What your industrial data analytics courses need

According to the panel discussion, the following topics are considered essential when designing the industrial data analytics course: * Review of basic data-driven modeling and engineering statistics knowledge (one week) * Data transformation and processing (two weeks) * Regression (two weeks) * Cla...

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Bibliographic Details
Published inIndustrial Engineer Vol. 48; no. 4; p. 43
Main Author Liu, Kaibo
Format Trade Publication Article
LanguageEnglish
Published Norcross Institute of Industrial and Systems Engineers (IISE) 01.04.2016
Subjects
Classrooms
Computers
Customer information files
Data analysis
Datasets
Design
Engineering
Industrial engineering
Interdisciplinary subjects
Labor market
Learning
Maintenance management
Personal computers
Point of sale systems
Problem solving
Servers
Skills
Software
Students
Systems engineering
Textbooks
Visualization
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Summary:According to the panel discussion, the following topics are considered essential when designing the industrial data analytics course: * Review of basic data-driven modeling and engineering statistics knowledge (one week) * Data transformation and processing (two weeks) * Regression (two weeks) * Classifcation (two weeks) * Clustering (two weeks) * Performance evaluation and visualization (two weeks) * Project presentations and peer-review-based learning (two weeks) Depending on the background of the enrolled students, the instructor also might consider introducing more or less advanced techniques or spend more or less time on certain topics. [...]it is often time-consuming for a single instructor to prepare and collect appropriate examples and effective course materials. [...]it would be important and benefcial for the community to establish some teaching repository for collaborating and sharing the course materials and datasets regarding industrial data analytics.
ISSN:2471-9579