SUPPLY CHAIN ANALYTICS
The ability to extract information from data has become essential for companies that want to remain competitive - and therefore essential for students aspiring to become successful managers. Provides an understanding of various analytics methodologies and concepts, and the ability to apply these to business problems related to supply chain management and operations management. Key topics include Data Visualization, Time Series Forecasting, Linear Regression, Classification Methods, Association Rules, Cluster Analysis and Text Mining.
Topics
- Data Visualization
- Time Series Forecasting
- Linear Regression
- Classification Methods
- Association Rules
- Cluster Analysis and Text Mining
Topics explicitly listed in official catalogue description
Prerequisites & course information
Requisites: Graduate/professional standing Course Designation: Grad 50% - Counts toward 50% graduate coursework requirement Repeatable for Credit: No Last Taught: Spring 2026 Learning Outcomes: 1. Examine data sets to recognize patterns and relationships in the data that provide insights into business problems Audience: Graduate 2. Apply principles of data visualization and presentation to real business problems Audience: Graduate 3. Develop basic skills using Tableau visualization software Audience: Graduate 4. Apply methods of forecasting product demand and measure the performance of such methods Audience: Graduate 5. Apply a variety of machine learning techniques (such as logistic regression, K nearest neighbors classification, association rules, cluster analysis, text mining) to perform analysis and/or make predictions and help solve business problems Audience: Graduate 6. Develop basic skills using the statistical analysis language R Audience: Graduate Learning Outcomes: 1. Examine data sets to recognize patterns and relationships in the data that provide insights into business problems Audience: Graduate 2. Apply principles of data visualization and presentation to real business problems Audience: Graduate 3. Develop basic skills using Tableau visualization software Audience: Graduate 4. Apply methods of forecasting product demand and measure the performance of such methods Audience: Graduate 5. Apply a variety of machine learning techniques (such as logistic regression, K nearest neighbors classification, association rules, cluster analysis, text mining) to perform analysis and/or make predictions and help solve business problems Audience: Graduate 6. Develop basic skills using the statistical analysis language R Audience: Graduate
Sources
- Course catalogue ↗2026-2027