQL

Teaches GROUP BY, HAVING, and aggregate functions for summarizing logistics data. These skills produce the summary tables used in reporting and dashboards.

Lesson 1 • Inventory and Warehouse Visualizations

Covers charts specific to inventory levels, stock aging, and warehouse utilization. These visuals support operations teams in daily decision-making.

Lesson 2 • Visualizing Shipment and Delivery Data

Applies bar, line, and scatter charts to shipment volume, transit time, and delivery performance data. Students match each logistics metric to its optimal visual form.

Lesson 3 • Building Interactive BI Dashboards

Guides students through creating interactive dashboards in a BI tool using logistics data. Interactivity allows end users to explore data without analyst involvement.

Lesson 1 • Descriptive Statistics for Logistics Data

Covers mean, median, standard deviation, and percentiles applied to transit times and costs. These measures form the baseline for all comparative and predictive analysis.

Lesson 2 • Correlation and Root Cause Analysis

Applies correlation analysis and fishbone diagrams to logistics performance problems. Students distinguish correlation from causation and structure root cause investigations.

Lesson 3 • Identifying Patterns and Seasonality

Teaches decomposition of logistics time series into trend, seasonal, and irregular components. Recognizing patterns enables more accurate planning and resource allocation.

Lesson 1 • Regression for Logistics Prediction

Applies linear and multiple regression to predict freight costs and lead times from operational variables. Regression links business drivers to measurable logistics outcomes.

Lesson 2 • Time Series Forecasting Methods

Covers exponential smoothing and ARIMA models applied to shipment volume and demand data. Students select and tune models based on data characteristics.

Lesson 3 • Forecasting Fundamentals in Logistics

Explains forecast types, error metrics, and the business value of accurate logistics forecasts. Sets the evaluation framework used to compare all models in this chapter.

Lesson 4 • Evaluating and Deploying Forecast Models

Teaches cross-validation, forecast monitoring, and model refresh cycles for production use. Students learn to maintain model accuracy as logistics conditions change.

Lesson 1 • Building Executive-Level Analytics Reports

Teaches structuring data-driven reports and presentations for senior leadership audiences. Students translate complex analysis into clear recommendations with supporting evidence.

Lesson 2 • Risk Analytics in Supply Chains

Applies probability and impact scoring to identify and quantify supply chain disruption risks. Students build risk registers and prioritize mitigation actions using data.

Lesson 3 • Supplier and Carrier Performance Analytics

Builds multi-dimensional scorecards for evaluating suppliers and carriers using historical data. Objective scoring replaces subjective assessments in vendor management decisions.