Project Description Guidelines
Current Projects
Our project description repository is available here: projects.the-examples-book.com
We share this link with students to help them select the best suited project for them.
Project Description Template
Fall 2023 - Spring 2024
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Use your company branded slides
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Provide one (1) slide with company background information and up to two (2) slides per project your company is sponsoring
Slide 1
Slide about your company
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Include company name, location, and brief description of company. Pictures are encouraged.
Slides 2 & 3
You may have up to 2 slides per project.
Project #1: [Title]
One sentence high level project overview
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Project description: Please add as many details that you can share publicly. Students will not sign a NDA before seeing these slides
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Keywords: Examples: supply chain analytics; machine learning; social media analysis; optimization; classification; web scraping
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Tools/Skills that will be used/learned: Examples: Python, R, Azure, AWS, PowerBI, SQL, machine learning, natural language processing
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Preferences for student profile: This section is optional and only recommend if there are specific skills or backgrounds absolutely necessary. Most projects will not have this section. Please keep in mind the students are here to learn and develop their skills. They are eager for these opportunities.
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Citizenship status: The following three categories of citizenship status are outlined by Purdue Office of Research export controls guidelines:
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Open to all students: this is the most commonly selected option and preferred.
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US persons preferred/or/required for workforce development: This is an option for partners that can only hire US persons and are seeking a team of US persons for the sole purpose of recruiting students after the project for internships or full time
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US persons required for national security: this option is for partners working with data/projects that require US persons for national security purposes. This should also be noted in the Sponsor Acknowledgment under data classification.
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